Structured decision API workbench

Return fast, structured decisions like labels, categories and extracted fields from text or images without generating long text. For product and platform teams that need fast structured decisions from text or images, convert permitted text and image inputs, label schemas, extraction fields and threshold rules into reviewed structured decisions linked to source evidence. The benefit is a testable hypothesis, measured through accepted decisions per developer hour and correction rate after deployment; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect permitted text and image inputs, label schemas, extraction fields and threshold rules, then follow this sequence: 1. Define labels, categories and typed decision fields. 2. Classify text and image inputs into approved categories. 3. Extract structured fields from supplied documents and images. 4. Return calibrated probabilities with threshold rules. 5. Emit JSON-native output for direct code consumption. Resolve uncertain cases with qualified reviewers, approve reviewed structured decisions linked to source evidence, and measure accepted decisions per developer hour and correction rate after deployment against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate decisions for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed label schema and approved field set; final decision thresholds and consequential actions remain human-approved. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve source attribution, input permissions and data rights. Named owners approve substantive decisions and deployment scope. One fixed label schema and approved field set; final decision thresholds and consequential actions remain human-approved. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One fixed label schema and approved field set; final decision thresholds and consequential actions remain human-approved. Implement one approved input format, a bounded representative case set and the first two task modules: define labels, categories and typed decision fields; classify text and image inputs into approved categories. Support the third module with operator review: extract structured fields from supplied documents and images. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Customer-owned text and image sources, label schemas and downstream systems. Cloud storage, message queues, webhook destinations and existing API clients. Start with file exchange and validate destination specifications before promising direct production integration. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Schema and label setup, Decision test bench, Batch and delivery. Use a project list for decision endpoints, a central test area for text and image inputs, and a right-hand panel for labels, fields, thresholds and comments. Let users compare model versions side by side. Display draft, review requested and approved states. Provide a client preview link with comments anchored to the relevant decision. Make the task-specific outcome reviewed structured decisions linked to source evidence visible beside its evidence, review state and value baseline.