The police here have booked a courier company for allegedly trying to send liquor in a parcel to Bihar and booked the company, officials said on Monday.

Courier company tries to send liquor parcel from Gurugram to Bihar, booked

Big4 company Deloitte has been mandated by the investors of Groyyo — Tiger Global and Alpha Wave Global — to ‘work with the startup and take a closer look at its trading business. This and more in todays ETtech Morning Dispatch.

Groyyo investors tap Deloitte; new-age firms hired more in FY23

While Paytm saw its employee headcount expand by a whopping 3.5 times during the fiscal, PB Fintech, which runs the Policybazaar platform, saw a 32% year-on-year increase in employees. Mumbai-based FSN Ecommerce,

Listed internet companies buck the trend to expand payrolls in FY23

Adani Green, DLF, TVS Motor, Marico, Tube Investments, Proctor and Gamble, Supreme Industries, APL Apollo, UPL, GMR Airports, Petronet, KPIT Technology, Nippon Life, Blue Star, DCM Shriram among others will announce their Q2 results.

Q2 results this week: Airtel, Adani Enterprises, Zomato, Tata Motors, L&T, Titan, IndiGo and others

Tirupur: Farmers and weavers are severely affected because of the flawed governance of the DMK-led government in Tamil Nadu, AIADMK general secretary .

EPS: Farmers, weavers hit by DMK policies

PORTLAND, PORTLAND, OR, UNITED STATE, October 27, 2023 /EINPresswire / -- Allied Market Research published a new report, titled, " The USD 6.5 Billion Multichannel Order Management Market Reach by 2031 | Top Players such as - Delhivery, Brightpearl & Zoho."

USD 6.5 Billion Multichannel Order Management Market Reach By 2031 | Top Players Such As - Delhivery, Brightpearl & Zoho

A recent incident involving a customer buying a TV from Flipkart highlights the issue of unexpected surprises in online shopping. The customer purchased a Sony TV but found a Thomson TV inside the box,

Man orders Sony TV from Flipkart, gets Thomson TV inside box

Delhivery Ltd, India's largest fully integrated logistics services provider, along with SellerApp, both partners on ONDC, are enabling skilled local w.

ONDC partners SellerApp and Delhivery boost sales for local Kanjeevaram weavers

Garre revealed that Paytm, Delhivery, and Zomato are notable holdings in their India portfolio, with a focus on shorter-term evaluations of these stocks. He said, “We have stocks like Paytm and Delhivery; Paytm is recently added to India portfolio and ...

Bernstein sees short-term correction but will buy on dips; adds Zomato to portfolio

Jyoti's small business was able to access a larger market through ONDC, facilitated by SellerApp and Delhivery. This enabled her to get 7-15 orders daily from 200+ cities, with orders ranging from Rs₹8,

