Structured decision API workbench

For

Product and platform teams that need fast structured decisions from text or images

Solves

Teams rent several tools to classify, extract and threshold decisions, then glue the outputs together and cannot own the workflow.

Delivers

Reviewed structured decisions linked to source evidence

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Return fast, structured decisions like labels, categories and extracted fields from text or images without generating long text.

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.
6. Serve text and image decisions through one API endpoint.
7. Expose OpenAI-compatible endpoints for migration.
8. Run high-volume jobs through a batch API.
9. Provide TypeScript and Python SDKs and a CLI.
10. Build models from Google Sheets or a no-code builder.
11. Test models through a forms-based interface.
12. Manage data projects in a dashboard.
13. Run automated data analysis with configurable parameters.
14. Support real-time team collaboration.
15. Produce reports and exports.
16. Offer a free tier or trial.
17. Support bring-your-own models.
18. Run CPU and edge inference without dedicated GPUs.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewed structured decision set with source references and unresolved questions.

Everything these tools do, in one app

• Structured decision output Returns labels, categories, structured fields, or typed decisions instead of freeform text.Found in Milliseconds.ai, Jev, ZeroGPU
• Text and image classification Classifies text and image inputs into categories or labels.Found in Milliseconds.ai, ZeroGPU
• Structured field extraction Extracts structured fields from input data.Found in Milliseconds.ai, ZeroGPU, Promptrepo
• Calibrated probabilities Provides probabilities that developers can threshold and act on.Found in Jev
• JSON-native output Outputs structured JSON that code can consume without parsing natural language.Found in Jev
• Single API endpoint Accepts text and image inputs and returns decisions through one endpoint.Found in Milliseconds.ai
• OpenAI-compatible API Offers API endpoints compatible with OpenAI's API for easy migration.Found in ZeroGPU
• Batch processing API Supports high-volume jobs through a batch API.Found in ZeroGPU
• SDKs and CLI Provides TypeScript/Python SDKs and a CLI for integration and testing.Found in Milliseconds.ai
• No-code model builder Builds AI models for classification, extraction, and generation using Google Sheets or a no-code builder.Found in Promptrepo
• Forms-based testing interface Tests AI models with an easy-to-use forms-based user interface.Found in Promptrepo
• Dashboard for data projects Manages and visualizes data projects through an intuitive dashboard.Found in l1m.io
• Automated data analysis Performs automated data analysis with customizable parameters.Found in l1m.io
• Real-time collaboration Enables team-based projects with real-time collaboration tools.Found in l1m.io
• Reporting and export Provides comprehensive reporting and export options.Found in l1m.io
• Free tier or trial Offers free usage options or trials to test the platform.Found in Milliseconds.ai, ZeroGPU, l1m.io and 1 more
• Bring your own models Supports bringing your own models for production customers.Found in ZeroGPU
• Edge-optimized CPU inference Runs models on CPU and edge hosts without dedicated GPU provisioning.Found in ZeroGPU

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted text and image inputs, label schemas, extraction fields and threshold rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted text and image inputs

4. 3

   Label schemas

5. 4

   Extraction fields and threshold rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed structured decisions linked to source evidence

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, schema versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   7 days

   One buyer segment, one recurring use case; first modules: define labels, categories and typed decision fields; classify text and image inputs into approved categories. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product and platform teams that need fast structured decisions from text or images use it to solve "teams rent several tools to classify, extract and threshold decisions, then glue the outputs together and cannot own the workflow"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted decisions per developer hour and correction rate after deployment.
4. Measure, then decide. Track accepted decisions per developer hour and correction rate after deployment; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed structured decisions linked to source evidence. Retain the explicit scope boundary: One fixed label schema and approved field set; final decision thresholds and consequential actions remain human-approved.

What the build depends on. Input upload and preview, asynchronous decision jobs, editable version history, reviewer access and tested JSON export formats. High-fidelity production requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed label schema and approved field set; final decision thresholds and consequential actions remain human-approved.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined decision package. Offer a monthly production allowance after repeat demand. Quote complex multi-tenant or edge deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed structured decisions linked to source evidence. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Return fast, structured decisions like labels, categories and extracted fields from text or images without generating long text. Demonstrate a concrete reviewed structured decisions linked to source evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and platform teams that need fast structured decisions from text or images professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed structured decisions linked to source evidence from a small authorized input set, with a transparent calculation of accepted decisions per developer hour and correction rate after deployment and no promised savings.

The first 30 days

1. Week 1: interview five product and platform teams that need fast structured decisions from text or images and inspect a recent example of teams rent several tools to classify, extract and threshold decisions, then glue the outputs together and cannot own the workflow.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted decisions per developer hour and correction rate after deployment, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted decisions per developer hour and correction rate after deployment. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted decisions per developer hour and correction rate after deployment; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed structured decisions linked to source evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved label schemas, extraction fields and review examples, together with reliable delivery for a narrow decision niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and platform teams that need fast structured decisions from text or images. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Milliseconds.ai, Jev, ZeroGPU, l1m.io and Promptrepo, plus generic generation tools and in-house scripts. Compare this product with the buyer's present method on accepted decisions per developer hour and correction rate after deployment. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Inference attempts, image processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed structured decisions linked to source evidence. Track cost per accepted decision, including correction work, unsuccessful cases and support.

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.