Generative AI

Power Generative AI

Enhance AI generation with real-time brand context, product facts, pricing pages, images and styleguide tokens.

AI Query Brand Styleguide Screenshot

Query a website with AI Read docs

01 Custom datapoint extraction

02 Evidence URLs

03 Brand and styleguide enrichment

04 Product and screenshot context

How it works

From input to useful output in three steps

POST /v1/brand/ai/query

Fetch

Collect brand and facts

Retrieve brand data, styleguide tokens and specific datapoints from the public website.

POST /v1/brand/ai/query

Prompt

Inject context into generation

Add colors, fonts, description, product claims and source URLs to the system prompt.

brand + styleguide + evidence

Create

Generate with reviewable sources

Produce content, UI direction or reports while preserving the source material used.

AI output + sources

Example workflow

Brand context to AI output

Prompt asks

Create a landing-page section for this brand and use current product facts.

MediaHarvester provides

Company description, brand colors, fonts, product facts, screenshots and source URLs.

AI generates

Copy, layout direction and visual tokens that match the company instead of a generic template.

{
  "domain": "airbnb.com",
  "data_to_extract": [{
    "datapoint_name": "positioning",
    "datapoint_description": "Summarize the brand positioning."
  }]
}

Built for

Teams that can use this immediately

AI content tools

Challenge: Generated content sounds generic.

Solution: Use real company context in prompts.

Impact: Drafts need fewer brand edits.

Design generators

Challenge: Layouts ignore brand visuals.

Solution: Add styleguide colors and fonts.

Impact: Generated UI starts closer to the brand.

Sales automation

Challenge: Personalization requires manual research.

Solution: Query the prospect website first.

Impact: Outreach feels more specific.

Implementation FAQ

Questions teams ask before building Power Generative AI

12 answers

What does Power Generative AI help me build?

It turns Website data extraction for AI content, reports and personalization. into a repeatable workflow. The first practical outcome is custom datapoint extraction.

Which API should I call first?

Start with POST /v1/brand/ai/query. The workflow section shows the supporting endpoints to add when you need more context, evidence or visual assets.

What input do I need from my user?

The smallest useful input is shown in the example workflow: Create a landing-page section for this brand and use current product facts. Ask only for the domain, URL, email, ticker or descriptor that the selected endpoint actually needs.

What should I store from the response?

Store the normalized result together with source URLs, confidence or access state, and the time it was fetched. This keeps later refreshes and human review explainable.

Can I use this from a backend, CLI or AI agent?

Yes. The REST endpoint, TypeScript and Python SDKs, CLI and MCP tools use the same local API contract. No-code templates are available for business workflows.

How do I keep the result current?

Use maxAgeMs where the endpoint supports it, or schedule the relevant crawl or workflow. Refresh facts that can change and cache stable assets such as logos more aggressively.

What happens when the website blocks access?

The API reports blocked, verification_required, login_required, robots_disallowed or permission_required instead of treating an empty page as a successful result.

How should I handle missing fields?

Keep the UI usable with optional fields, sensible placeholders and manual overrides. A missing logo, address or classification should not prevent the rest of the workflow from completing.

Who is this workflow designed for?

The examples focus on ai content tools, design generators, sales automation, but the API response is general enough to use in custom applications.

Can I review the result before publishing it?

Yes. Show extracted values, source URLs and generated visual previews in an approval step before changing customer-facing content or records.

Is batch processing supported?

Use the batch, crawl or workflow endpoint shown for the capability when available. For smaller integrations, process a controlled list and record individual failures rather than failing the whole job.

How do I try the complete example locally?

Open the linked playground from this page or call POST /v1/brand/ai/query at http://127.0.0.1:8013 with X-API-Key: mh-localhost-dev-key. The example payload on this page is ready to adapt.

Related use cases

Explore adjacent workflows

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