ONDC partners SellerApp and Delhivery drive up sales for Kanjeevaram weavers

Delhivery

delhivery

As the leading fulfilment platform for digital commerce in India, Delhivery fulfils a million packages a day, 365 days a year. Its 24 automated sort centres, 101 hubs, 3,100+ direct delivery centres, 1000+ partner centres, 11,000+ fleet, and 60,000+ team members run smoothly thanks to a vast network of IoT devices. Thousands of data events and messages are coming in and going out of our pipelines each second. This amounts to a massive daily data volume in terabytes, which makes operational visibility crucial for us and our stakeholders. Recognising the requirements, we decided to build data marts—centralised, eventually consistent databases that offer users quick access to pre-aggregated business data. This allows our stakeholders to quickly access business insights without searching through an entire data warehouse. However, with this daunting scale, one of the major challenges was to maintain data integrity and low latency while providing the capacity for analytical workloads. In this blog, I am going to unpack all of my leanings while migrating our data marts from Amazon Aurora to TiDB, a Hybrid Transactional/Analytical Processing (HTAP), distributed SQL database. Hopefully, this post can provide insights to data engineering leaders, database administrators, or data architects who are considering a similar migration to TiDB or any other HTAP database. OLTP, OLAP, and HTAP To better understand the real-time data marts case at Delhivery, let’s first get familiar with three concepts that are at the core of our use case: OLTP, OLAP & HTAP: Online Transaction Processing (OLTP) systems are designed for transaction-oriented applications, ensuring data integrity through ACID (Atomicity, Consistency, Isolation, Durability) properties. OLTP: Online Analytical Processing (OLAP) systems enable high-speed, multidimensional analysis of large data volumes, aiding in data-driven decision-making. OLAP: Hybrid Transaction/Analytical Processing (HTAP) combines OLTP and OLAP functionalities, permitting real-time analytics on transactional data. HTAP: Real-Time Data Marts Use Case at Delhivery Real-time data marts differ from traditional data marts in that they ingest data in real time, not at specific intervals. These data marts are critical for ground operational decision-making at Delhivery because we can't afford any delay in synchronising these events. Our real-time data mart journey began in 2020 when we identified a need for centralised dashboards—specifically the EYE dashboard. This dashboard's purpose was to provide real-time operational visibility to ground operations, enabling decision-making based on up-to-the-minute data. Examples of usages include: Real-time monitoring of incoming and outgoing connection schedules for Delhivery hubs. Vehicle planning & visibility: Continuous performance tracking of Delhivery's facilities. Performance tracking: Providing the central team with precise information on ground blockers to take appropriate actions. These could be due to various factors such as a dip in center performance, shipment aging, or congestion in incoming and outgoing connections. Centralised control visibility: Tracking of put and pick compliance metrics Compliances: Initial implementation and the challenges We thought of solving our use cases using data warehouse tools like Redshift, and Snowflake but none of these solutions worked for us considering the design pattern and requirement for real-time data ingestion along with merge. Thus, we initially chose Aurora (PostgreSQL) to serve our data mart use case. The data ingestion process around Aurora We architected our real-time data marts using Spark Streaming and Aurora. Our steaming pipeline was very simple--reading data from Kafka, processing data in Spark micro batches, and performing upsert operations in Aurora. Our database was modelled using a multi-layered architecture, which consists of a raw layer, a partitioned layer, and a data marts layer. Users did not have access to view or modify data in the raw layer. The partitioned layer is kept to maintain all partitioned tables (dimension tables generally). Below is a simple schema design of our database: Challenges we faced with Aurora The system initially performed well, until it had to handle a throughput beyond 3K messages per second. This marked the onset of several challenges: As we exceeded a throughput of 3K messages per second, Aurora's Input/Output Operations Per Second (IOPS) limitations became a bottleneck. The scalability constraint had started impacting our operations. Scalability limitation: Every record update led to the creation of a new record and a dead tuple (previous version of the record). When the production rate of these dead tuples outpaced the cleanup process, bloating occurred. Since VACUUM FULL was not able to claim the storage, the disk usage continuously increased. For roughly 5 TB of data, Aurora was using 30+ TB of storage. Bloating issue: The bloating issue is directly tied to our maintenance challenges. With over 70 pipelines and a total write QPS surpassing 5k messages/second, we found PostgreSQL's auto cleanup process, Auto Vacuum, failed to keep pace with the rate of dead tuple generation. Therefore, manually running VACUUM or VACUUM FULL is required to recover the database. Our attempts with PostgreSQL tools like pg_repack and pgcompacttable also proved unsuccessful. Maintenance burden: Consequently, maintenance became increasingly complex and time-consuming. To accommodate the read & write workload, we had to scale to the highest available nodes (24XLarge). This led to an expenditure of approximately $100,000 per month for a three-node Aurora cluster. With this scale, Aurora turned out to be expensive because of IOPS auto-scaling. Cost: Searching for alternatives To resolve Aurora’s limitations, we set out to find a better alternative that met the following requirements: The database should support at least 10k+ write QPS and is horizontally scalable. Scalable with high write QPS: The database should be able to provide high-speed or real-time OLAP capabilities Real-time analytics: The database should be distributed across multiple sites to provide high availability and fault tolerance. Fully distributed: The database should maintain strong consistency, ensuring that all users see the same data. Strong consistency: Considering all the above requirements, we initially explored many PostgreSQL alternatives including Spanner and Yugabyte because we wanted to keep our change management minimal. Spanner Spanner is  a distributed SQL database management and storage service offered by Google.  It is fully managed on the Google Cloud Platform (GCP). However, we found that Spanner might not be a good use case for our architecture due to the following reasons: Spanner does not support schemas. We did not find the proper tools to load historical data. We explored Harbourbridge, an open-source tool for Spanner evaluation and migration. However, it had limitations around 100 GB of data loading. Yugabyte YugabyteDB is a high-performance transactional distributed SQL database for cloud-native applications, developed by Yugabyte. This database is very close to our use case because it was fully PostgreSQL compliant, horizontally scalable, and fully distributed. Unfortunately, it didn’t work as well because of its limitation with scalability, Our success criteria demanded 7k+ transactions per second but Yugabyte was only able to scale up to 5k. We also looked into other possible candidates like BigQuery, but none of them served our requirements well. Landing with TiDB After the above PostgreSQL alternatives, we decided to add HTAP to our requirements, which led us to TiDB. It supports out-of-the-box scalability, consistency, availability, multi-site deployment topology, and many more features. As a distributed database, TiDB has multiple components that communicate with each other and form a complete TiDB system. It is the stateless SQL processing component that provides the client-facing endpoint to the user. It locates the correct TiKV node to connect from PD to get the data. TiDB: It is a distributed transactional key-value data store that keeps the data in the left-closed-right-open range. Data is kept in shards with multiple replicas. TiKV uses the Raft protocol for replication. TiKV: The placement driver (PD) keeps the metadata of the cluster such as shard replica locations, and it’s also responsible for scheduling the shards across TiKV nodes. PD leader handles such tasks while other nodes maintain high availability. PD: The columnar storage extension that uses the Multi-Raft Learner protocol to replicate data from TiKV in real-time, ensuring consistent data between the TiKV row-based storage engine. TiFlash: The following features of TiDB  addressed our key challenges and met our operational requirements: Easy scaling The TiDB architecture design separates computing from storage, letting you scale out or scale in the computing or storage capacity online as needed. The scaling process is transparent to application operations and maintenance staff. ACID compliant TiDB is MySQL-compliant and supports transactions out of the box.  It supports both optimistic & pessimistic types of transactions. This makes it unique from other databases. Highly available TiKV stores data in multiple replicas and uses the Multi-Raft protocol to obtain the transaction log. A transaction can only be committed when the data has been successfully written into the majority of replicas. This guarantees strong consistency and high availability when a minority of replicas go down. Real-time HTAP TiDB combines both row storage (TiKV) and columnar storage (TiFlash) in the same architecture, forming a streamlined tech stack that makes it easier to produce real-time analytics on operational data. Our TiDB Infrastructure Our TiDB infrastructure is deployed over the VMs of leading cloud service providers.   We  use TiUP,  TiDB’s package manager, to manage the cluster and all the administrative operations. Our cluster is deployed over 3 available zones (AZs). Our cluster configurations are as follows: The PD layer has 3 nodes split across Multi-AZs. PD leader handles such tasks while other nodes maintain high availability. PD: The TiDB layer has 9 nodes of the n2-highmem-8 family. These nodes were chosen based on the memory requirements, with 64 GB RAM & 8 Core CPUs allocated for each TiDB node. TiDB: The  TiKV layer has 15 nodes of the n2-highmem-16 family which has 128 GB RAM & 16 vCORE CPUs. TiKV: By deploying our TiDB cluster across multiple AZs and carefully selecting node types to meet our processing and memory needs, we've created a robust, highly available infrastructure capable of handling our high data throughput requirements. Tuning TiDB for Our Case To make it work for our use case, we worked closely with the PingCAP team to tune the database. Here are some of the critical adjustments we made: Index Optimisation Set the following parameters before starting the index. SET @@global.tidb_ddl_reorg_worker_cnt = 16;
SET @@global.tidb_ddl_reorg_batch_size = 4096; Reset to default values after index creation. SET @@global.tidb_ddl_reorg_worker_cnt = 4;
SET @@global.tidb_ddl_reorg_batch_size = 256; Partition Pruning This is mainly important for partitioned tables. It analyses the filter conditions in query statements and eliminates (prunes) partitions  when they do not contain any required data. SET @@session.tidb_partition_prune_mode = 'dynamic'; Tuning analyse Sometimes the auto analyser in TiDB fails if a high volume of data is ingested. In that case, all the queries might use the wrong execution plan and end up scanning the full table. To avoid such a situation we made the following changes in TiDB configurations: set global tidb_max_auto_analyze_time = 86400;
set global tidb_enable_pseudo_for_outdated_stats = off;
set global tidb_sysproc_scan_concurrency = 15; If you are working with partitioned tables,   we suggest you run analyze table operations manually for one partition at a time to avoid analyzing failures. Through adjustments like these, we were able to effectively streamline our use of TiDB, so that we can achieve an optimal performance for our real-time data mart. Our Experience With TiDB Improved queries performance We have bench-marked 400+ queries and found that all the queries are running within SLA. We have even seen a 15-20% performance gain of P95 queries. Easy migration We used the TiDB Lighting tool to migrate all of our table's historical data from Postgres to TiDB. This tool is very easy to use and very fast. We were able to load terabytes of data within roughly 2-3 hours. However, it’s worth noting that there is a lot of tuning required before loading such huge data. Strong support We went through a couple of hiccups during the production infrastructure setup but the PingCAP support team played a very crucial role and helped us tune the cluster for the nature of the workload. Conclusion In this post, we have explored the challenges of using Aurora with our use case of real-time data marts and the migration journey to TiDB. We also discussed how Delhivery is using TiDB at scale. Despite our success with TiDB, we acknowledge that no solution is perfect, and effectiveness can vary depending on the use case. In TiDB, we noted a couple of areas for improvement, including the lack of out-of-the-box support for materialized views and native quota management. However, with appropriate workarounds and adjustments, we have managed to address these limitations effectively. So far, we have deployed TiDB in our production environment. Based on our benchmarks, TiDB enables us to handle over thousands of requests per second with less than 100ms latency. Moving forward, we will continue to explore more use cases that require a robust, consistently distributed database. References https://docs.pingcap.com/tidb/stable/tidb-lightning-overview https://reorg.github.io/pg_repack/ https://github.com/dataegret/pgcompacttable https://cloud.google.com/spanner https://www.yugabyte.com/yugabytedb/ https://cloud.google.com/bigquery/ https://docs.pingcap.com/tidb/dev/transaction-overview https://proxysql.com/ Author: (Senior Engineering Manager @ Delhivery) Hari Kishan (Director of Technology @ Delhivery) Akash Deep Verma

