How much does the discovery sprint cost?

It's a fixed price, scoped on a free call. We don't publish a rate card because the price depends on how many systems, teams, and workflows are in scope — after the discovery call you get a written proposal with a single fixed fee, the exact deliverables, and the dates, so there are no open-ended hourly surprises.

What do we need to provide?

A clear use case, a decision-maker who can join weekly demos, and access to a representative sample of the real data — with sensitive fields masked where needed. We agree the details before the sprint starts.

Who owns the prototype code?

You do. The code lives in a repository you control, and the evaluation set and roadmap are yours to use with any team.

What if the prototype doesn't meet the success criteria?

The evaluation report shows where and why it fell short and what would need to change. Stopping after the sprint, adjusting the approach, or choosing a different use case are all legitimate outcomes.

AI discovery sprint

A fixed-price AI discovery sprint: a working prototype in weeks

The AI discovery sprint is a fixed-price, four-to-six-week proof-of-concept engagement. You bring one well-defined use case; we build a working prototype against your real data, measure how well it performs, and deliver a production roadmap — so the decision to invest in a full build is based on evidence instead of a demo.

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Why a sprint before a full build

AI projects tend to stall for predictable reasons: the data turns out messier than expected, the model is accurate on clean examples but not on the real ones, the integration is harder than it looked, or the running cost doesn't make sense at volume. A discovery sprint surfaces those problems in weeks, at a fixed price, before you've committed to a production budget.

It works best when you already know the use case — support ticket triage, document extraction, an internal knowledge assistant, a lead-qualification agent, a forecasting model. If you're not there yet, start with an AI readiness audit to find and rank the opportunities first.

Who the sprint is for

The sprint suits product and operations leaders who need a go or no-go decision on a specific AI idea, technology teams asked to validate a vendor's claims against their own data, and founders who need a working prototype to show stakeholders. It needs a sponsor who can make decisions weekly and someone who knows the data — the sprint moves fast, and waiting on access or approvals is the most common thing that slows it down.

What the sprint includes

The scope is agreed in writing up front: one use case, the success criteria, the data we'll use, and the systems the prototype may read from. Within that scope the sprint covers:

Use-case definition with measurable success criteria
Data access, sampling, and a data-quality assessment
Model and approach selection, including build-versus-buy
A working prototype running on your real data
An evaluation set and scored results against the success criteria
Cost-per-task and latency measurements
Security and privacy review of the proposed design
Weekly demos so you see progress and can steer

Timeline

Sprints run four to six weeks depending on data access and integration complexity. Each week ends with a demo of working software.

Week 1 — Frame and baseline

We confirm the use case and success criteria, get access to data, build an evaluation set from real examples, and measure how the task is done today so there's a baseline to beat.

Weeks 2–4 — Build and evaluate

We build the prototype in short iterations, scoring every version against the evaluation set. When an approach isn't working, you hear about it that week — not at the end.

Final weeks — Stress-test and plan production

We test edge cases and failure modes, measure cost and latency at realistic volume, and write the production roadmap. The sprint closes with a readout and a live demo for stakeholders.

Deliverables

You receive a working prototype — running code in a repository you own, not slides — along with an evaluation report showing how it performed against the agreed criteria, including where it failed. The production roadmap covers the architecture for a production version, the integrations and guardrails it needs, the security and monitoring work, an estimate of running costs, and a phased build estimate.

Sometimes the honest finding is that the use case isn't viable yet. When that happens, the report explains exactly why — data, accuracy, cost, or integration — and what would need to change. That's still a good outcome: you learned it in weeks, at a known price.

From prototype to production

If the prototype meets its criteria, the natural next step is a production build with our AI agent development or AI development services team, following the roadmap from the sprint. Once it's live, managed AI services can keep it monitored, evaluated, and cost-controlled. You're equally free to take the code and the roadmap to your own engineers. Every engagement starts with a free discovery call, where we learn your goals, systems, and constraints before proposing a scope.

Frequently asked questions

A fixed-price, four-to-six-week proof-of-concept engagement focused on one use case. It produces a working prototype on your real data, an evaluation report, and a production roadmap with a build estimate.