Your Opal agents are ready to take on analytics ops—but without a trusted bridge into Matomo they stall at the first dashboard request. MatoKit solves that handoff. It is the commercial-grade connector that turns Optimizely Opal into a Matomo-native copilot in days instead of quarters.
The pain we keep hearing
- Ops teams burn sprint cycles wiring bespoke API calls, reformatting data, and writing brittle prompts that fall apart when schema changes.
- Security pushes back because tokens, scopes, and segmentation rules leak into prompt text and shared documents.
- Stakeholders lose faith when agents hallucinate KPIs or mislabel funnels due to ambiguous instructions.
You don’t need another proof-of-concept script. You need a ready-to-run integration that treats analytics quality, governance, and speed-to-value as first-class requirements.
What MatoKit delivers
- Typed SDK + tool API – Prebuilt helpers for Matomo Reporting and Tracking APIs, complete with Opal discovery metadata, parameter validation, and autocomplete-ready typings.
- Governed access model – Service-to-service authentication, scoped permissions, and auditable agent activity so compliance teams can sleep at night.
- Marketing-ready playbooks – Default tool bundles for KPI health checks, consent-aware funnels, experiment baselining, and goal push events, so agents produce real impact from day one.
- Human-in-the-loop controls – Approval gates, provenance logs, and rollback hooks that keep humans in charge while agents do the heavy lifting.
- Deployment flexibility – Ship as a managed service, a Docker image that runs beside Matomo, or a Vercel/Cloudflare Worker for zero-infra rollouts.
Why it works with Opal
Optimizely Opal excels at orchestrating LLM agents, but it thrives on opinionated tools. MatoKit ships with the metadata Opal expects, making installation feel like flipping a switch:
- Connect Opal to the MatoKit tool server.
- Pick the preconfigured tool packs (insights, tracking, experimentation).
- Watch your agents fetch Matomo KPIs, compare cohorts, and queue tracking updates without touching raw tokens.
Behind the scenes, MatoKit coordinates retries, caching, rate-limit backoff, and timezone alignment so the agent’s answers stay trustworthy.
Early results
Teams piloting MatoKit report:
- 3x faster time-to-first automated insight compared to building custom Opal tools in-house.
- Zero incidents from agent overreach thanks to scoped tool policies and consent-aware defaults.
- Shorter feedback loops where analysts focus on strategy while Opal handles the grunt work of data pulls and instrumentation.
How to get it
We’re opening a limited number of onboarding slots this quarter.
- Implementation package – Two-week sprint to connect MatoKit to your Matomo instance, customise tool packs, and train your Opal operators.
- Pilot bundle – 60-day access with concierge support, success metrics, and option to transition to a managed service.
- Enterprise rollout – Hardened SLAs, high-availability deployment options, and roadmap influence.
Book a discovery call or request a demo environment by reaching out via GitHub issues or LinkedIn. If you already run Matomo and Opal, MatoKit is the fastest path from “agent curiosity” to measurable revenue wins.
Ready for your LLM agents to stop reading dashboards and start shipping insights? Let’s put MatoKit to work.