Delhivery’s Data Marts - Migration Journey from OLTP to HTAP

Data Marts de Delhivery: viaje de migración de OLTP a HTAP

Data Marts de Delhivery - Parcours de migration d'OLTP vers HTAP

डेल्हीवरी का डेटा मार्ट - ओएलटीपी से एचटीएपी तक प्रवासन यात्रा

Delhivery のデータ マート - OLTP から HTAP への移行ジャーニー

Data Marts da Delhivery - Jornada de migração de OLTP para HTAP

Data Marts của Delhivery - Hành trình di chuyển từ OLTP sang HTAP

Delhivery 的数据集市 - 从 OLTP 到 HTAP 的迁移之旅

As the Director of Data Engineering at (India’s leading fulfilment platform for digital commerce), I’m surrounded by a huge amount of data. Over 1.2 TB per day, to be exact. Delhivery Delhivery fulfills a million packages a day, 365 days a year. Its 24 automated sort centres, 85+ fulfillment centres, 70 hubs, 3,000+ direct delivery centres, 7,500+ partner centres, 15,000+ vehicles and 40,000+ team members run smoothly thanks to a vast network of IoT devices. There are nearly 60,000 data events and messages coming in and going out of our pipelines per second. With this much data, it’s probably no surprise that data discovery and organization are a big challenge. We finally found our dream data cataloging solution, but it was no simple task. At Delhivery, we started our journey with a data catalog in 2019. Over the next year and a half, we considered and invested in several different types of solutions. We evaluated traditional enterprise-focused data catalogs (e.g. Alation, Collibra, and Waterline), built our own catalog with Atlas and Amundsen, and later adopted the modern SaaS unified data workspace, Atlan. In this blog post, I’m unpacking all of my learnings as a lead engineer on this project in the hopes that it helps other data practitioners and leaders who are facing similar challenges and want to successfully implement a modern data catalog. Why Delhivery desperately needed a data cataloging solution As Delhivery has grown over the past decade, the scale and complexity of its data have grown even faster. Earlier in its history, Delhivery generated 1 TB of data per quarter. Now we’re generating 1.2 TB . per day That data is organized and processed by hundreds of microservices, which means that ownership over our data is distributed across different teams. Delhivery started with a monolithic system, but we took the call to start forking services about 4 years later as the business scaled exponentially. Teams soon started building their own microservices, motivated by a desire to make data-driven decisions. Everyone wanted to find and access specific data, so they’d reach out to several developers and ask for help. Later on, we realized that developers were becoming a bottleneck, and we needed a Google Search–style way for anyone to find data through a common portal. However, finding data wasn’t the only issue. Once teams got hold of the data they wanted, they struggled to understand it. There wasn’t a clear way to organize our data and add important information and business context. This quickly became clear throughout our onboarding process — the typical time to onboard a new team member was 1-2 months, but this process eventually grew to 3-4 months as Delhivery and its data kept growing. By 2019, we realized we desperately needed a data cataloging solution, one where people could navigate through all our data, look for whatever they need, check what a data asset looks like, build a better understanding of other domains or teams within the company, and even add their own info or context about a data asset. Step 1: Evaluating available commercial data catalog solutions in the market We started off our search for a data catalog by evaluating commercial products like , Waterline (now called ) and . We dove deep into their specs and compared them on two main criteria: Alation Lumada Colibra : Did the product have all the features we needed? Features : What was the total cost of ownership (i.e. the purchase price plus other costs like set-up or operations)? Did it fit with our budget? TCO Buying one of these products would have been the simplest fix, but unfortunately, we couldn’t find the right solution. Each one was either missing non-negotiable features (such as seeing a data preview or querying data) or the TCO was just too high for us (due to expensive set-up, licensing and professional service fees). We didn’t want to settle for something that wasn’t quite right, since setting up a data catalog is a huge commitment. So we realized we needed to build and customise our own internal solution. Step 2: Building our first data catalog with Apache Atlas and Amundsen To set up our first data catalog, we kicked off an internal project with two developers. They worked full-time for seven months to develop and customise a data cataloging solution based on open-source software. After researching different options, we decided to use as the backbone of our metadata management framework. Apache Atlas We chose Atlas mainly because of its available features. As a bonus, we also liked that it was widely used, actively in development, backed by Apache, and supported by the entire Apache community. However, as we started implementing Atlas, we realised we had a problem. Atlas is great for developers, but it has a steep learning curve for non-technical team members. We found its interface and language was highly technical, which made it difficult to onboard product managers or operations people who needed to understand our data. In short, Atlas had the features we needed, but it wasn’t right for our end users. To solve this, we decided to bring in . Started at Lyft and advertised as “Google search for data”, Amundsen is a data discovery service that became the search interface for our Atlas backend. Amundsen Step 3: Developing missing features on top of Amundsen and Atlas As we implemented and tested our open-source data catalog, we realized that Atlas and Amundsen still didn’t have all the features we needed. So we started modifying our solution and developing new features on top of it. Here are a few of the features that we built for our internal data catalog: Searching on columns, rather than the default option of searching on tables Better search organisation, based on custom criteria like popularity or relevance Displaying whether columns that appear in search have already been cleaned Improving how columns on a data asset’s detail page are organised to improve relevance Showcasing data quality with Markdown and APIs Our internal Atlas + Amundsen catalog Step 4: Rolling out version 1 of our data catalog to the end users After selecting Atlas and Amundsen, setting them up, and developing our own features on top of them, we finally finished our first data catalog. We rolled it out to the company, but we quickly struggled with getting people to use it. Initially, team members were excited to use the new data catalog. But after logging in a few times, they just didn’t return. It didn’t become a habit, something that they would use continuously. When we asked people what was missing, they all pointed to the user experience. The key problem was that they felt like they weren’t able to grab hold of the catalog and integrate it into their daily work. We had created our catalog with two developers, no UI or UX designers involved. We thought that was fine since the design and layout had already been fully thoughtout by Amundsen. We underestimated the importance of user experience. It plays a crucial role in driving data culture, and our solution couldn’t win without creating an experience that people wanted to use every day. Step 5: Back to the drawing board... After seven months of development, we weren’t much farther than where we started. Even though we had invested so much time and energy in our open-source data catalog, it wasn’t right. Time to go back to the drawing board and figure out our next move… After some thinking and a new roadmap, we realised that fixing our current solution wouldn’t be easy. User experience was key, so we couldn’t succeed without lots of hard work on our interface. We would need a full product team — UI/UX designers, product managers, and engineers — to revise our data catalog so our colleagues would actually use it. At the same time, we started researching commercial options. Yes, we had looked into these products before, but the data catalog market had seen a ton of innovation in the 18 months since our earlier research. So we started our search again, finding and evaluating the latest commercial data cataloguing products. That’s when we found Atlan. Step 6: Choosing Atlan as our unified data workspace is a modern data workspace that excels at data cataloging and discovery, but it is much more than just a traditional data catalog. In our research, we found it to be the best solution for our company. Atlan The Atlan team actually started as a data team themselves, and they built Atlan internally for their work. This has driven their collaboration and user experience–focused approach, making it closer to a . Github or Figma-like experience for data Atlan not only had all the dream features we were looking for, but the were extremely intuitive. Built around a Google-style search, Atlan was designed to be user-friendly for technical and non-technical users alike. Its collaboration-first approach — e.g. the ability to share data assets as links, or its integrations with Slack — was also very promising. When we gave Atlan to team members, they were able to get onboarded and start driving things very quickly. user interface and experience We also liked that Atlan has a fundamentally open philosophy. It was built on open-source (Apache Atlas and Ranger) with an open API backbone. This gave us the ability to build on top of Atlan and customise the product for our end users — bringing together the best of open-source with a polished, off-the-shelf user experience. When we had compared our roadmap with commercial products, we realised that the time we had already sunk into our open-source solution didn’t matter. It would take six or seven people and up to two years to build what Atlan gave us out of the box. We needed a solution on day zero, not in a year or two. As counterintuitive as it seemed, we realised that buying a data cataloging product would be a lower TCO than continuing to build our own. We set aside our internal catalog and said a big yes to Atlan. We also decided to go with Atlan because we realised that we needed a partner who was an expert in data discovery. This would let us rely on their expertise as we rolled out our solution within the company. We were happy to let them be the experts in data cataloging — we had plenty of our own business problems to tackle. The workshop sessions that Atlan conducted during our Proof of Concept also helped us feel sure that we were partnering with the right solution and team. These continuous workshops helped us onboard new team members, dive deep into advanced features, and maintain traction as team members started documenting data and filling our new metadata stores. Atlan’s metadata glossary, which helps users document and understand data assets. Some final thoughts on our build-vs-buy adventure I’ve been on this wild data cataloging journey for the past year and a half. There have been plenty of ups and downs, but I’m excited about where we’ve ended up. I think we’ve finally found the right platform to make our data self-service, discoverable and clear for our data-driven teams. The other day, someone asked me what I’d do differently if I was to start all over again. I realised I had two big learnings. Figure out the end consumers of your data — whether they’re product managers, engineers or operations people — and their daily use cases. What data-driven decisions are they making? How do they find data, and what information do they need about it? How can you remove frustrating bottlenecks like reaching out to developers? Focusing on your users’ needs upfront can save you from shelving a functional but poorly designed solution like us. Remember, if your end users are not developers, then ease of use and intuitiveness is key. First, user experience is key. Instead of waiting until data discovery becomes a problem, it’s much better to start on day one. This lets you scale your process and solution as your company and data scale, rather than having to shoehorn a new product into years of established workflows. Second, start early with data discovery and cataloging. Author: Akash Deep Verma, Director of Data Engineering Image credit: Header photo by CHUTTERSNAP on Unsplash . Also published on: https://humansofdata.atlan.com/2021/05/delhivery-learning-data-catalog/

