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Solution

Intelligent Document Processing for Invoices, Contracts, and Forms

Documents arrive by email, portal, scan, and photo. We deploy AI that reads them, extracts what matters, checks it against your systems and rules, and routes the result to the right place, with people reviewing only the exceptions. Built on hosted or self-hosted models, with every field traceable to the page it came from.

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Who This Is For

Built for back offices buried in paper and PDFs

  • Accounts payable teams keying invoices, matching them to purchase orders, and chasing approvals.
  • Operations teams processing applications, onboarding packets, claims, and compliance documents with strict fields and deadlines.
  • Legal, real estate, and finance teams extracting terms, dates, and obligations from contracts and leases into systems and reports.
  • Any team whose document volume grows faster than the people available to read it.

What the Solution Includes

From inbox to structured record

Capture from any channel

Email attachments, portal uploads, scans, photos from the field, and shared folders collected automatically and classified by document type.

Extraction with confidence scores

Fields, tables, and clauses extracted by layout-aware models and language models, each value carrying a confidence score and a link to its location on the page.

Validation against your rules and systems

Totals checked, vendors and POs matched, dates and IDs validated, duplicates caught, and business rules applied before anything is posted.

Routing and posting

Clean records posted to your ERP, accounting, CRM, or case system; approvals requested with context; documents filed with searchable metadata.

Exception queue for people

Low-confidence or rule-failing documents land in a review interface where a person confirms or corrects in seconds, and the system learns from it.

Audit trail

Every extracted value, rule result, correction, and posting logged with who, what, when, and source page, ready for auditors.

Why document work resists automation

Documents are where structured systems meet the unstructured world: a supplier's invoice layout, a scanned application, a photo of a delivery slip. Template-based capture breaks whenever a layout changes, so people end up keying anyway. Modern extraction combines layout-aware and vision models with language models that understand what a field means, not just where it sits. That is what makes reliable automation possible across hundreds of layouts.

How the DPI pipeline works

Documents are collected from email, portals, scans, and folders and classified by type. Extraction produces fields, tables, and clauses with a confidence score and a pointer to the page location for each value. Validation applies your rules: totals, vendor and PO matching, dates, duplicates, policy checks. High-confidence, rule-passing documents are posted to your systems automatically; the rest go to a review interface where a person confirms or corrects in seconds. Every step is logged for audit.

Systems and hosting

Results are posted through APIs into ERP, accounting, CRM, case, and document management systems, with approvals requested where your process demands them. Models run hosted under enterprise terms or self-hosted on your infrastructure, and storage can stay entirely in your PostgreSQL and file systems. Healthcare workloads run under a BAA.

What you measure

Documents processed per day without human touch, per-field accuracy, exception rate and reasons, cycle time from arrival to posting, and hours returned. We report monthly and use corrections from the review queue to keep improving the pipeline.

How It Works

How a document pipeline goes live

  1. Document inventory

    Types, volumes, sources, layouts, target fields, rules, and downstream systems mapped with the team that handles them today.

  2. Pipeline design

    Classification, extraction, validation, and routing designed per document type, with confidence thresholds and review rules.

  3. Shadow run on real documents

    The pipeline processes live documents alongside the manual process; accuracy is measured per field until it meets the bar.

  4. Cut-over and expansion

    Automated posting for high-confidence documents, review for the rest, then more document types added as each proves out.

Stack & Integrations

Document stack and integrations

We combine layout-aware extraction with language models, keep records in PostgreSQL, and post results through APIs into the systems your finance and operations teams use.

OpenAI, Anthropic Claude, AWS Bedrock Self-hosted Qwen and open-source vision-language models Open-source OCR and layout models LangChain and LangGraph PostgreSQL, pgvector, Qdrant, Redis ERP, accounting, and AP systems CRM, case, and document management systems Mailgun email, shared drives, portals

Industries

Document workflows by industry

Real Estate

Leases, applications, vendor invoices, and owner statements.

Engagement Models

Engagement models

Document assessment

Two weeks: inventory, volumes, target fields and rules, integration map, projected accuracy and hours returned.

Pipeline delivery

Fixed-scope build of the first document types through shadow run and cut-over, with the review interface and training.

Managed processing

Ongoing operation: accuracy monitoring, exception review support, new document types, and monthly reporting.

FAQ

Questions about AI document processing

How accurate is the extraction?

It depends on document type and quality, which is why we measure it per field on your real documents during the shadow run and set confidence thresholds so uncertain values go to a person. The accuracy bar is agreed before cut-over, not promised in advance.

Does it handle handwriting, photos, and poor scans?

Yes, with layout-aware and vision models, and with a review step for values the models are unsure about. Photos from the field are common in construction and logistics deployments.

Can it post directly into our accounting or ERP system?

Yes, through APIs, with validation and approvals before posting. Systems without APIs are handled through structured exports or database integration.

Can documents stay inside our environment?

Yes. OCR, vision, and language models can run self-hosted, with storage in your PostgreSQL and file systems. Healthcare documents are processed under a signed BAA.

What do people still do?

Review exceptions in a purpose-built interface, approve what needs approval, and handle unusual documents. Their corrections feed back into the pipeline.

How long until it runs?

The first one or two document types typically reach cut-over within weeks after the assessment; additional types are added as each proves out.

Related

Next Step

Bring one workflow. Leave with a production plan.

Tell us where calls, tickets, documents, or approvals pile up. We map the workflow, size the impact, and propose a deployment you can measure.

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