SpyderBot Earns a 96.53 Proof of Usefulness Score by Building Real-Time GEO Analytics to Track LLM Mentions

In this interview, we sit down with Tuan Tran, the creator of SpyderBot, a cutting-edge Generative Engine Optimization (GEO) analytics platform. SpyderBot leverages a massive network of distributed bots to collect real-time data on how AI systems mention and refer to brands.

What does SpyderBot do? And why is now the time for it to exist?

SpyderBot is a GEO (Generative Engine Optimization) analytics platform that tracks how LLMs mention, rank, and refer to brands and websites. It uses 20,000+ distributed LLM-bots to collect real-time visibility data across ChatGPT, Gemini, Grok, Perplexity, and other AI systems. Now’s a good time for SpyderBot to exist because the rapid adoption of AI search engines has created a massive blind spot for brands trying to measure their visibility, share of voice, and referrals outside of traditional search.

Who does your SpyderBot serve? What’s exciting about your users and customers?

Brand owners want to know the level of brand awareness and brand health based on responses from LLMs (for end-users).

GEO (Generative Engine Optimization) agency needs tools to measure and recommend necessary website optimization measures to improve GEO metrics.

What technologies were used in the making of SpyderBot? And why did you choose the ones most essential to your techstack?

To power its massive data collection and rapid search capabilities, SpyderBot leverages industry-leading technologies like Bright Data and Algolia. Bright Data enables the platform's distributed bots to continuously gather insights without hitting rate limits, while Algolia ensures fast, accurate indexing and retrieval of critical GEO metrics.

What is the traction to date for SpyderBot? Around the web, who’s been noticing?

Although currently in its pre-launch phase and actively raising seed funding, SpyderBot has generated significant early momentum through targeted content and community engagement on LinkedIn. The platform is successfully positioning itself as a thought leader in the emerging Generative Engine Optimization space by sharing practical GEO guides and tracking real-world LLM traffic trends.

SpyderBot scored a 96.53 proof of usefulness score (view report) - how do you feel about that? Needs reassessment or is it just right?

We’re genuinely thrilled with the 96.53 Proof of Usefulness score, it’s a strong validation that the HackerNoon community sees the real-world problem we’re solving: the massive blind spot in how brands appear (or disappear) inside AI responses.

That said, we don’t consider it “just right” yet. As a pre-launch platform actively raising angel/seed funding, we view this high score as powerful early validation and strong motivation. Our goal is to push toward 980+ once we ship the live product, onboard real paying users, and deliver measurable ROI through increased LLM visibility and AI-driven referrals. We’re using this score as a benchmark to drive even harder on execution.

What excites you about this SpyderBot's potential usefulness?

High demand for AI-specific analytics (as traditional tools fail to track LLM mentions, citations, and referrals). Early pilot interest from brands seeking to measure Share of Voice in LLMs, Crawler Frequency, LLM Referrals, and Sentiment Scores. Viral potential in SEO/AEO communities, with projected 24.2%+ AI traffic visibility gains for users and clear ROI via reduced missed opportunities (20-30% in traffic and conversions).

Walk us through your most concrete evidence of usefulness. Not vanity metrics or projections - what's the one data point that proves people genuinely need what you've built?

The strongest evidence so far comes from the pain we hear directly from brands and agencies during discovery calls and early pilot discussions.

One recurring data point: Traditional analytics tools (Google Analytics, SimilarWeb, SEMrush, etc.) report zero or near-zero traffic from major LLMs, even when those same brands appear in ChatGPT, Grok, or Gemini answers for high-intent queries. Meanwhile, our internal tests and early website tracking script installations consistently detect AI crawler activity (GPTBot, Google-Extended, PerplexityBot, etc.) that existing tools completely ignore or misclassify as generic “bot” traffic.

This gap, invisible LLM mentions + undetected AI crawlers + untracked referral potential, is the concrete proof that the market needs SpyderBot. Brands are flying blind in the AI era, and that blindness is already costing them 20-30% of emerging traffic and visibility opportunities.

How do you measure genuine user adoption versus "tourists" who sign up but never return?

Since SpyderBot is pre-launch, we currently track early registration quality rather than full product retention.

We measure genuine interest through:

• Engagement depth: Whether registrants complete domain setup, install the tracking script, or request a personalized demo.
• Repeat interactions: Follow-up emails opened, replies received, and scheduled calls booked.
• Waitlist behavior: How many ask for early access or pilot participation.

Our retention story is being built right now with a deliberate focus on high-intent users (agencies and brands serious about AI visibility). Once live, we will track classic SaaS metrics: activation rate (adding first website + connecting tracking), weekly active usage (running LLM queries or reviewing dashboards), and stickiness (return frequency to monitor Share of Voice and crawler activity). We expect strong retention because the problem we solve is recurring and high-stakes, AI visibility changes weekly, and brands that ignore it lose ground fast.

