Ten days to a straight answer: should you build this, and what will it cost?
One use-case. One team. Two weeks. You leave with feasibility, architecture, risks and a fixed-price proposal. The fee is credited against any build that follows.
What is an AI discovery sprint?
An AI discovery sprint is a fixed ten-working-day engagement that takes one use-case from idea to a go or no-go decision. Eazyware's Sprint Zero benchmarks candidate models on a sample of your data, sketches the architecture, estimates build and running cost, and ends with a fixed-price proposal. The fee is credited to the build.
| Service line | AI Strategy & Discovery |
|---|---|
| Engagement | Fixed-price program (Sprint Zero) |
| Duration | 10 working days |
| Starting price | $3,250 (fixed) |
| Typical range | Fixed · credited to next build |
| Deliverables | 5 listed below |
| Delivered from | Bengaluru, India (IST, UK and US East hours) |
| Code ownership | Client owns code, infrastructure, prompts and documentation |
What problem does it solve?
You have an idea and a rough budget. What you don't have is confidence: will the model be accurate enough, is the data usable, what will it actually cost to run. Guessing costs months.
How do we approach it?
Sprint Zero is ten working days with a single question: should you build this, and what would it cost. Days one and two are about access and framing: we agree the success metric, get a representative sample of your data, and write down what would make us say no. Days three to six are benchmarking, where we run two or three candidate models against your sample and measure accuracy, latency and cost per task rather than reading vendor pages. Days seven and eight turn that into an architecture sketch and an estimate that separates build effort from monthly running cost. Days nine and ten are the readout: a short memo, a walkthrough with your team, and a fixed-price proposal if the answer is yes.
What do clients use it for?
- Validate one AI idea before budgeting
- Benchmark models on your own documents or data
- Get a fixed-price proposal for an internal business case
- De-risk a vendor claim with an independent test
Is it the right fit?
Good fit when
- Founders and product leads with one clear idea
- Enterprises needing a fast, defensible go or no-go
- Teams preparing a build budget
Probably not when
- Multi-use-case roadmaps (use AI Strategy)
- Teams that need working software at the end (use ProofRun)
What do we build?
- Frame the problem and the success metric
- Sample your data and test it against 2–3 candidate models
- Sketch the architecture and integration points
- Estimate build effort and monthly inference cost
- Identify the top three risks and how we would de-risk them
What you get
- Feasibility memo: go, no-go, or go with changes
- Architecture sketch
- Model benchmark on your sample data
- Fixed-price, fixed-scope build proposal
- Risk register
How does the engagement work?
- 01
Days 1–2: kickoff and data access
- 02
Days 3–6: benchmarks and architecture
- 03
Days 7–8: costing
- 04
Days 9–10: readout and proposal
What does good look like?
You leave with a clear decision and the evidence behind it. If the answer is go, you have a proposal with a price, a date, a scope you can defend internally, and a list of the three risks we will retire first. If the answer is no or not yet, you have the reasons in writing, usually a data-readiness gap or an accuracy ceiling, and a cheaper path to close it. Either way the sprint fee is credited against the build, so the discovery is never money spent twice.
How does it compare?
| Eazyware | Typical agency | In-house hire | |
|---|---|---|---|
| Time to first result | 10 working days | 6–12 weeks of discovery before a proposal | 3–6 months to hire, then ramp |
| Pricing model | Fixed price | Time and materials, open-ended | Salaries, tooling, management overhead |
| AI depth | Multi-model, evals, cost routing, observability as standard | Often a single vendor API and a prompt | Depends entirely on who you can hire |
| Ownership | Client owns code, infra, prompts and docs | Sometimes retained or licensed back | Owned, but concentrated in one or two people |
| After launch | Care Plans with SLA and AI add-on | Change requests at hourly rates | Ongoing headcount whether or not there is work |
Which pitfalls do we design around?
The failure mode of discovery is that it becomes a sales process rather than an engineering one. We avoid that by benchmarking on your data, not a demo set, and by writing down the no-go criteria before we start. We also refuse to estimate without a sample; an estimate built on assumptions about your documents or your database is a guess with a decimal point. And we keep the scope to one use-case, because a sprint that tries to answer five questions answers none of them well.
What do we measure?
Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.
- Model accuracy on your sample
- Estimated build effort and monthly inference cost
- Top risks with mitigations
Which technologies do we use?
- OpenAI
- Anthropic
- Open-weight models
- Your sample data
Who does the work?
One senior AI engineer full time for ten days, an architect for the design and costing days, and the principal who will lead the build if it goes ahead, so the people who estimate are the people who deliver.
What do you need to bring?
One use-case, a representative data sample (anonymised is fine), an hour on day one and an hour on day ten. Someone who can say what success means in a number. If the idea touches an internal system, read access to a staging copy shortens the architecture work.
Frequently asked questions
What if the answer is no?
You'll know in ten days for the price of a sprint instead of six months for the price of a build. We'll tell you plainly.
Is the fee credited?
Yes, 100% credited against any build started within 60 days.
Do you need production data?
A representative sample is enough. Anonymised is fine.
Where does this fit?
AI Discovery Sprint is part of our AI Strategy & Discovery line. See all pricing or talk to an engineer.