Build vs Buy: What We Learned by Implementing a Data Catalog

Welcome to HackerNoon’s interview series. This section is curated by our Editorial team to spotlight bold and disruptive startups across the globe. If you’ve been nominated, and answer these questions Startups-Of-The-Year create a brand profile here. Hey Hackers, Syook has been nominated in HackerNoon's annual Startup of the Year awards in Bengaluru, India Please vote for us . here Read more about us below to understand why we deserve your vote. Meet Syook Syook enables organizations to build their own IIoT and RTLS solutions to track assets and people in real time. Syook provides a no-code platform to build customized and high-value digital technology solutions that help companies improve their operations across various industries like oil and gas, healthcare, manufacturing, defence etc. I am Arjun Nagarajan, the co-founder (Syook’s other cofounders are Saurabh Sharma and Aman Agarwal) and CEO of Syook with over 16 years of industry experience. I have previously worked in the Oil and Gas sector in various parts of the world and am a graduate of IIT Guwahati, graduating with a bachelor's degree in civil engineering in 2006. How We're Disrupting/Improving the Real Time Location System Industry We saw a gap in the market and founded Syook with the goal of enhancing operational efficiency. We created InSite, a location platform that allows businesses to track all of their mobile assets from one place. Users receive real-time location, dashboards, alarms, and analytics with InSite. InSite provides users with many benefits, solutions, modules and features that assist them to grow their business by enhancing their operations, profitability, reducing costs and increasing efficiency. Standing Out from The Crowd Here are some of the USPs we have: : Our solution comes with independent and interchangeable features with easy integration. Modular : Our solution can be easily modified and tailored to meet user needs and requirements. Configurable : Our complete solution has been developed in house, with zero dependencies of any 3rd party Syook InSite can be hosted on any Cloud Network, Private Data Centre or even On-Premise. Cloud Agnostic : From BLE based RTLS for low-cost deployments to  LoRaWAN deployments for larger facilities and moving towards integrating GPS location for citywide or countrywide asset tracking, Syook InSite has all location needs covered. This makes it the best fit for system integrators. Technology Agnostic : With expansion orders and the addition of user departments our customers regularly show their appreciation. Our customers include UPL, Unilever, Delhivery, Fortis Hospitals, Fiat Chrysler Automobiles and more. Proven Customer Success : The no-code platform makes it deployable at an unprecedented speed, hence making us grow exponentially across various markets and industries. Quick deployment Our Predictions/Thoughts on the Real Time Location System Industry in 2023 According to marketsandmarkets.com, The Real-Time Location Systems (RTLS) Market is estimated to be valued at USD 3.9 billion in 2021 and reach USD 12.7 billion by 2026, at a CAGR of 26.5%. We believe the real time location system industry would be booming What word defines the state of Real Time Location System in 2023? Exciting and booming! Why we decided to participate in HackerNoon's Startup of the Year awards We’re honoured and excited to participate in Hackernoon’s Startup of the Year award. Final Thoughts We’re excited to be a part of Hackernoon’s community! today! Vote for us

Vote for Syook in HackerNoon's Startups of the Year 2023

Retailers are always on a hunt for something that can help them connect better with the customers. And, this is the reason why the retail industry has seen such a massive transformation in previous years. There was a time when retail was solely dependent on marketing activities to drab customers attention but, things have changed. Now, the retail industry is dependent more on technology. Marketing is obviously important but, tech-oriented marketing is the future of this industry. Augmented reality, drone delivery, virtual reality are some of the techs used by modern retailers. These terms create a sci-fi kind of hype among the customers and trigger them to try on products that are being sold. So, let’s take a look at what the future of retail looks like and what are some of the latest technologies in the retail sector. Product Video Catalogs 52% of consumers say that watching product videos makes them feel more confident about going ahead and making a purchase. - Digiday With a combination of visuals and sound videos stand-out at grabbing the attention of consumers. People are bored with the usual e-commerce product listing which consists of image sliders and description at the end. These can be replaced with a short video which consists of the image as well as the description. This is a better way to showcase the product. And, video creation has become very simple with the upcoming video automation platforms like . Existing e-commerce listings can easily be converted into videos by using the existing images and copy. With this, we see video technology in the retail sector at its very best. You can see many such product videos on . StoryXpress target.com Virtual Reality Virtual reality is the use of computer technologies to provide a completely stimulating experience. In retail virtual reality is expected to reduce product returns and marketing cost. It is also predicted that it will provide efficient user analytics and data on a particular product. Some of the retail enthusiasts are seeing VR as the future of retail whereas the others think that it is just a trend that will pass by. Another reason why some retailers are interested in the tech is because of hype and sci-fi aspect it brings along which creates enthusiasm in the consumers. is an example to leverage VR for retail marketing and sales. Volvo used Google Cardboard to put up a virtual reality test drive for customers through their mobile phones. This app takes the person inside an SUV and makes them feel like they are sitting in one and driving through various landscapes. Other car companies like Lexus, Jaguar, Ford, are also similarly using VR headsets. Volvo Reality app Augmented Reality Augmented Reality(AR) is a cool tech which enables customers to decide which product suits them better. AR-enabled products are loved by people as it provides them with the option of trying on products without actually visiting the store or wasting money on them. Retailers are always on a try to stay ahead of the competition and provide a better shopping experience to their customers and, this is the main reason why more retailers are interested in the tech. Many retailers have already integrated AR-enabled applications and devices. With passing years this technology is becoming bigger and better. 2019 witnessed a lot more brands getting started with the AR technology to provide a better shopping experience to customers. is one of the latest luxury brands to start with AR technology. It recently added the AR feature to its application which enables users to ‘try on’ sneakers. Gucci <blockquote = > <a href="https://twitter.com/hashtag/AR?src=hash&amp;ref_src=twsrc%5Etfw">#AR</a> <a href="https://t.co/Qsq4Oybjsf">https://t.co/Qsq4Oybjsf</a> <a href="https://t.co/ytXqJdQk4E">pic.twitter.com/ytXqJdQk4E</a> &mdash; Ricardo (@RicO2O) <a href= >June , < blockquote> class "twitter-tweet" Gucci’s new < = = > p lang "en" dir "ltr" try-on app is pretty cool </ > p "https://twitter.com/RicO2O/status/1144538565210079233?ref_src=twsrc%5Etfw" 28 2019 /a></ < = = > script async src "https://platform.twitter.com/widgets.js" charset "utf-8" </ > script Same-day Delivery with pick-up & pack companies Most of the retail companies are already providing next day delivery but, same-day delivery is becoming the next big thing. It has been observed that people are ready to pay more to get their product delivered faster. This may increase the cost for brands but, it also leads to more sales and gives an edge over competitors who do not provide the service. In years we have seen a grown number of delivery companies coming up. These companies act as a personal courier service for the retailers to get their product delivered to the customers. These have grown in popularity specially among the small e-commerce companies as it provides the same-day delivery which otherwise would not have been possible. Some of these companies are , , which provide same-day product delivery service to their partnered e-commerce companies. Shipito Shippo Delhivery Other than these delivery companies some of the e-commerce giants like Amazon claim to start product to provide same day delivery to its customers. delivery via drones Chatbots, Assistants & AI In 2019 and beyond, robots will invade all the online stores. That’s right, chatbots and AI-driven custom shopping experience for better search results according to individual choice and preference. AI assistants can handle multiple tasks assigned to human beings, all at once. These tasks may include managing inventory and, handling queries related to the product. These digital assistants can carry out various tasks, which will ultimately free-up time so that other important business matters can be taken care of. Chatbots can fulfill several customer service needs, from answering the common questions about a product to discussing some repetitive issues. Chatbots learn from customer conversations and come up to better assistance for a users personalized shopping experience. And people are also becoming more comfortable getting answers to their questions from a chatbot or a virtual assistant while shopping. And, adding bots has become easier than ever, there are many tools like available which allows bot creation for free. HubSpot's Chatbot Builder Just Walk Out Shopping These stores go by the caption “No Line. No Checkout”. These are super high-tech convenience stores which were introduced by Amazon in the year 2018 as . Customers scan their phone with the Amazon Go app at the entrance of the store and just grab the item they want to buy and leave. They get the receipt on their mobile phones, for which they can make online payments. Amazon Go This “Just Walk Out” technology by Amazon is powered by sensors, deep learning, and computer vision. This is just an amazing trend which shows how the future of physical retail will be. With already a few operational stores in the US, Amazon thinks of expanding this to other countries as well. Will other retail brands come up with such stores in the future? Well, many retail experts do believe that we might see many such stores coming up. So, that was all from the latest retail tech’s which we ought to see in 2019 and beyond. But, I am sure that with the current speed of retail expansion and use of technology in the sector we may soon witness something even more astonishing. Retail giants are expanding and are constantly trying to improve the shopping experience for customers and I believe that tech will drive the future of retail.

