Status
Live · day 47
A platform that evaluates how language models reference your brand, product, and industry context across multiple LLMs — surfacing strengths and risks in generative model outputs, weekly.
When a buyer asks ChatGPT "what's the best CRM for a 50-person SaaS," the answer shapes the next purchase. Today, brands have no visibility into how often they show up, in what context, or against which competitors — across GPT, Claude, Gemini, Llama, and Mistral.
SEO solved this for search. No one has solved it for generative AI. That's the gap.
Each tenant configures a brand profile and target query set. The pipeline fans queries across LLM providers, normalizes outputs, classifies mention sentiment, scores share-of-voice against competitors, and writes deltas to a tenant warehouse for dashboards and weekly digests.
Live querying every week for freshness vs. content-hash caching with weekly invalidation.
Caching. Cuts cost ~70%; weekly cadence makes invalidation deterministic and auditable.
GPT-4o-as-judge vs. a fine-tuned distilled model on labeled mentions.
Distilled classifier. 8× cheaper, deterministic, and removes the "judge sees self" bias.
Single ClickHouse cluster with row-level tenant ID vs. per-tenant Postgres schemas.
Per-tenant. Simpler isolation contract; enterprise buyers ask "where is my data" and the answer is one schema.
Three brands are running weekly evaluations. The first surprise: brands with strong SEO are sometimes weakest in LLM mention rate — the engines aren't crawling their press, they're inferring from documentation and Reddit. That mismatch is the wedge.
Next: open the platform, ship the API, and add longitudinal sentiment trends.