NARIRO CORETEX
No-code machine learning and data work
Connect your data, shape it by dragging, test what you believe, train models and let an agent do the repetitive part — all in one web app, with no notebooks to maintain.
See how AI answers describe your brand — and what to fix.
Plays here. Or watch it on YouTube ↗
Buyers now ask an assistant instead of a search engine. Prism tracks what Google AI Overviews, Gemini, Perplexity and Claude say about you, who they name instead, and which missing fact is costing you the mention.
Search used to end at a list of links you could rank in. More and more it ends at one answer, written by a model, naming three or four brands. Prism tells you whether you are one of them.
Keep a library of the prompts that matter — the ones buyers actually type — and run them nightly against each engine. Branded prompts are detected for you, so you can see where you win on your own name and where you do not.
One visibility number, shown the same way on every page, built from how often you are named, where you appear among the brands named, and how you are described. No separate figure on the dashboard and in the report.
Being fourth in a list of eleven insurers is a fact. Being nineteenth because the answer had nineteen bullet points is not. Prism counts your position among the brands in the answer, which is the number you can act on.
Give Prism the handful of facts your product team has confirmed — a settlement ratio, a cooling-off window, what the cover excludes. Any answer that contradicts one is flagged for review, with the source that got it wrong.
Every run shows its estimated cost against the engines' pricing, and the budget page shows where it went. No surprise invoice at the end of the month.
No-code machine learning and data work
Connect your data, shape it by dragging, test what you believe, train models and let an agent do the repetitive part — all in one web app, with no notebooks to maintain.
Start on the platform today, or talk to our team about a custom engagement.