Tech-Trends to Drive the Future of Retail in 2019 and Beyond

E ship their products to customers via multiple courier partners. Most of them have an automated courier selection process for shipping. At IB one of the major challenges we faced was the increasing logistics cost. Hence we developed a system to automatically recommend the courier to be selected for each package that was shipped from our warehouses and by our sellers spread across India. It is essential for every company who rely on multiple courier service to optimise their logistics cost. Through this article I would like to give a brief overview of a simple courier selection system. This system need not be a web app, this could be even run on a excel based system and hence even small companies can take the advantage of this approach. commerce companies Master Data This include the data of From, To COD & To Prepaid pincodes of each LSPs along with amount limit and their pincode zones. Rate chart and other terms in the contract such as COD charges, AWB charges. Pincode Zones — Couriers categorize their serviceable pincodes into different zones. e.g. North east, West etc.. Rules Below are the criteria for selecting a courier for a shipment Serviceability Freight Cost Turn around time Service levels such as rate of RTOs, Damages, lost in transit, COD remittance TAT, Pickup TAT Let us go through each step in detail 1. Serviceability This process includes getting the list of LSPs which are serviceable. This serviceability check is done on the following parameters 1. Checking which all LSP serves both From & to Pincodes. 2. Checking which all LSP serves COD/Prepaid amount of the package. 3. Check if the Charged weight of the package exceeds the weight limit of the selected LSPs = Max (physical wt, Volumetric wt) Charged weight of the packet a. Physical weight = “Weight of SKU” b. Volumetric weight = “ L*b*h / volumetric factor ( each LSP has a different volumetric factor. L,B,H in cm) 4. If the Product Shipping Category is supported by the LSP. Various Products types falls under the following category Normal Liquids Paints Dangerous goods Fragile Gases 5. Number of Boxes — This is applicable in case of multi part shipment where a single product is shipped in multiple packages. Here a single AWB will have multiple boxes. Not all the LSPs support multi part shipments 2. Freight Cost Though cost is not the only criteria for selecting an LSP, it is one the most important parameter as its reduction can substantially improve the net earnings per package. Currently most of the ecommerce players charge a nominal shipping charge from the customers. However the actual freight charge will be different. This can go as high as 80% of the product value. I have even seen cases where freight charges is higher than the product value. The freight cost optimization can have a major impact especially in B2B ecommerce where the product size varies widely (from a small box of screws to big items such as solar panels and heavy machinery). Every LSP shares a Rate chart according to the contract signed. Below are some sample Rate charts. Fig.2 Rate Chart Type 2 FedEx, Bluedart etc. have Type 1 rate chart. Delhivery has type 2 rate chart. Some other couriers such as Safexpress has a 3rd type of rate chart as below Fig.3 Rate Chart type 3 Fig.4 Zone Matrix Rate Charts- LSPs prepare rate charts by laying out the flat rates applicable on the kg slabs against the Rate group. Refer to fig.3 Zone matrix — This matrix defines rate group which will be applicable on movement of goods from one zone to another. Refer to Fig.4 Type 3 is similar to Type1. The difference here is that instead of directly mentioning the per kg rate against the from-to zones, they give us a flat rate against the weight slab and a weight group. And then this rate group is mentioned in the zone matrix. Here we can take the type 3 case for freight calculation since type 1 & 2 charts can be structured to fit into type 3. The rate chart and the zone matrix is used to calculate the base freight. There will be other charges contributing to the freight based on the contract. Below is a list of such charges. COD charges - max (min COD charge, % of invoice value) AWB charge — Flat amount Fuel Surcharge — % of invoice value Freight on Value(FOV)- % of invoice Risk on Value(ROV)- % of invoice Additional charges- % of invoice Chargeable weight — Max(Min chargeable weight , Max (physical wt, Volumetric wt)) Base freight = If (chargeable weight ≤ max(weight slab)) { Rate(rate group,weight slab) Else  { Rate(rate group, max(weight slab))+(Roundup((chargeable weight- max(weight slab))/additional slab),* charges per additional slab } Note : Rate group is derived from the zone matrix. E.g.Suppose a case where ‘from pincode’ belongs to zone west and ‘ to pincode’ belongs to East and the packages chargeable weight is 3kg. Thus the rate group applicable will be ‘A’ as per fig.4 and referring to the fig.3, the base cost will be 80. CAF value= (Base freight+ COD charges+ AWB charges + Fuel surcharge+FOV+ROV+Additional Charges)* CAF(%)/100 Service tax=(Base freight+ COD charges+ AWB charges + Fuel surcharge+FOV+ROV+Additional Charges + CAF)*18/100 = Base freight+ COD charges+ AWB charges + Fuel surcharge+FOV+ROV+Additional Charges + CAF+ service tax . Total freight From this we would get the freight cost of all the serviceable LSPs and rank it in the ascending order of cost. 3. Turn around time (TAT) Each order may be having a expected delivery date. Based on historical trend of the we may derive a zone to zone TAT matrix. We can chose define a positive deviation in TAT that can be allowed either at global level or at package level. Or we use a 4. Service levels such as rate of RTOs, Damages, lost in transit, COD remittance TAT, Pickup TAT etc. The service level parameters can be used to derive performance rating for each LSP. A simple rating formula would look like this Rating of an LSP =((1- ((RTO cases+Damage cases+Lost in transit)/Total number of shipments in the past 3 months))+(1/Avg COD remittance TAT)+(1+Avg Pickup TAT))*100 You can give more weightage to each items according to your choice. You can select the courier based on individual criteria or use a combination of all. Originally published at www.abhilashjohn.com on Nov 8, 2017.

