Document data extraction and validation workspace

For

Operations teams processing high volumes of invoices, forms and scanned documents

Solves

Key data is trapped in mixed-format documents and manual entry causes errors, delays and rework.

Delivers

Reviewer-approved extracted records linked to source pages

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

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

Reduce manual document handling while keeping a named reviewer accountable for every extracted record.

1. Extract key fields from supplied documents.
2. Classify documents by type.
3. Accept PDFs, images, scans and structured files.
4. Let users define which fields to extract.
5. Automate document workflows and approvals.
6. Connect to ERP, CRM and storage systems.
7. Apply validation rules to extracted data.
8. Process varying document volumes.
9. Refine extraction accuracy from reviewer corrections.
10. Run accuracy experiments before deployment.
11. Validate relationships between fields, such as totals matching.
12. Send webhooks for failed validations and human-in-the-loop steps.
13. Handle messy real-world documents.
14. Recognize layout elements such as paragraphs, headings and lists.
15. Extract tables with structure and content.
16. Detect code blocks and preserve indentation.
17. Link equations and figures to their captions.
18. Compare the reviewed result with the recorded baseline and value assumptions.
19. Capture corrections and named-owner approval before consequential use.
20. Export a versioned reviewer-approved extracted records linked to source pages with source references and unresolved questions.

Everything these tools do, in one app

• Document data extraction Automatically pulls key information from documents.Found in docTI Custom Document Processing, SenseTask, Invofox 2.0 and 1 more
• Document classification Categorizes documents by type.Found in docTI Custom Document Processing, SenseTask, Invofox 2.0
• Multiple format support Handles various document formats like PDFs, images, and scanned files.Found in docTI Custom Document Processing, SenseTask, SmolDocling
• Customizable field extraction Lets users specify which fields to extract from documents.Found in SenseTask
• Workflow automation Automates document-related workflows and approvals.Found in SenseTask
• System integration Connects with existing business systems like ERP and CRM.Found in docTI Custom Document Processing, SenseTask
• Data validation Checks extracted data for errors and applies validation rules.Found in docTI Custom Document Processing, Invofox 2.0
• Scalable processing Handles varying document volumes efficiently.Found in docTI Custom Document Processing
• Continuous learning Improves accuracy over time through AI refinement.Found in SenseTask
• Accuracy experimentation Provides a workflow to test and measure extraction accuracy before deployment.Found in Invofox 2.0
• Cross-field validation Validates relationships between fields, such as totals matching.Found in Invofox 2.0
• Webhook support Enables integration hooks for handling failed validations and human-in-the-loop workflows.Found in Invofox 2.0
• High-variance input handling Processes messy, real-world documents effectively.Found in Invofox 2.0
• Layout recognition Identifies page layout elements like paragraphs, headings, and lists.Found in SmolDocling
• Table extraction Extracts tables with their structure and content.Found in SmolDocling
• Code block detection Detects and formats code blocks, preserving indentation.Found in SmolDocling
• Equation and figure handling Handles equations and figures, linking captions appropriately.Found in SmolDocling

What goes in, what comes out

The workflow

1. In
   Start with

   Supplied PDFs, images, scans and structured files

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect supplied PDFs

4. 3

   Images

5. 4

   Scans and structured files

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved extracted records linked to source pages

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs 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. Final posting, payment and compliance decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Document intake and classification, Extraction review and validation, Export and system handoff. Use a queue view for incoming documents, a large central viewer showing the source page beside extracted fields, and a right-hand panel for validation rules, confidence and comments. Let users compare extracted values against source regions. Display draft, changes requested and approved states. Provide a webhook and export log view for failed validations and human-in-the-loop steps. Make the task-specific outcome reviewer-approved extracted records linked to source pages visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, document versions, reviewer comments, approval states, usage allowances, processing 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

Buyer-owned document repositories, ERP and CRM systems, cloud storage and webhook endpoints. Start with file exchange and validate destination specifications before promising direct posting. 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

   6 days

   One buyer segment, one recurring use case; first modules: extract key fields from supplied documents; classify documents by type. 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 operations teams processing high volumes of invoices, forms and scanned documents use it to solve "key data is trapped in mixed-format documents and manual entry causes errors, delays and rework"?
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 records per operator hour and corrections after downstream posting.
4. Measure, then decide. Track accepted records per operator hour and corrections after downstream posting; 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 document family and one approved export destination; final posting and compliance checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: extract key fields from supplied documents; classify documents by type. Support the remaining modules with operator review: apply validation rules to extracted data; validate relationships between fields, such as totals matching. 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 reviewer-approved extracted records linked to source pages. Retain the explicit scope boundary: One document family and one approved export destination; final posting and compliance checks remain human.

What the build depends on. Document upload and preview, asynchronous extraction jobs, editable version history, reviewer access and tested export formats. High-fidelity extraction requires specialist document QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One document family and one approved export destination; final posting and compliance checks remain human.

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 document family. Offer a monthly processing allowance after repeat demand. Quote complex integrations or specialist document types separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved extracted records linked to source pages. 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

Reduce manual document handling while keeping a named reviewer accountable for every extracted record. Demonstrate a concrete reviewer-approved extracted records linked to source pages using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations teams processing high volumes of invoices, forms and scanned documents professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved extracted records linked to source pages from a small authorized input set, with a transparent calculation of accepted records per operator hour and corrections after downstream posting and no promised savings.

The first 30 days

1. Week 1: interview five operations teams processing high volumes of invoices, forms and scanned documents and inspect a recent example of key data trapped in mixed-format documents and manual entry causing errors, delays and rework.
2. Week 2: prepare a consented or synthetic demonstration of the stated task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted records per operator hour and corrections after downstream posting, 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 records per operator hour and corrections after downstream posting. 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 records per operator hour and corrections after downstream posting; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved extracted records linked to source pages. 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 field mappings, validation rules and review examples, together with reliable delivery for a narrow document niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for operations teams processing high volumes of invoices, forms and scanned documents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

docTI Custom Document Processing, SenseTask, Invofox 2.0 and SmolDocling, plus manual entry and generic OCR tools. Compare this product with the buyer's present method on accepted records per operator hour and corrections after downstream posting. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference, document storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved extracted records linked to source pages. Track cost per accepted record, including correction work, unsuccessful cases and support.

Preserve source attribution, field accuracy and usage permissions. Named reviewers approve extracted records and downstream posting scope. One document family and one approved export destination; final posting and compliance checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.