You are Not Using the Right AI/ML API: Here’s Why

Companies are increasingly using Artificial Intelligence services, especially when it comes to automating internal processes or improving their customers' experience. The strong development of AI makes it a commodity. These functionalities can be used in several fields: health, human resources, tech, etc. These AI technologies come in various forms:

• Image processing: object detection, facial recognition, text extraction, etc.
• Text analysis: keyword extraction, sentiment analysis, etc.
• Machine translation: customer relationships, document translation, etc.
• Audio transcription: phone call analysis, video subtitling, etc.
• Prediction: sales prediction, production optimization, etc.

The big players in the cloud market (Amazon Web Services, Microsoft Azure or Google Cloud) offer solutions that provide access to this type of service, but there are also smaller providers that are already competing with them: Mindee, Dataleon, Deepgram, AssemblyAI, Rev.AI, Speechmatics, Lettria, etc. Because of their specialization, they often manage to offer very powerful solutions.

In the AI sector, there are many different suppliers offering various solutions. As a result, it is complicated to correctly choose the best solution for your needs, as performance depends on the user's data. A transcription of audio into text will not necessarily be the same using Google Cloud as using AssemblyAI, for example. Indeed, depending on the language used, one provider might generate a better transcription in Spanish than the other one. 

In order to demonstrate this, here are some more in-depth analyses demonstrating the difference in performance of several providers with concrete examples of data for: vision, OCR, prediction, object detection, speech-to-text and facial recognition. An AI comparator also provides quick access to public benchmarks between vendors based on public data: compare.edenai.run. 

To address the complexity of this market, Eden AI is developing an API that allows users to access the AI engines of their choice. The company combines these engines and provides a single entry point to compare, test and use them. Offering different levels of performance, price, and response quality depending on the data processed, this is where Eden AI stands out thanks to its unique, multi-vendor approach. 

The company's ambition is to democratize the use of AI to make it easily accessible to all developers. To achieve this promise, Eden AI focuses on three main axes:

→ The company positions itself as an aggregator operating a standardization: it gives the possibility to use several engines at the same time, as well as replacing and combining them with the greatest simplicity. Eden AI also takes care of centralizing costs to allow users to save time.

→ It also develops the "Genius" feature: an automatic and dynamic recommendation system based on each user's data. Eden AI redirects to the best solution and engines adapted to the user.

→ Finally, the user will probably combine several AI engines for different uses. The "Pipelines" tool allows to directly create a data processing flow allowing to combine AI solutions instantly between them.

To succeed in its project, Eden AI recently raised $1.6 million in a round of financing supported by the French accelerator 50 Partners, but also by renowned business angels: Olivier Pomel (Datadog), Nicolas Dessaigne (Algolia), Sébastien Pahl (Docker), Julien Lemoine (Algolia), Benjamin Fabre (DataDome), Laurent Letourmy (Ysance), Jean-Baptiste Aviat (Sqreen), Georges Gomes (Div Riots) and Thomas Grange (Botify):

Eden AI is therefore developing in a booming market, with the ambition to make AI accessible to all developers. To do this, the company wants to expand its team with talented recruits: edenai.co/careers.