Find where AI pays, then build it
AI Consultancy & Strategy
From boardroom roadmap to production LLM systems. We find where AI moves your numbers — then build it.
What's inside
5 servicesWhat we do
What’s included
AI readiness audits
Where AI will pay off, ranked by ROI
Learn moreLLM & RAG systems
Assistants grounded in your own data
Learn moreWorkflow automation
Agents that take repetitive work off your team
Learn moreApplied ML models
Forecasting, scoring and classification in production
Learn moreAI training & workshops
Hands-on AI upskilling for your team
Learn more
Technology
Tools we work with
- Process mapping
- Data audits
- Value / effort scoring
- Security & compliance review
- OpenAI
- Anthropic Claude
- Open-source LLMs
- Vector databases
- Embeddings
- Python
- TypeScript
- AI agents
- Model Context Protocol (MCP)
- APIs & webhooks
- Browser automation
- scikit-learn
- PyTorch
- SQL
- Cloud ML services
- Docker
- Claude
- ChatGPT
- Microsoft Copilot
- Cursor
- Claude Code
- Prompt engineering
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 an AI readiness audit?
A structured review of your workflows, systems and data that identifies where AI would save time or money, ranks those opportunities, and outlines what it would take to build them.
Do we need clean data before an audit?
No. Assessing what data you have and what state it is in is part of the audit.
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.
What is an AI agent?
Software that uses a language model to decide and carry out the steps of a task, such as reading a request, looking something up and updating a system, rather than following a fixed script.
Do we need to replace our current systems?
No. We connect to the systems you already run through their APIs or, where there is none, through the interfaces people use today.
When is classic machine learning better than an LLM?
When the task is a prediction from structured data, such as a forecast, a score or a category. Classic models are usually cheaper, faster and easier to validate for these.
How much data do we need?
It depends on the problem. Assessing whether your data is sufficient is the first step, and we will say if it isn't.
Who is the training for?
We run sessions for leadership teams, business users and engineers, and tailor the content and depth to each group.
Is it online or in person?
Either. Sessions can run live online or on site, depending on your team and location.
Insights
Related reading
- EngineeringAI in production: the checklist most teams skip.Getting a demo working is the easy 20%. Here's what separates a prototype from something you can trust running unattended in front of customers.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
- 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
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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