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RAG Development

Enterprise RAG Development Services

Most RAG demos work on ten PDFs and fall apart on ten thousand. I design retrieval for real corpora: structure-aware chunking, hybrid BM25 and vector search, re-ranking, metadata filters, permission-aware retrieval, and evaluation harnesses that prove quality before launch. It is the same approach behind a 50,000-document legal platform running in production.

At a glance

  • Accurate, cited answers
  • Secure by design
  • Measured quality
  • Fast at scale
Book a free discovery call See related case studies
Outcomes

What you get from this engagement.

  • 01

    Accurate, cited answers

    Every answer links back to its source, so users can verify instead of trusting blindly.

  • 02

    Secure by design

    Role-based retrieval, namespace isolation and PII anonymization before anything is indexed.

  • 03

    Measured quality

    An evaluation set built with your experts, scored on retrieval hit-rate, faithfulness and relevance.

  • 04

    Fast at scale

    Sub-2-second responses over tens of thousands of documents in production.

Deliverables

Scope, made concrete.

  • Data audit and retrieval architecture document
  • Ingestion pipeline for PDF, Word, HTML and scanned files (OCR)
  • Vector database setup: Pinecone, Qdrant, Weaviate, pgvector or Elasticsearch
  • Hybrid search, re-ranking and query routing
  • Evaluation dataset and automated quality reports (RAGAS)
  • Production API, Docker deployment and documentation
Use cases

Where it delivers value.

  • Legal research and case-law search
  • Internal knowledge assistants
  • Customer-support copilots
  • Policy and compliance Q&A
  • Research over financial and technical reports

Technology

  • LangChain
  • LlamaIndex
  • Pinecone
  • Elasticsearch
  • Qdrant
  • Weaviate
  • pgvector
  • FAISS
  • Cohere Rerank
  • RAGAS
  • Claude API
  • OpenAI
  • FastAPI
  • Docker
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
civic-assistant · dual endpoint
10K+/day
“When does the youth scholarship program expand?”temporal router → planned

Publicns: public

The expansion is part of the next program cycle. Eligibility criteria and official announcements are listed on the program page.

2 public sources

Field teamns: field

Rollout guidance for coordinators: priority regions, required documents and the household briefing checklist.

3 field sources · restricted

one knowledge base · two namespacesno cross-audience leakage

Illustrative interface · client data never shown
02Civic Intelligence Assistant

High-Volume Public-Sector RAG Assistant

A dual-endpoint RAG assistant that serves the public and the field teams of a national political organization from one governed knowledge base. It is sized for 10,000+ queries a day, with temporal query routing and namespace-isolated retrieval.

Queries per day capacity
10K+
Audience-specific endpoints
2
Governed knowledge base
1
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.

03Document 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
Digital Health4 stages
  1. 01Screen
  2. 02Retrieve
  3. 03Generate
  4. 04Remember

Stage 1, Screen: Crisis-risk detection. Stage 2, Retrieve: Pinecone CBT/mindfulness corpus. Stage 3, Generate: GPT-4o via LangChain. Stage 4, Remember: Session memory and mood history.

04Namhee

Safety-Aware Mental-Health AI Assistant

A RAG-grounded mental-health support assistant with mood assessment and tracking, CBT and mindfulness guidance, session memory and automatic crisis-risk detection that points users to helplines.

Pass rate across 11 core tests
100%
RAG response target
<5s
Crisis-risk escalation
Auto
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.

One email with the checklist. Unsubscribe from anything else any time.

FAQ

RAG Development: common questions.

How long does it take to build a production RAG system?

A focused proof of value on your real documents usually takes 2–4 weeks. A production rollout with access control, evaluation and monitoring usually takes 6–12 weeks, depending on data volume, formats and integrations. We scope it precisely on the discovery call.

Should we use RAG or fine-tuning?

If your knowledge changes, needs citations or lives in documents, start with RAG. It is faster to build, easier to debug and updates instantly. Fine-tuning helps with style and format, and the two can be combined.

Can you work with private or sensitive data?

Yes. I design for least-privilege access, PII anonymization before indexing, role-based retrieval and deployment inside your own cloud environment, so data stays under your control. I'm happy to sign an NDA before we start.

Which vector database should we choose?

It depends on your stack and scale. Pinecone is a strong managed default. pgvector suits teams already on Postgres, Elasticsearch excels at hybrid keyword and vector search, and Qdrant or Weaviate work well self-hosted. I recommend based on your constraints, not habit.

How do you measure accuracy?

Before optimizing anything, I build an evaluation set of real questions with your domain experts. Every change is then scored on retrieval hit-rate, faithfulness and answer relevance, so improvements are measured rather than assumed.

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