The problem worth solving
A disciplined public-data workflow for tracking messaging and product changes without building a surveillance machine.
Market plans often rely on fragmented notes, old screenshots and untraceable assumptions. Structured public website evidence cannot replace strategy, but it can make hypotheses easier to challenge.
A workflow that teams can actually ship
Use brand enrichment for company identity, crawl or search to find relevant pages, and AI query for explicitly requested fields such as positioning or pricing language. The result becomes research input rather than a magical decision engine.
For this pattern the practical entrypoint is `POST /v1/brand/ai/query`, combined with whichever workflow endpoint or presentation layer the product requires.
Workflow map
Scenario in practice
A growth team is evaluating target accounts and competitor positioning before committing budget to an outreach experiment.
Start with one observable workflow, keep source URLs available during review and expand only after the result is reliably useful.
Try the capability
POST /v1/brand/ai/query Local API key:mh-localhost-dev-key
POST /v1/brand/ai/queryImplementation notes
Define the decision before collecting data, retain the page URLs behind conclusions and combine automated extraction with a human review sample. Sensitive personal targeting is outside the responsible use case.
When the result affects a customer-facing experience, add an approval or override path rather than treating extraction output as immutable truth.
Signals to monitor
Evaluate research turnaround, evidence completeness, analyst corrections and whether experiments based on the enriched context outperform an unresearched baseline.
Metrics turn a promising prototype into a maintainable product capability and make regressions easier to catch.
Try it with MediaHarvester
Open the local playground, choose the feature matching `POST /v1/brand/ai/query` and test with a public or authorized target.
The local project includes API, documentation, SDK, CLI, MCP and no-code surfaces so the same idea can move from exploration into implementation.
FAQ
Questions teams ask before implementing this workflow
What does this market research workflow return?
It uses POST /v1/brand/ai/query and related MediaHarvester surfaces to return structured context together with metadata appropriate to the workflow.
Can I test this locally?
Yes. Run the local service at http://127.0.0.1:8013 and send X-API-Key: mh-localhost-dev-key to protected API routes.
Does it work with private or blocked pages?
The platform is designed for publicly accessible or authorized sources. Verification, login, permission and robots restrictions are reported rather than bypassed.
Can this be automated?
The same API surfaces are available through CLI, Python and TypeScript SDKs, MCP tools and starter no-code integration templates.
How do I keep the result current?
Use cache freshness controls such as maxAgeMs where exposed, and schedule refreshes in a production worker only as frequently as the business case needs.