Courier selection engine for E commerce companies

Half a year ago I was an Amazon driver in rural Germany, it was not at all fulfilling. I was working long hours and had no time to myself. One day I had to take a few weeks off from work to do some documents, and that's when I realized I did not want to go back to this job. So I turned to programming. I have always regretted not getting a CS degree when I had the chance earlier in my life, but I was at a make-it-break-it point because I did not want to be a delivery driver anymore. I went back to work but every morning I would get up early and do a free coding course which I finished in three weeks. After that, I went into another one which I also ended up finishing in three weeks. Now I had a solid foundation and basic understanding of web apps due to learning Django, but along with all of that, I was also introduced to React. React was very interesting to me so I decided to go in a direction where React would be a priority. So I found a MERN focused course and ended up almost finishing it fully. I did encounter a learning burnout (I had been studying daily for close to 6 months now…), it was due to be already being skilled enough to make my own projects and I was tired of learning. Now that I had a solid foundation I teamed up with my best friend to work on one of his projects, he tasked me with making the front end for this web app. After completion, I now had enough experience that I felt confident enough to try making my own full-stack web app. During Christmas, my mother visited and we talked about AI, she is a producer and during that time she was working on a movie about AI. From that, I got the idea of making a script-reading AI, since producers often get offered many scripts that they do not have time to read so they outsource that to script readers. So why not kill two birds with one stone and make AI do it? It will save time and money. So I ended up building AIScriptReader in a month using NextJS which I had to learn as I went because it seemed like an awesome tool to know. I also ended up running into issues with NextJS but I was too time invested in it so I had to make a Django microservice that uses Celery to deal with async functionality. Also, I ended up learning Docker and Caprover specifically to make this all work and allow for smooth and simple CI/CD. Now looking back on my journey I am amazed with what I have achieved in the short amount of time that I have spent, from where I was half a year ago to today. I think I was able to do it because I knew what I did not want to do, and I knew that I liked building things with computers. The point is, just do it. I did it all for free, thanks to the various online courses which I found online and even offer certificates. Do it a little at a time because the snowball will start rolling and you will be in a better place day by day. Half a year ago I was an Amazon driver in rural Germany, it was not at all fulfilling. I was working long hours and had no time to myself. One day I had to take a few weeks off from work to do some documents, and that's when I realized I did not want to go back to this job. So I turned to programming. I have always regretted not getting a CS degree when I had the chance earlier in my life, but I was at a make-it-break-it point because I did not want to be a delivery driver anymore. I went back to work but every morning I would get up early and do a free coding course which I finished in three weeks. After that, I went into another one which I also ended up finishing in three weeks. Now I had a solid foundation and basic understanding of web apps due to learning Django, but along with all of that, I was also introduced to React. React was very interesting to me so I decided to go in a direction where React would be a priority. So I found a MERN focused course and ended up almost finishing it fully. I did encounter a learning burnout (I had been studying daily for close to 6 months now…), it was due to be already being skilled enough to make my own projects and I was tired of learning. Now that I had a solid foundation I teamed up with my best friend to work on one of his projects, he tasked me with making the front end for this web app. After completion, I now had enough experience that I felt confident enough to try making my own full-stack web app. During Christmas, my mother visited and we talked about AI, she is a producer and during that time she was working on a movie about AI. From that, I got the idea of making a script-reading AI, since producers often get offered many scripts that they do not have time to read so they outsource that to script readers. So why not kill two birds with one stone and make AI do it? It will save time and money. So I ended up building AIScriptReader in a month using NextJS which I had to learn as I went because it seemed like an awesome tool to know. I also ended up running into issues with NextJS but I was too time invested in it so I had to make a Django microservice that uses Celery to deal with async functionality. Also, I ended up learning Docker and Caprover specifically to make this all work and allow for smooth and simple CI/CD. AIScriptReader Now looking back on my journey I am amazed with what I have achieved in the short amount of time that I have spent, from where I was half a year ago to today. I think I was able to do it because I knew what I did not want to do, and I knew that I liked building things with computers. The point is, just do it. I did it all for free, thanks to the various online courses which I found online and even offer certificates. Do it a little at a time because the snowball will start rolling and you will be in a better place day by day.

From Amazon delivery driver to deploying my Full-Stack app in 6 months

Are You Hungry, Bro? Let’s Hack Your Favorite Food Delivery App

هل أنت جائع يا أخي؟ دعنا نجرب تطبيق توصيل الطعام المفضل لديك

¿Tienes hambre, hermano? Vamos a hackear tu aplicación de entrega de comida favorita

Tu as faim, mon pote ? Hackons votre application de livraison de repas préférée

Ou grangou bro? Ann Hack App Livrezon Manje Pi renmen ou a

Éhes vagy, tesó? Hackeljük meg kedvenc ételkiadó alkalmazását

Lapar, Bro? Yuk Retas Aplikasi Pengiriman Makanan Favoritmu

Hai fame, fratello? Facciamo un hack della tua app di consegna di cibo preferita

お腹が空いていますか？お気に入りのフードデリバリーアプリをハックしてみましょう

Vai tu esi izsalcis, brāli? Uzlauzsim jūsu iecienītāko ēdienu piegādes lietotni

Adakah Anda Lapar, Bro? Mari Menggodam Apl Penghantaran Makanan Kegemaran Anda

A je i uritur, vëlla? Le të hakojmë aplikacionin tuaj të shpërndarjes së ushqimit të preferuar

Ulambile, Bro? Masisebenzise uhlelo lwakho lokusebenza oluthandayo lokulethwa kokudla

