Keyword search misses the answer
People waste hours in shared drives and legacy portals because exact-match search can't understand intent, synonyms or specialist language.
RAG Development
RAG development services for enterprise knowledge
We design and build retrieval-augmented generation platforms that turn contracts, case law, policies and knowledge bases into fast, cited answers your teams can trust. Hybrid search, evaluation and security are engineered in from day one, not bolted on after the demo.
What you can expect
- Answers in under 2 seconds across 50,000+ documents
- Every answer cites its source passage
- Access control enforced down to the document
(01) The problem
Why teams come to us for RAG Development.
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Chatbots that hallucinate
Generic LLM chat invents facts and cites nothing. In regulated work, a confident wrong answer is worse than no answer at all.
Prototypes that never reach production
A notebook demo over ten PDFs falls apart at 50,000 documents, several languages, access rules and real latency budgets.
Sensitive data everywhere
Documents contain personal and confidential information that must be protected before it is indexed, retrieved or sent to a model.
(02) Use cases
What we build with RAG Development.
- 01
Legal research and case-law search
Semantic and keyword search across judgments, contracts and statutes, with answers that cite the exact decision and passage.
- 02
Enterprise knowledge assistants
Internal copilots over policies, SOPs, wikis and tickets that respect who is allowed to see what.
- 03
Customer support deflection
Grounded answers from product documentation and past tickets, with a clean hand-off to a person when confidence is low.
- 04
Compliance and policy Q&A
Instant, sourced answers on regulations and internal controls for risk, audit and compliance teams.
- 05
Document drafting and generation
Retrieve precedent and clauses to generate first drafts. Our legal drafting module cut document creation time by 80%.
- 06
Public-facing assistants at scale
Assistants that serve citizens or customers, built for 10,000+ queries a day with routing by audience and time period.
Deliverables
Everything you need to run it in production.
- Document ingestion, OCR and chunking pipeline
- Hybrid retrieval: BM25 keyword, vector and metadata search with reranking
- Citation-first answer generation with guardrails
- Role-based access control and audit logging
- Evaluation suite built on golden question-and-answer sets
- Production API, admin tooling and cloud deployment
- Monitoring for answer quality, latency and token cost
- Architecture documentation and team handover
Our approach
How we deliver reliable results.
- 1
Audit the corpus
We profile your documents (formats, languages, structure and sensitivity) and define the questions the system must answer.
- 2
Design the retrieval stack
Chunking, embeddings, indexes and fusion are chosen and tested on your data, not copied from a tutorial default.
- 3
Build a golden dataset
Domain experts help assemble real questions and answers. On Datagreffe Justice, 200 lawyer-validated pairs gated every change.
- 4
Engineer for trust
Citations, refusal behavior, PII protection and access control are first-class requirements from the first sprint.
- 5
Ship and observe
We deploy to your cloud with dashboards for answer quality, latency and spend, so improvements are measured.
Proof
Related case studies, with real numbers.
Legal-techProduction platform
Datagreffe Justice
An enterprise legal AI platform for 50,000+ court decisions
- court decisions indexed
- 50,000+
- court decisions indexed
- PII detection accuracy
- 98%
- PII detection accuracy
- data-protection & OHADA compliance
- 100%
- data-protection & OHADA compliance
Government & public sector
National Political RAG Assistant
A national-scale AI assistant serving citizens and field teams from one knowledge base
- queries a day, by design
- 10,000+
- queries a day, by design
- audience-specific endpoints
- 2
- audience-specific endpoints
- routing: past vs planned programs
- Temporal
- routing: past vs planned programs
Document AI
Enterprise OCR-to-RAG Pipeline
A five-stage pipeline that turns scanned archives into search-ready knowledge
- pipeline stages
- 5
- pipeline stages
- in
- Scanned PDF
- in
- out
- Vector JSON
- out
Digital mental healthProduction-ready MVP
Namhee
An AI mental-health companion with mood tracking and automatic risk detection
- pass rate on 11 core tests
- 100%
- pass rate on 11 core tests
- RAG response target
- <5s
- RAG response target
- crisis detection
- Risk-aware
- crisis detection
Also delivered
- Backend enhancement of a production legal document-search system
- RAG-ready restructuring of a large-scale CSV and MySQL investment database
Technology
Tools we use for RAG Development.
