AI Consultancy & Strategy
LLM & RAG systems
We build assistants and search tools on large language models that answer from your own documents, tickets and databases, not the open internet. Retrieval-augmented generation (RAG) keeps answers grounded in your sources and lets people check where each answer came from.
Retrieval-augmented generation
Grounded answersRetrieving from your sources
Is this for you?
You’ll get value from this if…
- Staff lose time hunting through documents, wikis and old tickets
- Customers ask the same questions your docs already answer
- You want an internal assistant but can't send data to a public chatbot
- A prototype works in demos but gives wrong answers in real use
Benefits
What changes for your business
Answers people can trust
Every answer is grounded in your sources and shows where it came from.
Your data stays yours
Designed around your access rules and data policy from the start.
Quality you can measure
An evaluation set tells you whether changes make answers better or worse.
What you get
What we deliver
Retrieval pipeline
Your content ingested, split, embedded and indexed so the right passages come back for each question.
Grounded assistant
A chat or search interface that answers from your sources and cites them.
Evaluation set
Real questions with expected answers, so quality is measured rather than guessed.
Guardrails and access control
Answers limited to what each user is allowed to see, with sensible refusals when the sources don't cover a question.
How it works
From first call to running in production
- 01
Scope
Pick the sources and the questions the system must answer well.
- 02
Prototype
A working version on a sample of your data to prove answer quality.
- 03
Harden
Evaluation, access control, monitoring and the edge cases that break demos.
- 04
Roll out
Deploy into the tools your team already uses and hand over.
Example applications
What this looks like in practice
Typical applications of this service. Illustrative, not client case studies.
- 01Internal knowledge assistant over policies, wikis and SOPs
- 02Customer-support assistant that drafts replies from your help centre
- 03Search across contracts, reports or technical manuals
- 04Sales assistant that answers product questions from your own documentation
Technology
Tools we work with
- OpenAI
- Anthropic Claude
- Open-source LLMs
- Vector databases
- Embeddings
- Python
- TypeScript
Why Bitfumes
Built by engineers who ship
Senior team, no hand-offs
The engineers on your first call are the ones who build your product.
10+ years of engineering leadership
Led by Sarthak Shrivastava, Docker Captain, AWS Certified Solutions Architect, AWS Certified Developer.
We teach this for a living
156K+ developers learn from our founder on YouTube, and 100K+ on Udemy.
Production, not prototypes
Tests, monitoring and handover are part of every build, not extras.
How to start
From first conversation to production
- 1
Talk to us
Tell us the problem. We come back with a straight view on whether it is worth building.
Get in touch - 2
Build
A senior team embeds with yours and ships in short cycles, with a demo every week.
- 3
Run and improve
We hand over cleanly, or stay on to monitor, support and extend what we built.
FAQs
Common questions
What is RAG?
Retrieval-augmented generation: before the model answers, the system retrieves relevant passages from your own content and gives them to the model, so the answer is based on your sources rather than the model's general training.
Will our data be used to train a public model?
We design the system so your data is used only to answer your users' questions, and choose model providers and hosting options that fit your data policy.
How do you stop the assistant making things up?
By grounding answers in retrieved sources, showing citations, testing against an evaluation set of real questions, and having the system decline when the sources don't cover a question.
Which language model do you use?
We choose per project based on answer quality, cost, latency and your data policy, and design the system so the model can be swapped later.
Can it work with scanned PDFs and messy documents?
Yes. Extracting and cleaning content is part of building the retrieval pipeline, and we flag sources that need fixing at the source.
Insights
Related reading
- AI StrategyRAG, explained without the hand-waving.Retrieval-augmented generation is the difference between an AI that guesses and one that knows your business. Here's how it actually works, and when it's the wrong tool.Read
- AI StrategyChoosing an LLM for production isn't a benchmark exercise.Leaderboards tell you which model is smartest in a vacuum. Shipping software tells you which model is cheapest, fastest, and most consistent for your exact task — a different question entirely.Read
Next step
Want this built into your business, not just explained?
Tell us the problem and we'll come back within one business day with a straight view on whether AI is worth it for you, and what it would take to build.
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