It can be tough for fresh e-commerce startups to compete with the big names (not to mention the millions of competitors you must contend with), but sometimes, all it takes is a little innovation to make a name for yourself and stand out from the crowd. For tech innovator and startup founder Raj Jaiswal, e-commerce success means leveraging modern technology to solve specific and significant customer problems while staying nimble in a digital market that’s always changing. To strike this balance, Raj embarked on a journey of trial and error, diverse experiences, and personal growth — gaining valuable insights from every step along the way. Learn more about the challenges he’s discovered in e-commerce entrepreneurship, how he’s blended tech acumen with business savvy, and how he’s changing the face of e-commerce with Neyon. The Challenges That Early-Stage E-Commerce Startups Face Raj knows full well that startups face a lot of challenges, especially when coming up with the right ideas, building a solid team, and knowing when to pivot. For example, about 32% of innovation failures happen because the wrong idea is chosen. Surprisingly, many entrepreneurs put too much money and effort into refining a product without checking if customers even want it or are willing to pay for it. When it comes to building a team, Raj knows that the success of any given startup depends largely on the people behind it. Even with a groundbreaking idea, any new startup will likely fail (60% of the time, no less) if it doesn’t have a strong and adaptable team to bring that idea to life. That’s because technical skills and experience aren’t the only ingredients for success — passion, a shared vision, and creativity are what truly drive a startup forward. Finally, Raj notes that an inability to pivot when things aren’t going as planned (like a change in leadership or market demand) is a sign of a rigid team. It’s one of the top reasons that startups don’t make it. But while it can be a challenge, 94% of companies that successfully pivot are more likely to keep innovating. This not only keeps their ideas fresh but also makes them more appealing to venture capitalists, boosting their chances of securing funding. To tackle these challenges, Raj is turning to artificial intelligence — and in doing so, he’s bringing efficient workflows and scalable infrastructure to modern e-commerce. The Building Blocks of Innovation: How Raj Jaiswal Learned to Blend Technical Skills with Entrepreneurship In Gujarat, India, entrepreneurship is practically in the water. Growing up there, Raj Jaiswal was surrounded by a culture that celebrates building something from scratch. On top of the local culture, his parents were both entrepreneurs, and as Raj watched them build their businesses, it sparked a love for innovation and problem-solving that paired nicely with his fascination for technology. Upon reaching adulthood, Raj decided to take a leap and move to the U.S., eager to dive into the world of tech startups. His big break came when his first startup got accepted into Brown University’s Summer B-Lab incubator. This initial experience building a startup was in the form of an on-campus delivery platform called Fetchum. It was here that Raj created real-time matching algorithms and built multi-profile app interfaces that made it easier for students to connect with local vendors and get their deliveries quickly. But Fetchum also taught him the unpredictable nature of entrepreneurship and the importance of pivoting when challenges arise. “When my co-founder at Fetchum had to step down,” Raj explains, “I unexpectedly took on the role of CEO in addition to my CTO duties, handling everything from fundraising to operations.” This experience pushed him to quickly master time management, seek mentorship, and broaden his skills beyond tech — making him a more adaptable and well-rounded leader. In his next role, Raj got a firsthand look at how crucial solid ideation is. One of his proudest achievements was his work at Circulation, a health-tech startup that modernized non-emergency medical transportation (NEMT). As one of the organization’s early engineers, Raj was instrumental in building the core technology behind the platform, including developing its scheduling and booking systems and integrating it with ride-sharing platforms like Uber and Lyft. It was through this experience that Raj learned the value of creating a solution that truly solves a customer’s problem. “It wasn’t just about building impressive tech,” he says. “It was about understanding the real needs of patients who struggled to get to medical appointments.” The success of Circulation, which led to its acquisition by ModivCare, showed him that a smart idea only works if it addresses a genuine need and if there’s a strong, adaptable team to bring it to life. Next, at GoPuff, a fast-growing delivery startup, Raj Jaiswal used his technical skills to tackle big challenges in supply chain management. Leading a team of six, he developed systems for real-time procurement enabled by real-time demand forecasting and improved how the company processed and stored large amounts of data. By leveraging complex data migration strategies to optimize these workflows, Raj saved the company 90% in supply chain data storage costs while ensuring a reliable procurement process. How Neyon Revolutionizes E-commerce Raj is now channeling his passion into his new startup, Neyon, which is set to shake up online shopping. With Neyon, he’s building an AI-powered search engine focused on the apparel industry, aiming to make shopping more intuitive, personalized, and efficient. Leveraging cutting-edge natural language processing, computer vision, and machine learning, Neyon will have a robust suite of tools like image recognition and conversational AI that understand what customers are looking for and help them find it faster. “I had an ‘aha’ moment while trying to find a specific coat online,” Raj recalls. “After 30 minutes of searching with no luck, I thought, ‘There has to be a better way.’ That frustration was the spark that led to Neyon.” With Neyon, Raj believes that AI can bridge the gap between what shoppers imagine and what they find online. It isn’t just about making shopping easier; it’s about solving a real customer problem — helping people discover the relevant and stylish clothes they truly want. While Neyon’s tech is powered by AI, Raj credits his success to the insights he’s gained throughout his journey. “I spotted this major customer pain point because my time at startups taught me to see the real impact of a problem and whether a solution can truly take off." He also credits Neyon’s ability to pivot and adapt to his team-building experience. “I’m confident Neyon can handle whatever the e-commerce market throws our way because I’ve built a team that’s not just packed with tech skills but also knows how to think creatively to solve problems.” Going Beyond Innovation With Raj Jaiswal But Raj’s vision goes beyond Neyon. As the NYC Ambassador for the Brown University Alumni Entrepreneurship Group, he mentors alumni, organizes events, and builds connections within the startup community. By sharing his experiences and guiding aspiring entrepreneurs, Raj is committed to shaping the next generation of tech innovators. “This way,” Raj says, “we can build a future where AI technology and innovation truly serve people, not just profits. That’s the core lesson startups need to remember.” Raj Jaiswal believes that true startup success comes from balancing tech innovation with essential entrepreneurship skills. His journey, shaped by the promise of AI and the pitfalls of overlooking the bigger picture, taught him the importance of mastering ideation, team building, and pivoting to revolutionize consumer tech. Want to learn more? Check out Neyon, and follow Raj on LinkedIn. It can be tough for fresh e-commerce startups to compete with the big names (not to mention the millions of competitors you must contend with), but sometimes, all it takes is a little innovation to make a name for yourself and stand out from the crowd. For tech innovator and startup founder Raj Jaiswal, e-commerce success means leveraging modern technology to solve specific and significant customer problems while staying nimble in a digital market that’s always changing. To strike this balance, Raj embarked on a journey of trial and error, diverse experiences, and personal growth — gaining valuable insights from every step along the way. Learn more about the challenges he’s discovered in e-commerce entrepreneurship, how he’s blended tech acumen with business savvy, and how he’s changing the face of e-commerce with Neyon. The Challenges That Early-Stage E-Commerce Startups Face Raj knows full well that startups face a lot of challenges, especially when coming up with the right ideas, building a solid team, and knowing when to pivot. For example, about 32% of innovation failures happen because the wrong idea is chosen. Surprisingly, many entrepreneurs put too much money and effort into refining a product without checking if customers even want it or are willing to pay for it. about 32% of innovation failures about 32% of innovation failures When it comes to building a team, Raj knows that the success of any given startup depends largely on the people behind it. Even with a groundbreaking idea, any new startup will likely fail ( 60% of the time , no less) if it doesn’t have a strong and adaptable team to bring that idea to life. That’s because technical skills and experience aren’t the only ingredients for success — passion, a shared vision, and creativity are what truly drive a startup forward. 60% of the time 60% of the time Finally, Raj notes that an inability to pivot when things aren’t going as planned (like a change in leadership or market demand) is a sign of a rigid team. It’s one of the top reasons that startups don’t make it. But while it can be a challenge, 94% of companies that successfully pivot are more likely to keep innovating. This not only keeps their ideas fresh but also makes them more appealing to venture capitalists, boosting their chances of securing funding. 94% of companies that successfully pivot 94% of companies that successfully pivot To tackle these challenges, Raj is turning to artificial intelligence — and in doing so, he’s bringing efficient workflows and scalable infrastructure to modern e-commerce. The Building Blocks of Innovation: How Raj Jaiswal Learned to Blend Technical Skills with Entrepreneurship The Building Blocks of Innovation: How Raj Jaiswal Learned to Blend Technical Skills with Entrepreneurship In Gujarat, India, entrepreneurship is practically in the water . Growing up there, Raj Jaiswal was surrounded by a culture that celebrates building something from scratch. On top of the local culture, his parents were both entrepreneurs, and as Raj watched them build their businesses, it sparked a love for innovation and problem-solving that paired nicely with his fascination for technology. entrepreneurship is practically in the water entrepreneurship is practically in the water Upon reaching adulthood, Raj decided to take a leap and move to the U.S., eager to dive into the world of tech startups. His big break came when his first startup got accepted into Brown University’s Summer B-Lab incubator. This initial experience building a startup was in the form of an on-campus delivery platform called Fetchum. It was here that Raj created real-time matching algorithms and built multi-profile app interfaces that made it easier for students to connect with local vendors and get their deliveries quickly. But Fetchum also taught him the unpredictable nature of entrepreneurship and the importance of pivoting when challenges arise. “When my co-founder at Fetchum had to step down,” Raj explains, “I unexpectedly took on the role of CEO in addition to my CTO duties, handling everything from fundraising to operations.” This experience pushed him to quickly master time management, seek mentorship, and broaden his skills beyond tech — making him a more adaptable and well-rounded leader. In his next role, Raj got a firsthand look at how crucial solid ideation is. One of his proudest achievements was his work at Circulation, a health-tech startup that modernized non-emergency medical transportation (NEMT). As one of the organization’s early engineers, Raj was instrumental in building the core technology behind the platform, including developing its scheduling and booking systems and integrating it with ride-sharing platforms like Uber and Lyft. It was through this experience that Raj learned the value of creating a solution that truly solves a customer’s problem. “It wasn’t just about building impressive tech,” he says. “It was about understanding the real needs of patients who struggled to get to medical appointments.” The success of Circulation, which led to its acquisition by ModivCare, showed him that a smart idea only works if it addresses a genuine need and if there’s a strong, adaptable team to bring it to life. Next, at GoPuff, a fast-growing delivery startup, Raj Jaiswal used his technical skills to tackle big challenges in supply chain management. Leading a team of six, he developed systems for real-time procurement enabled by real-time demand forecasting and improved how the company processed and stored large amounts of data. By leveraging complex data migration strategies to optimize these workflows, Raj saved the company 90% in supply chain data storage costs while ensuring a reliable procurement process. How Neyon Revolutionizes E-commerce How Neyon Revolutionizes E-commerce Raj is now channeling his passion into his new startup, Neyon, which is set to shake up online shopping. With Neyon, he’s building an AI-powered search engine focused on the apparel industry, aiming to make shopping more intuitive, personalized, and efficient. Leveraging cutting-edge natural language processing, computer vision, and machine learning, Neyon will have a robust suite of tools like image recognition and conversational AI that understand what customers are looking for and help them find it faster. “I had an ‘aha’ moment while trying to find a specific coat online,” Raj recalls. “After 30 minutes of searching with no luck, I thought, ‘There has to be a better way.’ That frustration was the spark that led to Neyon.” With Neyon, Raj believes that AI can bridge the gap between what shoppers imagine and what they find online. It isn’t just about making shopping easier; it’s about solving a real customer problem — helping people discover the relevant and stylish clothes they truly want. While Neyon’s tech is powered by AI, Raj credits his success to the insights he’s gained throughout his journey. “I spotted this major customer pain point because my time at startups taught me to see the real impact of a problem and whether a solution can truly take off." He also credits Neyon’s ability to pivot and adapt to his team-building experience. “I’m confident Neyon can handle whatever the e-commerce market throws our way because I’ve built a team that’s not just packed with tech skills but also knows how to think creatively to solve problems.” Going Beyond Innovation With Raj Jaiswal Going Beyond Innovation With Raj Jaiswal But Raj’s vision goes beyond Neyon. As the NYC Ambassador for the Brown University Alumni Entrepreneurship Group, he mentors alumni, organizes events, and builds connections within the startup community. By sharing his experiences and guiding aspiring entrepreneurs, Raj is committed to shaping the next generation of tech innovators. “This way,” Raj says, “we can build a future where AI technology and innovation truly serve people, not just profits. That’s the core lesson startups need to remember.” Raj Jaiswal believes that true startup success comes from balancing tech innovation with essential entrepreneurship skills. His journey, shaped by the promise of AI and the pitfalls of overlooking the bigger picture, taught him the importance of mastering ideation, team building, and pivoting to revolutionize consumer tech. Want to learn more? Check out Neyon , and follow Raj on LinkedIn . Neyon Neyon LinkedIn LinkedIn

