Audit & optimization
I find where AI truly creates value, and cut the bill on what's already running.
Adopting AI without a strategy is expensive both ways: missed opportunities, and token bills that spiral. And before we even talk about the model, I check the state of your data: scattered, incomplete or out of date, it sinks the best project. I audit your processes AND your data, put numbers on the profitable cases, and optimize what's already running: often 30 to 70% lower costs, at the same quality.
Key facts
-30 to -70%
on your AI bill, at the same quality
Step 0
the state of your data, checked before we code
48h
for a first audit readout
What I build
AI audit & strategy
Your processes put under the microscope to find the use cases with real ROI, and honestly rule out the ones that aren't worth it.
Audit · Quantified use cases · ROI roadmap
Cost optimization
Model selection, caching, prompt architecture, batching: the LLM bill goes down without losing quality.
30 to 70% less · Caching · Batching · Model selection
Data readiness
Before building anything, I look at your data where it lives: is it complete, up to date, gathered, usable? Building AI on data that isn't ready is building on sand. I tell you what's blocking and where to start, without a big pointless overhaul.
Data readiness · Data quality · Feasibility
The promise
AI at the right price, where it pays off.
Before / after
An AI project launched on messy data
You know whether your data holds up, before the first line of code
AI everywhere except where it pays off
A roadmap prioritized by return on investment
The stack
Token audit
Prompt engineering
Caching
Batching
Observability
Evaluation
Straight answers
What does an AI audit include?
An inventory of your processes, AI use cases quantified by ROI, the traps flagged early (data, costs, compliance) and a prioritized roadmap. First readout within 48 hours, full report within days.
How do you cut an LLM bill by 30 to 70%?
Caching answers and stable context, the right model for each task, leaner prompts, batching bulk jobs. I first measure where the tokens go, then pull the levers in order of payoff.
What if the audit concludes AI is useless for me?
I tell you, and you save yourself a failed project. It happens. An honest no after a few days of audit beats a letdown after months of building.
Does the audit commit me to anything afterwards?
No. The report and the roadmap are yours: you can build with me, in-house, or with someone else. The audit stands on its own.
What if my data isn't ready?
It's the most common case, and there's nothing shameful about it. The audit says it plainly: what's usable right away, what needs cleaning up or gathering first, and the shortest path to get there. We don't build on sand. And your data stays with you at every step.
Contact
Ready to go from demo to production?
Reply within 24 hours · first conversation free, no strings attached.