RAG Development ServicesAI That Answers From Your Data — With Receipts.
We build retrieval-augmented generation systems that ground every answer in your own documents, tickets and databases — with citations, evaluation suites and hallucination rates you can measure, not just hope about.
The demo is easy. Production is the job.
Anyone can wire a vector database to a chat model and demo it in a week. The gap between that demo and a dependable system — clean ingestion from messy sources, hybrid retrieval that actually finds the right passage, re-ranking, citations, refusal behaviour, and an evaluation harness that catches regressions — is where most RAG projects quietly die. That gap is precisely what we build.
The full retrieval pipeline.
Every layer between your raw knowledge and a trustworthy answer, engineered as one system and handed over with documentation.
PDFs, wikis, tickets, databases and transcripts, normalised with chunking strategies that respect document structure instead of splitting mid-sentence.
Dense vectors plus keyword search, fused and re-ranked — because embeddings alone miss exact names, codes and numbers that your users search for.
pgvector, Pinecone, Qdrant or Weaviate — chosen for your scale and budget, with index tuning, metadata filtering and multi-tenant isolation.
A golden-question eval set run on every change, measured faithfulness and answer rates, citation enforcement and refusal when retrieval is weak.
Customer-support bots, internal knowledge assistants and in-product copilots — with handoff to humans and full conversation analytics.
Deployed to your cloud with monitoring, cost controls, prompt versioning and re-indexing pipelines that keep answers current as your data changes.
Deciding between approaches? Read our essay on taking generative AI past the demo, or see the broader generative AI services.
Common questions.
Anything not covered here, ask us directly — we answer within 24 hours.
What does a RAG development engagement include?
Everything between your documents and a grounded answer: data ingestion and chunking strategy, embedding model selection, vector database setup (pgvector, Pinecone, Qdrant or Weaviate), hybrid search with re-ranking, prompt assembly, citation handling, an evaluation suite, and production deployment with monitoring.
RAG vs fine-tuning — which do we need?
RAG when the answers must come from your own, changing knowledge (docs, tickets, policies) and must be traceable to a source. Fine-tuning when you need the model to adopt a style or a narrow skill. Most business assistants need RAG first; many need no fine-tuning at all. We will tell you honestly which applies before any build starts.
How do you stop the system from making things up?
Grounded-only answering: retrieval with hybrid search and re-ranking, strict context windows, citation of the retrieved source in every answer, refusal when retrieval confidence is low, and an evaluation set that is run on every change — hallucination rate is a measured number, not a hope.
How long does a RAG project take and what does it cost?
A production assistant over one well-defined knowledge base typically takes 6 to 10 weeks. Cost depends on data messiness and integration count — after a free scoping call you get a fixed quote. Senior engineering delivered from Lahore keeps the number well below US or EU agency equivalents.
Which stacks do you build RAG on?
Model-agnostic: Claude, GPT, Gemini or open-weights via vLLM. TypeScript or Python pipelines, pgvector, Pinecone, Qdrant or Weaviate for retrieval, deployed to your cloud — you own all code and infrastructure from day one.
Have a knowledge base? Let’s make it answer.
Bring a sample of your documents to a free scoping call — a senior AI engineer will sketch the retrieval architecture with you and give you a fixed quote.