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Solutions · AI models

Make your AI smarter
with your product intelligence.

Your AI is only as useful as the context it gets. Connect your AI to Countly through MCP and it can use your product intelligence as context: behavior, audiences, user context and product signals, for the outputs, recommendations and decisions you build. Governed at ingestion, and on infrastructure you control.

Key questions

Key challenges with AI on product data

The three questions we hear most often in this space, and how Countly answers them on data you keep.

How do we give models real behavior?

Cohorts, funnels, event properties and segments turn raw interactions into clean, connected, machine-readable data.

How do we feed copilots live insight?

Live context through MCP: what users do, when and how often, without exporting raw data.

How do we stay compliant?

Your data, your control. Transparent AI integration, no raw data exposure, support for GDPR and HIPAA requirements.

How Countly helps

What makes Countly data AI-ready

One platform covers the whole loop: Capture, Analyze, Act, Adapt. The insight and the action stay in the same place.

See the platform
  1. 01

    Capture

    Every key event, action and interaction across your product, governed at ingestion.

  2. 02

    Structure

    Cohorts, funnels, properties and segments as machine-readable context.

  3. 03

    Deliver

    The same context to your own models and agents through MCP; Cee for your team inside Countly.

What each team can do

One context,
every team

Dev teams

Validate SDK integrations and debug issues with real product context.

Marketing

Ground marketing decisions in product data.

Product

Personalize experiences, experiments and Journeys.

Data science

Analyze metrics, forecast and detect anomalies.

Data ownership

A data layer for AI,
on infrastructure you control

Countly turns product analytics into a secure, structured data layer for AI, that you can self-host on your own infrastructure. It connects models to real user behavior, giving them governed, contextual input they can learn from, not just raw logs or isolated events. Feed your models, copilots and agents live context, and keep your product data within the infrastructure you operate.

Explore data privacy
Features teams use most here
Cee AI agentCohortsFunnelsDrillContext for your AI through MCP

FAQ

Questions about
AI models

Straight answers for this space. For anything else, talk to us.

Can we connect our own AI to Countly?

Yes. Connect your model, copilot or agent to Countly through MCP so it can use your product intelligence as context: behavior, audiences, user context and product signals.

Which platforms can Countly capture data from?

Web, mobile, desktop, connected products and server-side systems, through Countly SDKs and APIs for custom ingestion, so your AI works from the whole product, not one channel.

Does AI stay inside our infrastructure when we self-host?

Cee’s product agents can run fully inside your infrastructure when configured. The Documentation Agent, the Countly AI Gateway (EU) and third-party AI providers run outside. Your own models connected through MCP run wherever you run them.

What is the difference between Cee and MCP?

Cee is the AI analyst inside Countly: ask in plain language, get the cohort, funnel or query back with evidence. MCP connects your own AI to Countly. They’re separate: Cee works on your data inside Countly, MCP gives your AI the same context.

Solutions

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The same platform, seen from the next industry or use case over.

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Solutions

Your industry.
Your data. One platform.

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