If we re-score your project in 12 months, which criterion will show the biggest improvement, and what are you doing right now to make that happen?

In 12 months, we expect the largest improvement in real-world user adoption and revenue metrics (the core of Proof of Usefulness).

Right now we are laser-focused on:

• Closing angel/seed funding to accelerate full platform development and infrastructure scaling.
• Converting our growing waitlist (currently averaging ~30 new registrations per month) into closed beta users and then paying customers.
• Shipping core features: real-time Competitor Insights Dashboard (Share of Voice, Sentiment Score, Position in LLM lists) and Website Tracking Dashboard (Crawler Frequency, LLM Referrals, AI Traffic Percentage).
• Running targeted pilots with agencies and brands to generate early case studies and revenue.

By this time next year, we aim to show hundreds of active users, measurable ROI delivered (e.g., documented increases in LLM referrals and visibility scores), and recurring revenue, turning strong validation into proven product-market fit.

How Did You Hear About HackerNoon?

We’ve been following HackerNoon for years as one of the most respected platforms for deep tech and startup stories. We discovered the Proof of Usefulness Hackathon through their calls for projects that prioritize real utility over hype, exactly the philosophy that matches SpyderBot’s mission.

Our experience has been excellent. The hackathon’s focus on genuine usefulness (instead of polished pitch decks) resonates deeply with us. Submitting SpyderBot here has already generated valuable feedback and visibility within the community. We appreciate how HackerNoon creates space for builders solving hard, emerging problems like Generative Engine Optimization and AI visibility. It’s been motivating and aligned from day one.

Given that SpyderBot is currently pre-launch and gathering an average of 30 new registrations a month, what is your primary strategy for converting these early registrants into active, paying agency and brand customers once the platform goes fully live?

Our conversion strategy is built on high-touch value delivery and proven pain-point urgency:

1. Immediate onboarding value: Upon launch, every registrant receives guided setup (domain + tracking script) and an instant AI Visibility Report showing current LLM mentions and gaps.
2. Personalized pilots: We segment registrants and offer 14-30 day guided pilots for agencies and brands, with dedicated support to surface quick wins (e.g., Share of Voice improvements or undetected crawler insights).
3. Clear ROI demonstration: We will showcase concrete metrics: increased LLM referrals, higher Content Citations, and competitive gap closures.
4. Tiered pricing with agency-friendly plans: Starter for individuals, Pro/Enterprise for agencies with multi-client dashboards and white-label options.
5. Community & content nurturing: Regular webinars, GEO guides, and success stories keep engagement high while educating on the shifting AI search landscape.

The combination of immediate insights + measurable outcomes + strong product-market timing makes conversion highly predictable once the platform is live.

With a system utilizing over 20,000 distributed LLM-bots, how do you plan to scale your infrastructure and manage costs as your user base and their daily query volume grows exponentially?

We designed SpyderBot for scale from day one. Our architecture uses a distributed network of 20,000+ LLM-bots combined with intelligent caching, query batching, and prioritization.

Scaling plans include:

• Horizontal infrastructure: Leveraging cloud providers with auto-scaling groups and serverless components for query execution.
• Smart optimization: Caching frequent prompt results, using differential analysis (only re-query when content or LLM behavior changes), and rate-limit-aware scheduling.
• Cost management: Tiered usage in pricing plans, usage-based credits for heavy users, and efficiency algorithms that reduce redundant queries.
• Partnerships: Early conversations with infrastructure and data partners to optimize token/query costs at volume.

We monitor cost-per-query in real time and have clear thresholds to maintain healthy margins even at thousands of queries per second. Our goal is sustainable scaling that keeps SpyderBot affordable while delivering enterprise-grade reliability.

You've highlighted that traditional analytics tools fail to track LLM mentions. Can you share an example of a brand or agency during your early pilot phase that uncovered a massive blind spot or "missed opportunity" using SpyderBot’s Share of Voice data?

During early pilot discussions and internal testing with partner brands, one clear pattern emerged: several mid-sized SaaS and e-commerce companies discovered they had near-zero Share of Voice in high-intent prompts (e.g., “best [category] tools 2026”) despite strong traditional SEO rankings.

In one case, a productivity tool brand appeared in less than 15% of relevant LLM responses compared to competitors at 60%+. SpyderBot’s Competitor Visibility Score and Share of Voice breakdown immediately highlighted the gap, including specific prompts where they were missing and unfavorable sentiment contexts.

This “invisible” weakness represented a massive missed opportunity: potential LLM-driven referrals and brand recommendations they were completely unaware of. Using our data, they could now prioritize content optimization and structured data to close the gap. This is exactly the kind of actionable insight traditional tools cannot provide, and why SpyderBot exists.