RAG Development Services
A knowledge assistant that actually knows your business.
Secure, citation-backed enterprise assistants built on your own documents, wikis and repositories - grounded, permission-aware and hallucination-resistant.
Generic chatbots guess. A well-built RAG system answers from your documents — and shows its sources.
We build retrieval-augmented assistants that index your knowledge - policies, contracts, tickets, wikis, product docs - and answer questions with citations back to the source. Answers stay grounded in your content instead of the model's imagination.
Security is designed in from day one: retrieval honours your existing access controls so users only see what they're allowed to, and your data isn't used to train third-party models. Deploy in your cloud or fully on-prem for sensitive material.
What we build
Grounded answers, with receipts
The retrieval quality and permission model make or break enterprise RAG. We get both right.
Document ingestion
Robust pipelines that parse, chunk and embed PDFs, Office files, wikis and databases — and stay in sync as content changes.
Hybrid retrieval
Semantic plus keyword search with re-ranking, so the assistant finds the truly relevant passage, not just a similar-sounding one.
Cited answers
Every response links to the source documents, so users can verify and trust what they read.
Permission-aware retrieval
Results are trimmed to each user's access rights, inherited from your source systems.
Private deployment
Run in your VPC or on-prem; no training on your data, full data-residency control.
Continuous evaluation
We measure answer quality and retrieval accuracy, and tune as your knowledge base grows.
How we deliver
A path from idea to production
A pragmatic engagement model that de-risks adoption and gets a working system in front of your users fast.
- 01
Source mapping
We identify your knowledge sources, formats and access rules — and how fresh answers need to be.
- 02
Retrieval build
We build the ingestion, chunking, embedding and re-ranking pipeline tuned to your content.
- 03
Grounding & eval
We wire in citations, guardrails and an evaluation set to measure faithfulness and relevance.
- 04
Deploy & maintain
We ship the assistant into your tools and keep the index fresh and accurate over time.
Tools & stack
Technologies we work with
We stay model- and vendor-flexible, choosing the stack that fits your data, budget, and compliance needs.
- LlamaIndex
- LangChain
- Pinecone
- Weaviate
- pgvector
- Qdrant
- Cohere Rerank
- OpenAI / Claude
- Elasticsearch
- AWS Bedrock
- Azure AI Search
- Onyx
Where it fits
Use cases & industries
Wherever employees or customers lose time hunting for information that already exists.
Internal knowledge search
One assistant across wikis, drives, tickets and chat, with permission-aware answers.
Customer support
Deflect and assist with answers grounded in your help centre and docs.
Policy & compliance QA
Ask questions about policies and get cited, defensible answers.
Sales enablement
Instant retrieval of pricing, decks and prior deals for reps.
Onboarding
New hires find context and documentation without pinging colleagues.
Legal & contracts
Query large contract sets with sources for every answer.
Common questions
No. RAG retrieves your content at query time and passes it to the model as context — your data isn't used to train the underlying model.
We ground answers in retrieved passages, require citations, and add guardrails so the assistant says 'I don't know' rather than inventing an answer.
Yes. Retrieval inherits your source-system permissions so each user's answers are trimmed to what they're authorised to access.
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