AI Integration Sprint
I add one AI capability to your existing software and take it all the way to production. Two weeks.
- Duration
- 2 weeks
- Engagement
- Sprint
What you get
- One concrete capability integrated end to end and running in production
- A connection into your current system — no migration, no platform change
- Cost measurement: what each call costs and what the monthly bill will look like
- A designed failure path for wrong answers, including a human approval step
- Source code and documentation are yours; you are not locked to me
Ideal for
- Teams with working software and no AI capability yet
- Anyone doing repetitive classification, summarising or matching by hand
- Companies that tried AI before and never got past the demo
Not included
- Training a model from scratch — I use existing models
- More than one capability at a time; a sprint is deliberately narrow
- Rewriting your existing system
- Long-term maintenance, which is a separate engagement
Why a sprint covers exactly one thing
Most AI projects die because they promise too much. Picking one capability and taking it to production in two weeks is worth more than leaving ten capabilities at demo stage after six months. One thing that works beats ten things that do not.
What a good sprint topic looks like
- Routing an incoming request to the right team
- Extracting structured data from a free-text order note
- Making supplier quotes comparable
- Reducing a long report to the summary a decision-maker will actually read
What they have in common: done by hand, repeated often, and tolerant of the occasional error.
What a bad sprint topic looks like
Anything where an error is not tolerable — calculating invoice totals, producing legal text, making medical or financial decisions. If you ask for one of those, I will tell you so and decline.
Are two weeks really enough
For a single capability, yes — because I am not training a model, I am connecting existing ones to your data. Most of the time goes not into writing code but into answering “what happens when it is wrong”. No AI feature should reach production before that question is settled.
What you end up with
A feature your users actually use, and a cost table — because a system with an unpredictable bill does not count as production.