RankSage measures how AI answer engines describe your brand, and joins that to the rest of your search data at the page level.

We run tracked prompts against ten answer engines — ChatGPT, Claude, Gemini, Perplexity, OpenAI Web Search, DeepSeek, Grok, Qwen, Mistral and Meta AI — and detect where Google's AI Overviews cite you. Those results are joined to Google Analytics, Search Console, competitor crawls, and first-party behavioural data collected by a 5KB tracker. Every one of them resolves to the same row: the URL.

The join is the product. Traffic is grouped by channel, rankings by query, behaviour by session, technical audits by issue type. No two of those share a key. The page is the one thing they all have in common, and it is what turns four disconnected dashboards into a single ranked list of what to change.

We are deliberate about what the platform will not claim.

A check that could not run says so. It never collapses into a zero or a pass.

Citation rates are reported with confidence intervals, not a single figure implying precision we don't have.

A crawler identifying itself as GPTBot is verified by forward-confirmed reverse DNS before it counts as one.

Scores are derived from stored evidence. Answer text and cited sources are kept, and the score is computed from them afterwards.

Seventeen read-only tools expose customer data over the Model Context Protocol — an open standard for connecting AI assistants to external data — so the data leaves the platform as easily as it enters.

RankSage is built by a small independent team in Nashik, India. We publish here on answer engine optimisation, context engineering for production systems, and the measurement problems we run into while building the product.