SERVICE
LLM & RAG Development
Language models that know your business.
What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation (RAG) makes a large language model answer from your own documents. When a question arrives, the system searches a vector database for the most relevant passages, gives them to the model as context, and instructs it to answer from that context and cite it. RAG keeps answers current and traceable without retraining the model.
THE PROBLEM
General-purpose language models do not know your products, policies, or documents, and when they do not know, they guess. That makes them risky for customer-facing answers and unreliable for internal knowledge work.
OUR SOLUTION
We engineer production generative AI: retrieval-augmented generation that connects models to your knowledge base, fine-tuning where a model needs domain-specific behaviour, and the guardrails, citations, audit trails, and evaluations that make LLM features safe to ship.
HOW IT WORKS
How we build a RAG system
- 01
Prepare the knowledge
Documents are cleaned, split into meaningful chunks, tagged with metadata, and embedded into a vector database.
- 02
Tune retrieval
Search is tested against real questions and tuned, often combining semantic and keyword search, until the right passages come back.
- 03
Engineer the answers
Prompts, citation rules, and guardrails make the model answer from the retrieved context, say when the answer is not there, and stay on topic.
- 04
Evaluate
An evaluation set built from real questions measures retrieval quality, answer accuracy, and how often the model invents things, before launch.
- 05
Operate
Monitoring, audit trails, and a re-indexing pipeline keep answers accurate as your documents change.
TYPICAL STACK
- LLM APIs
- LangGraph
- Pinecone
- Chroma
- pgvector
- FastAPI
- Python
TIMELINE
A RAG assistant over a defined set of documents is typically a focused build in our 4 to 8 week range.
Key features
- RAG pipelines with vector search (Pinecone, Chroma, pgvector)
- Cited answers with source attribution
- LLM fine-tuning on proprietary datasets
- Guardrails, audit trails, and compliance controls
- Evaluation sets that measure accuracy before launch
Use cases
- Internal knowledge assistants over policies and documentation
- Customer support answers grounded in help content
- Document Q&A for legal, finance, and healthcare teams
- Domain-specific copilots inside existing products
Benefits
- Answers grounded in your documents, not guesses
- LLM features without an in-house AI research team
- Every answer traceable to its source
- Multilingual support, including Arabic and Urdu
COMPARE
RAG vs. fine-tuning
| Criterion | RAG | Fine-tuning |
|---|---|---|
| What it changes | What the model knows when it answers | How the model behaves and writes |
| Staying current | Update the documents, no retraining | Retrain to add new knowledge |
| Citations | Can cite the exact source passage | Cannot point to a source |
| Best for | Changing knowledge, answers you must trace | Consistent format, tone, or special tasks |
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FAQS
LLM & RAG Development: common questions
What is the difference between RAG and fine-tuning?
RAG gives the model the right information at the moment it answers, by retrieving passages from your documents. Fine-tuning changes the model itself so it behaves differently, for example following a format or tone. RAG is the usual starting point for business knowledge because it stays current and can cite sources. Fine-tuning is added when behaviour, not knowledge, is the problem.
Which vector database should we use?
Pinecone for a fully managed service at scale, Chroma for lightweight and self-hosted setups, and pgvector when you already run Postgres and want one less system. We choose based on data volume, hosting constraints, and your existing stack.
How do you reduce hallucinations?
By grounding answers in retrieved passages, requiring citations, instructing the model to say when the answer is not in the sources, and measuring how often it invents things on an evaluation set before and after launch.
Can a RAG system keep our data private?
Yes. Access can mirror your existing document permissions, data can be processed in infrastructure you control, and model providers are chosen to meet your data requirements. Audit trails record who asked what and which sources were used.
Does it work in languages other than English?
Yes. We build multilingual pipelines, including Arabic and Urdu, so people can ask and get answers in the language they work in.
Discuss this project
Tell us what you're building. We'll show you exactly how we'd engineer it.
- Free 30-minute discovery call
- You own the code, models, and IP
- Working software every week