01 / FEATURE
Feasibility sprint
Can this actually work on your data? We run the experiment end to end and write up what we learn.
AI Sprint, a working prototype in 2 to 4 weeks.
No discovery theatre. No slide marathons. Two weeks in, you have a working AI prototype on real data that your team can pressure-test.
What it is
Three things fit in a sprint. Pick one.
The AI Sprint is a fixed-scope, fixed-price, fixed-timeline engagement that takes one well-formed use case from idea to a working answer in two to four weeks. It runs on your data inside your environment from week one. The deliverable is a functioning thing, an evaluation report against measurable criteria, and a written recommendation, including, sometimes, the recommendation not to build it. A sprint isn't a mini-project. It's designed to produce one demonstrable answer, so you can decide whether to invest, adjust, or walk away.
What you get
02 / FEATURE
Prototype sprint
A working thing, in your environment, connected to real data, ready for user testing.
03 / FEATURE
Architecture sprint
A buildable, governed design for a larger initiative, before you commit to a full build.
04 / FEATURE
Two to four weeks
Fixed scope, fixed fee, fixed timeline. No scope creep, no change orders billed on Friday afternoon.
05 / FEATURE
Your team alongside
Two of your people pair with two of ours. Knowledge transfer is built in.
06 / FEATURE
Decision-ready output
Working code, written memo, recorded demo. Everything your board needs to say yes or no.
The Framework
How it works
A two-engineer EIS pod runs the sprint, embedded in your Slack and your repo. Your team contributes a product owner, a data steward, and a domain expert. That's the team, no steering committee, no weekly status decks.
PHASE 01
FRAME
Kickoff Monday. By Friday we've signed off the success metric, audited the data, mapped the integration surface, and stood up the dev environment. If the data isn't ready, we say so, and the sprint pauses until it is.
Deliverable
Sprint brief + eval rubric
PHASE 02
BUILD
End-to-end thin slice. Ugly, partial, but real. It runs on your data and produces output you can read. We show it Friday, the first checkpoint where the project can be killed cleanly.
Deliverable
Thin-slice build + risk log
PHASE 03
ITERATE
Eval-driven development. Every change is measured against the week-one rubric. Prompts, retrieval, fine-tunes, whatever the data tells us. Cost and latency tracked alongside accuracy.
Deliverable
Iteration log + scorecards
PHASE 04
DECIDE
Final eval, written recommendation, handover. Three possible answers, build it, kill it, or run a second sprint with this scope adjustment. We do not optimise for 'build it'.
Deliverable
Working prototype + go/no-go memo
Real-world example, regional logistics operator
Two AI Sprints in parallel, one for invoice extraction, one for an inbound-call triage agent. Same cadence, same eval rubric standard. Different answers, fast.
Sprint A, invoice extraction
Shipped to production at week four. Accuracy on a held-out month of invoices.
94.2%
Sprint B, call triage
Killed at week two with evidence. Avoided spend on a six-figure vendor contract.
USD 380K
Time to clean answer
From kickoff to a defensible go/no-go memo on both initiatives.
4 weeks
The killed sprint was more valuable than the one that shipped. We were three weeks from signing a six-figure vendor contract.
VP Operations · regional logistics operator
Where this connects
Pairs with Strategy & Governance, strategy chooses what to build, the sprint proves it
Graduates into FORGE for the full production build
Successful sprints feed directly into AI Launchpad, Accelerate or Deploy
Killed sprints are a deliverable too, written and signed
Book a sprint scoping call
30-minute call to decide whether a sprint fits, and which of the three shapes is right for you.
Scope a sprint
30 minutes · reply within 1 business day