Document data extraction and validation portal

For

Finance and operations teams processing invoices, receipts and forms

Solves

Invoices, receipts and forms arrive in mixed formats and staff re-key the same data into finance systems.

Delivers

Reviewer-approved structured records linked to source pages

Built in

about 6 weeks of creation time, MVP in 7 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 re-keying while keeping a named reviewer accountable for every extracted field.

1. Capture documents from upload, email, mobile scan and watched folders.
2. Classify document type and route to the matching template.
3. Extract header and line-item fields with AI.
4. Apply custom extraction templates and field rules.
5. Build and edit templates and workflows without code.
6. Handle scanned images and digital files.
7. Switch between vision and language model extraction per template.
8. Flag low-confidence fields for human review.
9. Let reviewers correct values and confirm source pages.
10. Validate totals, tax, dates and duplicate checks.
11. Push approved records to finance systems via API.
12. Capture invoices from Gmail and return structured data by email.
13. Train and fine-tune models on approved layouts.
14. Use preconfigured models for common document types.
15. Track accuracy over time from reviewer corrections.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Export a versioned reviewer-approved structured records linked to source pages with source references and unresolved questions.

Everything these tools do, in one app

• AI-powered data extraction Automatically extracts structured data from unstructured documents using artificial intelligence.Found in Extracta.ai, FormX.ai, TurboDoc and 1 more
• Custom extraction templates Allows users to create and customize templates for extracting specific data fields from documents.Found in Extracta.ai, FormX.ai, Cradl AI
• No-code interface Enables users to build and customize extraction workflows without programming.Found in Extracta.ai, Cradl AI
• Multiple document formats Supports extraction from various document types such as invoices, receipts, contracts, and forms.Found in Extracta.ai, FormX.ai, TurboDoc and 1 more
• Scanned and digital files Handles both scanned images and digital documents.Found in Extracta.ai, FormX.ai
• Mobile scanning Captures data from physical documents using a mobile device.Found in FormX.ai
• API integration Integrates with existing systems via API to import structured data.Found in FormX.ai, Cradl AI
• User-friendly dashboard Provides a web portal or dashboard for managing, monitoring, and analyzing extracted data.Found in FormX.ai, TurboDoc
• Gmail integration Captures invoices from Gmail and sends structured data back via email.Found in TurboDoc
• Human-in-the-loop validation Allows users to review and correct uncertain AI predictions to ensure accuracy.Found in Cradl AI
• Workflow builder Enables creation of automations with custom rules to streamline document processing.Found in Cradl AI
• Custom AI models Allows training and fine-tuning of AI models for specific document layouts.Found in Cradl AI
• Preconfigured models Offers ready-to-use extraction models for common document types.Found in FormX.ai
• Continuous accuracy improvement Improves extraction accuracy over time through real-world feedback.Found in FormX.ai
• Vision and LLM switching Allows switching between Vision and Large Language Model technologies for extraction.Found in FormX.ai
• Security and compliance Ensures data security with encryption and compliance certifications like ISO 27001 and GDPR.Found in Extracta.ai
• Automated invoice processing Automatically extracts and organizes data from invoices, reducing manual entry.Found in TurboDoc

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted documents, extraction templates, validation rules and system schemas

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted documents

4. 3

   Extraction templates

5. 4

   Validation rules and system schemas

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved structured 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 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. One fixed document set and approved finance schema; final coding, tax treatment and posting decisions remain with finance staff. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Document intake and templates, Editable extraction review, Export and reconciliation. Use a queue of incoming documents, a large central page viewer with field overlays, and a right-hand panel for extracted values, confidence, validation rules and comments. Let users compare original page and extracted record side by side. Display received, needs review, approved and exported states. Provide a reconciliation view linking each record to its source page and destination system. Make the task-specific outcome reviewer-approved structured records linked to source pages visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, template versions, reviewer assignments, approval states, usage allowances, retention limits, export 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

Client-owned documents, authorized email inboxes and permitted finance systems. Cloud document storage, accounting and ERP import/export and email destinations. 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

   7 days

   One buyer segment, one recurring use case; first modules: capture documents from upload, email, mobile scan and watched folders; classify document type and route to the matching template. 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 finance and operations teams processing invoices, receipts and forms use it to solve "invoices, receipts and forms arrive in mixed formats and staff re-key the same data into finance systems"?
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 processing hour and corrections after posting.
4. Measure, then decide. Track accepted records per processing hour and corrections after 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 fixed document set and approved finance schema; final coding, tax treatment and posting decisions remain with finance staff. Implement one approved input format, a bounded representative case set and the first two task modules: capture documents from upload, email, mobile scan and watched folders; classify document type and route to the matching template. Support the third module with operator review: extract header and line-item fields with AI. 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 structured records linked to source pages. Retain the explicit scope boundary: One fixed document set and approved finance schema; final coding, tax treatment and posting decisions remain with finance staff.

What the build depends on. Document upload and preview, asynchronous extraction jobs, editable version history, reviewer access and tested export formats. High-fidelity finance posting requires specialist accounting QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed document set and approved finance schema; final coding, tax treatment and posting decisions remain with finance staff.

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 package. Offer a monthly processing allowance after repeat demand. Quote complex ERP integrations or specialist document types separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved structured 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 re-keying while keeping a named reviewer accountable for every extracted field. Demonstrate a concrete reviewer-approved structured records linked to source pages using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Finance and operations teams processing invoices, receipts and forms 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 structured records linked to source pages from a small authorized input set, with a transparent calculation of accepted records per processing hour and corrections after posting and no promised savings.

The first 30 days

1. Week 1: interview five finance and operations teams processing invoices, receipts and forms and inspect a recent example of invoices, receipts and forms arrive in mixed formats and staff re-key the same data into finance systems.
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 records per processing hour and corrections after 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 processing hour and corrections after 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 processing hour and corrections after 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 structured 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 templates, validation rules and reviewer corrections, together with reliable delivery for a narrow finance niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for finance and operations teams processing invoices, receipts and forms. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Extracta.ai, FormX.ai, TurboDoc and Cradl AI, plus manual entry and generic OCR tools. Compare this product with the buyer's present method on accepted records per processing hour and corrections after posting. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

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

Preserve document integrity, source attribution, field accuracy and usage permissions. Finance staff approve substantive changes and posting scope. One fixed document set and approved finance schema; final coding, tax treatment and posting decisions remain with finance staff. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.