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Document AI & OCR

Intelligent Document Processing & PII Anonymization

Documents are where most enterprise knowledge is trapped. I build pipelines that OCR scanned files, extract structured fields, classify and summarize documents, and detect and anonymize personal data. These are the same techniques that reached 98% PII-detection accuracy on legal case decisions.

At a glance

  • Archives become searchable
  • Privacy built in
  • Less manual entry
  • Multilingual
Book a free discovery call See related case studies
Outcomes

What you get from this engagement.

  • 01

    Archives become searchable

    Scanned files turned into clean text and structured data.

  • 02

    Privacy built in

    PII detected and anonymized before data moves downstream.

  • 03

    Less manual entry

    Fields extracted straight into your systems instead of typed by hand.

  • 04

    Multilingual

    Pipelines proven on French-language legal material.

Deliverables

Scope, made concrete.

  • OCR pipeline for scanned PDFs and images
  • Structured extraction to JSON or your database
  • Document classification and summarization
  • PII detection and anonymization
  • Multilingual processing, including French
  • Hand-off into search, analytics or RAG
Use cases

Where it delivers value.

  • Legal case files and court decisions
  • Contracts and agreements
  • Financial filings and earnings calls
  • Insurance and healthcare forms
  • Archive digitization

Technology

  • Python
  • OCR
  • PDF extraction
  • Claude API
  • OpenAI
  • NER
  • Elasticsearch
  • Pinecone
  • LangChain
Proof

Related case studies.

Real systems delivered with this approach, with the constraints and results that came with them.

legal-search · data-greffe-justice
Hybrid · RBAC
résiliation abusive d’un bail commercial, OHADA

50K+ décisions· < 2 s· BM25 + vecteurs· réponses citées

  • Cour d’appel · Commercial0.94

    …la juridiction relève que [PARTIE A] a procédé à la résiliation du bailsans préavis, en méconnaissance de l’Acte uniforme OHADArelatif au droit commercial général…

    [1] Arrêt n° ••• · chaîne d’appel ↗

  • Tribunal de commerce · Bail0.86

    …[PARTIE B], preneur, sollicite des dommages-intérêts pour rupture abusive…

rôle : juristePII masquées3 index
Illustrative interface · client data never shown
01Data Greffe JusticeLive in production

Enterprise Legal Intelligence Platform

A production RAG platform that turns 50,000+ French-language court decisions into a searchable, privacy-safe knowledge base, with triple-search retrieval, role-based access, compliance-grade PII anonymization and AI-assisted drafting.

Legal documents indexed
50K+
PII detection accuracy
98%
Query response time
<2s
Read case study
Enterprise Knowledge Management5 stages
  1. 01PDF
  2. 02Image
  3. 03Clean
  4. 04OCR
  5. 05Structure

Stage 1, PDF: Scanned document intake. Stage 2, Image: Page-to-image conversion. Stage 3, Clean: Preprocessing for OCR accuracy. Stage 4, OCR: Text extraction. Stage 5, Structure: Vector-ready JSON.

02Document Intelligence Pipeline

OCR-to-RAG Document Ingestion Pipeline

A five-stage pipeline that converts scanned PDFs into clean, structured, vector-ready JSON, opening up archives that search and RAG could not read before.

Processing stages
5
Unstructured to structured
Scan → JSON
Vector-store output
RAG-ready
Read case study
Fintech & Capital Markets5 stages
  1. 01Ingest
  2. 02Extract
  3. 03Summarize
  4. 04Structure
  5. 05Deliver

Stage 1, Ingest: PDF and transcript extraction. Stage 2, Extract: Figures, guidance, signals. Stage 3, Summarize: Specialized summarization agent. Stage 4, Structure: Report-structuring agent. Stage 5, Deliver: Client-ready report.

03Earnings Intelligence Crew

Financial Intelligence Multi-Agent Pipeline

A CrewAI multi-agent pipeline that reads earnings-call transcripts and financial PDFs, splits the analysis across specialized agents and produces structured, client-ready reports.

Specialized CrewAI roles
Multi-agent
Unstructured financial inputs
PDF + calls
Structured client output
Report-ready
Read case study
How we work

A delivery process built for zero surprises.

Clear milestones, working demos every week and measurable quality gates, so you always know what's done, what's next and what it costs.

  1. 01

    Discover

    A free 30-minute call to understand the problem, the data and what success looks like.

    You getClear problem statement and success metrics

  2. 02

    Architect

    A written technical plan: architecture, model and vector-store choices, risks, milestones and a fixed quote.

    You getArchitecture document and milestone plan

  3. 03

    Build

    Iterative delivery in short milestones with working demos, so you see progress every week, not at the end.

    You getWorking increments you can test

  4. 04

    Evaluate & harden

    Measured against an evaluation set, then hardened with guardrails, access control, error handling and load testing.

    You getEvaluation report and test suite

  5. 05

    Deploy & support

    Dockerized deployment to your cloud, documentation and handover, with optional ongoing support and improvement.

    You getProduction system plus documentation

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Free checklist

The Enterprise RAG Production Checklist

Ten areas that separate a RAG demo from a system people trust: evaluation, ingestion, hybrid retrieval, access control, PII, citations, cost and monitoring. Sent to your inbox.

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FAQ

Document AI & OCR: common questions.

How accurate is OCR on poor-quality scans?

Accuracy depends heavily on preprocessing. I clean, deskew and normalize images before OCR and validate the output, because downstream search and RAG are only as good as the extracted text.

How do you handle personal data?

PII is detected and anonymized before indexing or sharing, with extra safeguards for sensitive groups such as minors. On legal case data this reached 98% detection accuracy.

Which languages do you support?

English and French are proven in production, and modern LLM-based extraction extends well to most major languages.

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