We engineer AI visibility, we don't just measure it.
Built in Manchester by a husband-and-wife team with 25+ years of combined experience bridging Data Science and Enterprise SaaS. As an engineering-driven duo passionate about solving highly technical problems, we approached AI search the way you would any system you intend to control: from first principles, asking what actually decides how models surface and cite a brand.
Measured by prompts
61
A share-of-voice score from running prompts in bulk. Useful for a snapshot, but probabilistic: it drifts each time you look, even when nothing has changed.
Diagnosed under the hood
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Deterministic. The same brand returns the same score. It moves only when something real changes, and points to what to change next.
▸ locked · reproducible
Why we're different
Most tools watch the output. We diagnose the cause.
Monitoring dashboards
Measure what the model said
The category that existed when we started was built around one move: fire thousands of prompts, count how often a brand appears, return a share-of-voice number.
It tells you where you stand today. But it's a lagging indicator of an output you don't control, and it can't tell you why, or what to do about it.
Deterministic diagnosis
Engineer what the model sees
We read what's actually under the hood: the structure, signals and entity footprint that decide whether AI can resolve, trust and cite a brand in the first place.
From that we produce a stable score and a prioritised, evidence-backed roadmap. Fix the cause, and the visibility follows.
First principles
How we approach the problem
01
Start from the mechanism, not the marketing
We're an AI and data team, not an SEO team that bolted GEO on top. We designed GenSight.AI around what genuinely determines how models recommend and cite, treating AI visibility as an engineering problem with knowable inputs rather than a content trend to chase. That work became our 35-node deterministic framework.
02
Diagnose deterministically, under the hood
Instead of inferring health from bulk prompt outputs, we read the underlying signals directly and score them deterministically. It's the only way to give teams a metric they can rely on: one that holds steady, and moves only when something real changes.
03
Answer the only question that matters: what do I do?
We've built data-heavy products long enough to know a score on its own is a dead end. So GenSight.AI takes teams from diagnosis to action with done-for-you, data-backed roadmaps: prioritised, specific fixes explained step by step, ready to implement.
The team
Two disciplines, one architecture
Co-founder · AI & Data Science
Tamanna Haque
AI thought leader, inventor & speaker
A data scientist who has spent her career building production, customer-facing AI from idea to live product. An award-winning voice in the field: a national-award winner, an inventor, a university guest lecturer and an internationally invited speaker on the future of AI. She brings the deep modelling instinct behind how GenSight.AI reads what AI systems actually understand about a brand.
Applied AI / MLInventorAI thought leadershipSpeaker & lecturer
Co-founder · Product & Enterprise SaaS
Jonas Almesri
Product strategist for data-heavy platforms
A product leader and owner who turns thorny, technical problems into platforms people actually adopt. He has designed, built and shipped enterprise SaaS and data products end to end across global organisations, owning everything from strategy to roll-out. With a restless curiosity for AI and emerging tech, he keeps GenSight.AI grounded in a single principle: it should be a tool you act on, not a dashboard you're left to interpret.
We could see search changing, first in our own work, then plainly in the data. The tools we found to make sense of it were all the same shape: monitoring layers measuring an output, after the fact.
Coming from data and AI, that felt backwards. If you understand the mechanism, visibility stops being something you observe and becomes something you engineer. So we built the diagnostic-and-roadmap tool we wished existed, one that shows a team exactly where the problems are and walks them through fixing each one.
