Data & AI Engineering

AI document processing that integrates with business workflows

Extracting text from a PDF is only the start. We build document pipelines that classify, extract, verify and deliver structured information to ERP, DMS and business applications with human review when needed.

Discuss the current situation

The operational problem

Contracts, claims, forms and invoices vary in structure and quality. OCR output is not a reliable business record without field validation and handling of missing or ambiguous values.

We work backwards from the destination system: what fields are required, which decisions depend on them and which errors need human approval? Then we compare rules, OCR, layout models and language models.

What we engineer

Classification and ingestion

Validate files, recognize document classes, manage duplicates and provenance.

OCR and extraction

Read scans, PDFs and tables, then generate schema-conformant structured values.

Business validation

Check amounts, dates, IDs and cross-field consistency against domain rules.

Human review

Route uncertain or high-risk results to accountable specialists.

Governed integration

Connect approved data to ERP, DMS and CRM with access controls, audit trails and retention policies.

Methods and trade-offs

A generative model is not inherently the right extractor. Stable layouts may be better served by deterministic OCR and rules; variable layouts can benefit from learned methods, provided output is validated.

We measure field-level accuracy, error severity and correction effort. Human-in-the-loop is not a fallback added at the end; it is part of a controllable processing design.

From evaluation to production

Define document types

Collect representative files, exceptions, field definitions and target systems.

Create evaluation criteria

Measure extraction quality, completeness and downstream business risk.

Build extraction pipeline

Implement parsing, OCR, classification, extraction and schema tests.

Integrate and approve

Deliver valid records and route exceptions for manual review.

Monitor changes

Track new templates, errors, access and version changes.

Frequently asked questions

Can AI read every PDF reliably?

No. Results vary by scan, layout, language and target field. Validation and review are essential for high-risk values.

When are rules enough?

For stable formats with clear fields, deterministic extraction can be cheaper and more testable.

How are personal data protected?

By minimization, access controls, defined storage and retention, masking and audited handoffs.

How do we verify quality?

Evaluate per-field accuracy, completeness, false positives and manual correction effort.

Technical discovery

Discuss the system and implementation constraints

We review the existing systems, technical constraints and a measurable first delivery step.