Build with GetGenAI

Why builders choose GetGenAI

Label compliance, streamlined for the AI stack.

Built for label artwork

Handle any input: press-ready PDFs, AI files, PNGs, photographed packaging, or structured JSON. Parse panels (front, Supplement Facts, claims, warnings) and extract elements in one call.

Checks that know label rules

FDA Supplement Facts format, DSHEA disclaimers, structure/function vs. disease claims, allergen warnings, net quantity, serving size. Versioned rulesets auto-update weekly so you never rebuild to stay compliant.

Pipeline you can compose

Call extract, suggest, inspect, and aggregate as separate endpoints or chain them – whatever your workflow needs. Build once, compose differently for each use case (Supplement Facts only, brand review, full artwork audit).

Three ways to build

Pick the layer that matches how you build. Or use all three.

API

Programmatic label review

Send artwork (PDF, AI, PNG, or structured text), get parsed panels and findings back. REST endpoints, OpenAPI spec, and SDKs in TypeScript and Python. Rate-limited per workspace, audit-logged by default.

curl -X POST "https://api.getgen.ai/api/inspect" \
  -H "Authorization: Bearer $GETGEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "inspector_id": 123,
    "objects": [
      {
        "type": "image",
        "content": "https://cdn.example.com/vitd3-press.png"
      }
    ],
    "ctx": {
      "product_type": "supplement"
    }
  }'

Read the API docs →

MCP

Agent-discoverable label inspection

Run our MCP server in your agent stack. Claude, Cursor, and any MCP-compatible client get a typed toolset: parse labels, suggest inspectors, run reviews, aggregate findings. Agents compose the pipeline however they need.

{
  "mcpServers": {
    "getgenai": {
      "url": "https://mcp.getgen.ai",
      "auth": { 
        "type": "bearer", 
        "token": "$GETGEN_API_KEY" 
      }
    }
  }
}

See available tools →

SKILLS

Agent-ready label review skills

Give your AI assistant a reusable review workflow for supplement labels. The skills pack includes instructions for FDA labeling checks, structure/function claims, DSHEA disclaimers, Supplement Facts panels, ingredients, warnings, and review handoff.

getgenai-label-review/
  SKILL.md
  workflows/
    review-supplement-label.md
    check-structure-function-claims.md
    verify-dshea-disclaimer.md
  examples/
    supplement-label.pdf
    expected-findings.md
  mcp/
    getgenai-tools.md

Browse skills →

The Extract → Review → Feedback pipeline

One label, three inspection stages. Compose them however you need.

Flowchart showing doc-parse processing input documents into front panel, supplement facts, claims, ingredients, and warnings.

Flowchart showing parsed panels processed by get_inspectors to select inspectors, then inspected to produce raw findings.

Extract

Send artwork (PDF, AI file, photo, or structured text) to doc-parse. Identifies all panels and elements (front panel, Supplement Facts, claims, ingredients, warnings, etc.).

Review

Call suggest_inspectors to let the platform recommend relevant rules (FDA Supplement Facts, DSHEA disclaimer, structure/function claims, allergen warnings, etc.) or pass your own inspector list to inspect. Get raw findings with location and severity.

Feedback

Aggregate findings into a clean, human-readable report grouped by panel, severity, or inspector. Export as PDF, JSON, or feed directly into your design tool or Jira.

Supplements is our example here; the same Extract → Review → Feedback pipeline runs for food, cosmetics, cannabis, pharma, and chemical labels — see /industries.

Why builders choose GetGenAI

Label compliance, streamlined for the AI stack.

API reference

Post /api/doc_pase, api/inspect, /api/aggregate, and custom checks.

/api

MCP server docs

Tools: parse_document, suggest_inspectors, inspect, provide_feedback etc.

/mcp

Skills library

getgenai-supplement-labels, Supplement facts panel, DSHEA, structure/function claims

/skills

Status & changelog

Service uptime, API updates, breaking changes, and release notes.

status.getgen.ai

Got questions?
We’ve got answers

Have questions about our services?
Explore our detailed FAQ section
for in-depth answers and insights

How does GetGenAI actually work? Guide me step by step.

You create as usual — ads, landing pages, or copy. If something breaks a rule or could be improved, GetGenAI instantly highlights it, explains why, and suggests how to fix it. You can accept the change with one click, and the text updates automatically.

What kind of content does GetGenAI review?

Everything from digital ads, landing pages, and packaging to influencer content and partner collateral — in text, image, and video formats.

Can we customize GetGenAI for our company’s internal guidelines?

Yes! GetGenAI can be trained on company-specific guidelines, brand standards, and custom rules. Clients have full access to add and manage their inspectors.

Does GetGenAI integrate with my existing workflow?

Yes, GetGenAI seamlessly integrates with Google Docs, Jira, Microsoft Word, CMS platforms, and other workflow tools.

How quickly can GetGenAI be implemented?

Most teams are up and running within days. Full integration—like customizing reports, roles, and workflows—typically takes 2–5 weeks.

How does GetGenAI handle regulatory updates?

GetGenAI automatically checks and updates regulatory checklists in real-time, ensuring your content always meets the latest legal, regulatory, and policy requirements.

Can GetGenAI check content against multiple rulesets at once?

Yes! You can apply multiple compliance layers—from brand rules to regulatory policies—simultaneously.

Does GetGenAI guarantee 100% accuracy?

While GetGenAI dramatically reduces compliance errors, human oversight is recommended for high-stakes content.

Why builders choose GetGenAI

Built for label artwork

Checks that know label rules

Pipeline you can compose

Three ways to build

Programmatic label review

Agent-discoverable label inspection

Agent-ready label review skills

The Extract → Review → Feedback pipeline

Why builders choose GetGenAI

API reference

MCP server docs

Skills library

Status & changelog

Ship label-compliant from day one

Solutions

Industries