What CompetLab is

What it is

CompetLab is a B2B competitive-intelligence platform. It watches how the AI answer engines describe and recommend your company against its competitors, tracks the broader competitive landscape around that, and synthesizes both into a monthly Strategic Briefing — what changed, what it means, and what to do next.

The reason it exists is a shift in how buyers research. More of them ask an AI assistant instead of scrolling a results page, and the answer they get names some vendors and not others. CompetLab measures your standing in those answers — across ChatGPT, Claude, and Gemini — and connects it to the rest of the competitive picture: how rivals position and price, what they publish, what their stack and trust signals look like, and where the market is heading.

The same intelligence reaches you three ways — the dashboards in the app, your AI tools, and your own code — because it’s one dataset underneath. This page is the front door: what CompetLab tracks, the metric at the center of it, and the two ways to put it to work. For the vocabulary — projects, competitors, dimensions, alerts — read Core concepts.

What it tracks

CompetLab tracks 13 dimensions of your competitive position, in two tiers: five it monitors continuously and eight it researches for the monthly briefing. A dimension is one angle on a competitor set, scoped to a project.

The five monitored dimensions refresh on a schedule and can raise alerts: AI Visibility (how the AI engines mention and recommend you), Positioning, Pricing Intelligence, Content Intelligence, and Tech & Trust. The other eight — including Agent Adoption, Customer Voice, Funding & Capital, and Landscape — are researched for the Strategic Briefing rather than tracked live, because they move less often and read better as periodic research than as a live feed.

This is a summary; the full registry, with what each of the 13 covers and why it sits in its tier, is on the Core concepts page. That page is the canonical list — this one just names the shape.

The AI Visibility Score

The AI Visibility Score is CompetLab’s flagship metric: a single 0–100 measure (higher is better) of how visible and how often recommended your company is when people ask the AI answer engines about your space. It’s the number the rest of the platform orbits.

Under the hood it reflects how frequently and how prominently the engines mention you versus your competitors — mention rate, average rank, and per-engine breakdowns — rolled into one comparable figure so you can watch it move over time and against rivals. It answers a plain question: when someone asks AI about products like yours, do you come up, and how favorably?

We don’t invent the number here. Exactly how it’s collected and computed — which engines, which prompts, how the pieces combine — is on the Methodology page, which also names the limits of the approach.

The two ways in

There are two ways to put CompetLab to work, on top of the dashboards: in your AI tools and in your code. Both read the same backend — you pick the surface that matches who’s asking.

In your AI tools — connect an AI assistant to your data with the hosted MCP server, or run prebuilt Agent Skills that turn that data into finished deliverables. In your code — call the REST API over plain HTTP, or use the typed TypeScript SDK. Plus six free scan tools you can run against any URL without a paid plan.

If you’re wiring CompetLab into an assistant, start with the MCP server. If you’re building it into your own backend, start with the REST API.

Wiring it into your workflow. CompetLab is built to fold into what you already run, not just be a dashboard you visit: alerts POST to a webhook you own so changes land in your own tooling, a scheduled job can pull any dimension through the REST API or SDK on your own cadence, the MCP server drops the same data into your AI assistants, and it composes with automation platforms like n8n and the pipelines you already run.

Who it is for

CompetLab is built for people who need to know their competitive standing in the AI era without running the research by hand. In practice that’s founders, product marketers, competitive-intelligence and marketing leaders, and the sales and product teams they hand findings to.

The monitored dimensions and the AI Visibility Score give a marketing or CI lead a live read on where the company sits across the AI answer engines and the wider landscape. The Strategic Briefing turns that read into a monthly set of recommended actions, ranked by impact and effort — the kind of thing a founder or CMO acts on directly. Sales and product teams tend to consume the derived outputs: battlecards, competitor dossiers, and the signals that feed a call or a roadmap. Because the same data is reachable from an AI assistant and from code, technical teams can fold it into their own tools rather than opening one more dashboard.

Where to go next

Three pages take you from here: the quickstart gets you a first result, Core concepts gives you the vocabulary, and Methodology explains how the numbers are made.