Screenshot of the Contract data extraction calibration service interactive demo
Screenshot of the interactive demo, on sample data

Contract data extraction calibration service

Buy automation based on meaningful task accuracy.

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For
Legal technology implementation teams
Solves
Extraction accuracy is reported without evaluating consequential field errors.
Delivers
Counsel-reviewed extraction evaluation
Built in
about 5 weeks of creation time, MVP in 6 days
Investment
$19,500 for the MVP, $50,000 for the full product
Run it
Inside your business, or as part of your offer to clients
01

What it does

Buy automation based on meaningful task accuracy.

  1. Build representative evaluation sets.
  2. Measure field-level errors.
  3. Compare reviewer-assisted workflows.
  4. Compare the reviewed result with the recorded baseline and value assumptions.
  5. Capture corrections and named-owner approval before consequential use.
  6. Export a versioned counsel-reviewed extraction evaluation with source references and unresolved questions.

What goes in, what comes out

What the customer puts in
  • Authorized redacted contracts
  • Lawyer-labeled answers

AI drafts, people review. Research evidence workspace with reviewed deliverables.

What the customer gets
  • Counsel-reviewed extraction evaluation
02

How it works

The workflow

  1. In
    Start with

    Authorized redacted contracts and lawyer-labeled answers

  2. 1

    Confirm the buyer's problem and scope

  3. 2

    Collect authorized redacted contracts and lawyer-labeled answers

  4. 3

    Then follow this sequence: 1

  5. Out
    Finish with

    Counsel-reviewed extraction evaluation

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. No unsupported model accuracy claims; rights-cleared evaluation only. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Research question and consent, Evidence comparison, Reviewed findings and experiment. Organize work by research question. Show a source library, an evidence matrix and a draft findings panel with linked quotations. Keep contradictory findings and unanswered questions visible. Allow reviewers to inspect the original context before accepting an interpretation. Make the task-specific outcome counsel-reviewed extraction evaluation visible beside its evidence, review state and value baseline.

Accounts and administration

Source provenance, participant consent where applicable, research questions, coding definitions, reviewer disagreements, citations and versioned conclusions. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Authorized matter files, firm templates and approved legal knowledge collections. Permitted research libraries, interview recording imports, citation exports and document editors. Preserve original source metadata throughout the workflow. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

03

How we build it

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

    6 days

    One buyer segment, one recurring use case; first modules: build representative evaluation sets; measure field-level errors. Manual review in the loop. Built by our AI software factory.

  3. 3

    Paid pilot

    7 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 legal technology implementation teams use it to solve "extraction accuracy is reported without evaluating consequential field errors"?
  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: Reviewer time per correct field and material error rate.
  4. Measure, then decide. Track reviewer time per correct field and material error rate; 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: No unsupported model accuracy claims; rights-cleared evaluation only. Implement one approved input format, a bounded representative case set and the first two task modules: build representative evaluation sets; measure field-level errors. Support the third module with operator review: compare reviewer-assisted workflows. 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 counsel-reviewed extraction evaluation. Retain the explicit scope boundary: No unsupported model accuracy claims; rights-cleared evaluation only.

What the build depends on. A clear research protocol, source access, citation tracking and qualified interpretation. Interview work also needs relevant participants and consent management. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: No unsupported model accuracy claims; rights-cleared evaluation only.

04

Investment

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

  1. Phase 1

    MVP

    One buyer segment, one recurring use case; first modules: build representative evaluation sets; measure field-level errors. Manual review in the loop.

    $19,500 · about 6 days of creation time

  2. Phase 2

    Paid pilot

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

    $13,000 · about 7 days of creation time

  3. Phase 3

    Full product

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

    $17,500 · about 3 weeks of creation time

Indicative total, MVP to full product$50,000about 5 weeks of creation time · start with the MVP from $19,500

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.

StageHosting and infrastructureAI usageTotal per month
MVP and paid pilotabout 3 customers$50–$100$80–$160$130–$260
Full productabout 50 customers$190–$380$880–$1,750$1,070–$2,130
05

Run it or resell it

Internally

For your own team

Legal technology implementation teams run it inside the business: authorized redacted contracts and lawyer-labeled answers in, counsel-reviewed extraction evaluation out, reviewed by your people.

For your clients

As part of your offer

Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.

Your brand, or this one

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

  • primary#275691
  • accent#c97b54
  • surface#e4eaf1
  • ink#22201e
Headings
Sora
Text
Work Sans
Voice
Precise, measured, defensible
Selling it to your own clients: the go-to-market playbook

Pricing to test

Test USD 750-3,000 for one tightly bounded research question and evidence pack. Participant recruitment, specialist review and licensed data are separately scoped. Repeat tracking can become a retainer. Prices are hypotheses. Package the initial sale as one bounded counsel-reviewed extraction evaluation. 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

Buy automation based on meaningful task accuracy. Demonstrate a concrete counsel-reviewed extraction evaluation using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Legal technology implementation teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample counsel-reviewed extraction evaluation from a small authorized input set, with a transparent calculation of reviewer time per correct field and material error rate and no promised savings.

The first 30 days

  1. Week 1: interview five legal technology implementation teams and inspect a recent example of extraction accuracy is reported without evaluating consequential field errors.
  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 reviewer time per correct field and material error rate, 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: Reviewer time per correct field and material error rate. 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

Reviewer time per correct field and material error rate; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs counsel-reviewed extraction evaluation. 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

Niche research protocols, credible researcher relationships and a rights-cleared evidence archive with consistent interpretation methods. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for legal technology implementation teams. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Research consultants, internal analysts, literature databases and general search or summarization tools. Compare this product with the buyer's present method on reviewer time per correct field and material error rate. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Researcher time, source access, participant recruitment, transcription, evidence coding, expert review and report revisions. Additional initial validation requires representative authorized sample preparation, buyer interviews, qualified domain review and bounded validation of counsel-reviewed extraction evaluation. Track cost per accepted output, including correction work, unsuccessful cases and support.

06

Safeguards

Preserve matter confidentiality, access boundaries and original evidence. Qualified professionals review legal interpretations and final client documents. No unsupported model accuracy claims; rights-cleared evaluation only. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

Get this solution built

Built for you by our AI software factory, MVP in about 6 days. Tell us about your business and how you want to run it: inside your company, or as part of what you offer your clients. We reply within one working day.

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