AI Consultancy & Strategy
AI readiness audits
Before anyone writes a line of AI code, we map how work actually moves through your business, find where time and money leak, and rank the places AI would pay off. You get a clear, costed shortlist instead of a pile of ideas.
Where AI pays off
Ranking- 1Invoice processingStart here
- 2Monthly reporting
- 3Customer support
- 4Document review
- 5Lead qualification
Is this for you?
You’ll get value from this if…
- Leadership wants an AI plan but nobody agrees where to start
- You have tried a pilot or two and nothing reached production
- Teams spend hours on repetitive, rules-based work every week
- You need a straight answer on whether AI is worth it for you yet
Benefits
What changes for your business
Spend on what pays
Budget goes to the use cases with the clearest return, not the loudest idea.
Fewer dead pilots
Feasibility, data and risk are checked before build, so pilots are chosen to reach production.
A plan everyone can read
A plain-language report leadership, finance and IT can all act on.
What you get
What we deliver
Process map
How the key workflows run today, where the hand-offs are and where effort is lost.
Ranked opportunities
Each AI use case scored on value, effort and risk, so the order of work is obvious.
Build plan and costs
For the top opportunities: the approach, the systems it touches and what it would take to build.
Data and risk review
What data each use case needs, whether you have it, and the security and compliance points to plan for.
How it works
From first call to running in production
- 01
Interviews
Short sessions with the people who do the work, not just the people who manage it.
- 02
Map
We document the workflows, systems and data involved.
- 03
Score
Every candidate use case is ranked on value, effort and risk.
- 04
Report
A written report with the shortlist, the plan and our honest recommendation.
Example applications
What this looks like in practice
Typical applications of this service. Illustrative, not client case studies.
- 01Finding which back-office processes are worth automating first
- 02Checking whether customer-support data can power an assistant
- 03Pressure-testing a vendor's AI proposal before you sign
- 04Building the business case for an AI budget
Technology
Tools we work with
- Process mapping
- Data audits
- Value / effort scoring
- Security & compliance review
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 if AI is not a good fit for us?
Then the report says so, in writing, and explains why. Not every process benefits from AI.
Who from our team needs to be involved?
A sponsor who owns the outcome, plus short sessions with the people who do the day-to-day work in the processes being reviewed.
What happens after the audit?
You can take the plan to any team, build it in-house, or have us build the top opportunities with you.
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
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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