From Campus Delivery to E-Commerce Visionary: Raj Jaiswal’s Journey in Revolutionizing Consumer Tech

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How Mawari is Revolutionizing 3D Content Delivery

ማዋሪ የ3-ል ይዘት አቅርቦትን እንዴት እየቀየረ ነው።

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The management of Type 1 diabetes has evolved significantly with advancements in technology, particularly in the development of artificial pancreas systems. This article delves into the different types of insulin delivery systems, highlighting the latest innovations in artificial pancreas technology. These advancements are paving the way for more effective and convenient diabetes management, significantly improving the quality of life for diabetic individuals. Insulin and its Role Insulin is a hormone produced by the pancreas that allows cells to absorb glucose from the bloodstream for energy. For individuals with Type 1 diabetes, the pancreas does not produce insulin, necessitating external insulin administration to regulate blood glucose levels. Open-Loop Insulin Delivery Systems In the open-loop method, patients manually inject insulin at different times throughout the day. There are two main types of insulin used: • Basal Insulin: Also known as background insulin, it keeps blood glucose levels consistent during fasting. It is typically injected once or twice a day to provide a steady release of insulin. • Bolus Insulin: Taken at mealtimes to control blood glucose levels after eating, bolus insulin acts quickly on the body. The dosage is based on a combination of blood glucose measurements and estimates of the carbohydrate content of the upcoming meal. While effective, open-loop systems require constant monitoring and manual adjustment by the patient, which can be cumbersome and error-prone. Closed-Loop Insulin Delivery Systems: The Artificial Pancreas Closed-loop insulin delivery systems, commonly referred to as artificial pancreas systems, represent a significant advancement in diabetes management. These systems automate insulin delivery, reducing the burden on patients and providing more precise control over blood glucose levels. Components of an Artificial Pancreas: • Continuous Glucose Monitor (CGM): Continuously measures blood glucose levels and provides real-time data to the control algorithm. • Control Algorithm: Analyses the CGM data and determines the appropriate insulin dosage to maintain healthy blood glucose levels. • Insulin Pump: Delivers the calculated insulin dosage into the patient’s body. These components work together to create a closed-loop system where glucose levels are monitored, and insulin is administered automatically, mimicking the function of a healthy pancreas. Benefits of the Artificial Pancreas • Improved Glucose Control: The system continuously adjusts insulin delivery based on real-time glucose levels, reducing the risk of hyperglycaemia (high blood sugar) and hypoglycaemia (low blood sugar). • Reduced Patient Burden: Minimises the need for manual insulin injections and frequent blood glucose testing. • Enhanced Quality of Life: Allows patients to maintain more stable blood glucose levels, reducing complications and improving overall well-being. Recent Advancements and Future Directions Recent advancements in artificial pancreas technology have focused on improving the accuracy and reliability of CGMs, enhancing control algorithms, and integrating additional features such as exercise and meal recognition. Future developments may include: • Integration with Mobile Health Platforms: Allowing for remote monitoring and adjustments by healthcare providers. • Wearable and Implantable Devices: Making the system more discreet and convenient for daily use. • Advanced Algorithms: Incorporating machine learning to predict and adapt to individual patient needs more effectively. The development of artificial pancreas systems marks a significant milestone in diabetes management. By automating insulin delivery and providing real-time glucose monitoring, these systems offer improved glucose control, reduced patient burden, and enhanced quality of life. As technology continues to advance, the future holds even greater promise for innovative solutions that will further revolutionise diabetes care. In the next article, Machine Learning in Diabetes Management, we will explore various machine learning methods used in predicting and managing blood glucose levels, highlighting how these technologies are enhancing the precision of diabetes care. The management of Type 1 diabetes has evolved significantly with advancements in technology, particularly in the development of artificial pancreas systems. This article delves into the different types of insulin delivery systems, highlighting the latest innovations in artificial pancreas technology. These advancements are paving the way for more effective and convenient diabetes management, significantly improving the quality of life for diabetic individuals. Insulin and its Role Insulin and its Role Insulin is a hormone produced by the pancreas that allows cells to absorb glucose from the bloodstream for energy. For individuals with Type 1 diabetes, the pancreas does not produce insulin, necessitating external insulin administration to regulate blood glucose levels. Open-Loop Insulin Delivery Systems Open-Loop Insulin Delivery Systems In the open-loop method, patients manually inject insulin at different times throughout the day. There are two main types of insulin used: • Basal Insulin : Also known as background insulin, it keeps blood glucose levels consistent during fasting. It is typically injected once or twice a day to provide a steady release of insulin. Basal Insulin • Bolus Insulin : Taken at mealtimes to control blood glucose levels after eating, bolus insulin acts quickly on the body. The dosage is based on a combination of blood glucose measurements and estimates of the carbohydrate content of the upcoming meal. Bolus Insulin While effective, open-loop systems require constant monitoring and manual adjustment by the patient, which can be cumbersome and error-prone. Closed-Loop Insulin Delivery Systems: The Artificial Pancreas Closed-Loop Insulin Delivery Systems: The Artificial Pancreas Closed-loop insulin delivery systems, commonly referred to as artificial pancreas systems, represent a significant advancement in diabetes management. These systems automate insulin delivery, reducing the burden on patients and providing more precise control over blood glucose levels. Components of an Artificial Pancreas: Components of an Artificial Pancreas: • Continuous Glucose Monitor (CGM) : Continuously measures blood glucose levels and provides real-time data to the control algorithm. Continuous Glucose Monitor (CGM) • Control Algorithm : Analyses the CGM data and determines the appropriate insulin dosage to maintain healthy blood glucose levels. Control Algorithm • Insulin Pump : Delivers the calculated insulin dosage into the patient’s body. Insulin Pump These components work together to create a closed-loop system where glucose levels are monitored, and insulin is administered automatically, mimicking the function of a healthy pancreas. Benefits of the Artificial Pancreas Benefits of the Artificial Pancreas • Improved Glucose Control : The system continuously adjusts insulin delivery based on real-time glucose levels, reducing the risk of hyperglycaemia (high blood sugar) and hypoglycaemia (low blood sugar). Improved Glucose Control • Reduced Patient Burden : Minimises the need for manual insulin injections and frequent blood glucose testing. Reduced Patient Burden • Enhanced Quality of Life : Allows patients to maintain more stable blood glucose levels, reducing complications and improving overall well-being. Enhanced Quality of Life Recent Advancements and Future Directions Recent Advancements and Future Directions Recent advancements in artificial pancreas technology have focused on improving the accuracy and reliability of CGMs, enhancing control algorithms, and integrating additional features such as exercise and meal recognition. Future developments may include: • Integration with Mobile Health Platforms : Allowing for remote monitoring and adjustments by healthcare providers. Integration with Mobile Health Platforms • Wearable and Implantable Devices : Making the system more discreet and convenient for daily use. Wearable and Implantable Devices • Advanced Algorithms : Incorporating machine learning to predict and adapt to individual patient needs more effectively. Advanced Algorithms The development of artificial pancreas systems marks a significant milestone in diabetes management. By automating insulin delivery and providing real-time glucose monitoring, these systems offer improved glucose control, reduced patient burden, and enhanced quality of life. As technology continues to advance, the future holds even greater promise for innovative solutions that will further revolutionise diabetes care. In the next article, Machine Learning in Diabetes Management , we will explore various machine learning methods used in predicting and managing blood glucose levels, highlighting how these technologies are enhancing the precision of diabetes care. Machine Learning in Diabetes Management

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