Model-agnostic and cloud-agnostic: we pick the right components for your data, budget and compliance requirements.
- Claude
- OpenAI GPT-4o
- LangChain
- LlamaIndex
- Pinecone
- Elasticsearch
- Qdrant
- pgvector
- Cohere Rerank
- Python
- Docker
- AWS
Industries we serve
Engagement models
Flexible ways to work, one standard of delivery.
Start small with a discovery sprint or an audit, or go straight to a production build. Most projects start from $5,000, scoped after a free strategy call.
1–2 weeks · fixed scope
AI Strategy & Discovery Sprint
Teams validating an AI opportunity before committing a budget.
- Use-case and ROI workshop
- Data and systems audit
- Architecture blueprint
- Risk and compliance review
- Build plan with estimates
4–12 weeks · milestone-based
Production Build
A defined RAG platform, AI agent, automation or AI product, delivered end to end.
- Architecture and full build
- Evaluation suite and QA
- Security and compliance controls
- Deployment and monitoring
- Documentation and handover
Monthly retainer
Dedicated AI Engineering
Product and platform teams with an ongoing AI roadmap and no time to hire.
- Senior AI engineering capacity
- Weekly planning and demos
- Continuous evaluation and monitoring
- Model and cost optimization
- Scale up with vetted specialists
1–3 weeks · fixed scope
AI Audit & Evaluation
An existing LLM, RAG or agent system that underperforms or is changing hands.
- Architecture and code review
- Answer-quality evaluation
- Security, PII and compliance review
- Cost and latency analysis
- Prioritized remediation report
Not sure which model fits? A free 30-minute call is enough to decide.
Book a free strategy callRAG Development FAQ
Common questions about RAG Development.
Can't find your answer? Ask us on a free strategy call or send a short brief.
What is RAG, and why does my business need it?
Retrieval-augmented generation connects a large language model to your own documents and data. Instead of guessing, the model retrieves the most relevant passages and answers from them, with citations. It is the most reliable way to put an LLM to work on private, changing knowledge.
Should we use RAG or fine-tuning?
If your knowledge changes, needs citations or must respect access rules, start with RAG. Fine-tuning helps with a consistent style or format. Many production systems combine both; we recommend based on evaluation results on your data.
How accurate will the answers be?
We measure it. Every build includes an evaluation suite built from real questions in your domain, so accuracy, citation quality and refusal behavior are tracked numbers before and after launch.
Can it handle scanned PDFs, tables and other languages?
Yes. Our ingestion pipelines combine OCR, layout parsing and table extraction, and we have shipped French-language legal retrieval across more than 50,000 decisions.
How do you keep confidential documents secure?
PII detection and anonymization before indexing, role-based access at retrieval time, encryption, audit logs, and deployment in your own cloud or on-premise with open-weight models when data cannot leave your environment.
How long does a RAG project take?
A focused pilot on one document collection typically takes three to six weeks. Enterprise platforms with multiple sources, access tiers and compliance requirements usually take eight to twelve weeks.
Can you fix an existing RAG system that gives poor answers?
Yes. We audit chunking, embeddings, retrieval, prompts and evaluation, then fix the biggest gaps first. Moving to hybrid search alone improved retrieval by 15% on one legal platform.
Taking on new projects
Let’sbuild.
Have an AI project in mind? Let’s make it real.
Book a free 30-minute strategy call with our founder. You’ll leave with a clear view of the right architecture, timeline and next step, whether or not we work together.
- Reply within 24 hours
- NDA before you share anything confidential
- You own the code, prompts and documentation
- Hours overlap with US, UK, EU and Gulf teams