The problem worth solving
Create client-ready proposals with extracted style tokens, evidence and screenshot review instead of generic templates.
Generative systems write fluently even when the facts are stale. A brand name or landing page screenshot is not enough: a useful AI workflow needs attributable evidence and fields shaped for its final task.
A workflow that teams can actually ship
MediaHarvester combines page discovery, markdown extraction and schema-like AI query output. Each requested datapoint can carry a source URL; brand and styleguide endpoints can then give generated deliverables a credible visual layer.
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 product marketer is generating a competitive brief and needs current feature and positioning facts with sources.
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
Start with narrowly defined datapoints, such as pricing language or integration names, and preserve sources in the final report. Use screenshots for human review where messaging or visual positioning matters.
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
Review evidence coverage, unsupported statement rate, time saved per brief and the share of generated deliverables approved without factual correction.
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 applied ai 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.