AI MVP Development
Production AI in 4–8 weeks — not 4–8 months.
Your idea doesn't need a year-long roadmap. We build AI-native MVPs at startup speed — concept to launch in 4–8 weeks. Fixed scope, fixed price, real software you ship to customers.
From $45,000 · Eval harness from day 1 · 30-day post-launch support included
The 4–8 week build
What ships every week.
01 · Week 1
Discovery sprint
Domain interviews, success metrics, throwaway prototype on the riskiest assumption. Output: fixed-price proposal you sign or walk away from.
02 · Weeks 2–3
Core build
Senior engineers, daily async standups in your Slack, weekly demo. The product takes shape on real data — not slideware.
03 · Week 4
AI integration + eval
Wire in the agentic / RAG layer. Eval harness comes online with golden test set. Prompt-injection defense baked in.
04 · Weeks 5–6
Polish + launch
UI refinement, performance tuning, observability dashboards, deployment. You're live with a product, not a demo.
AISD vs. typical agency MVP
Same product. Different cost and time.
| Factor | AISD | Typical agency |
|---|---|---|
| Timeline | 4–8 weeks | 4–8 months |
| Starting price | $45,000 | $150,000–$500,000 |
| AI-native architecture | yes | bolt-on |
| Eval harness from day 1 | yes | rare |
| Post-launch support | 30 days included | extra fee |
| IP + source code | yours from day 1 | varies |
Featured case study · Fintech
AI-assisted fintech MVP shipped at 60% lower cost, 2× faster.
Original quote was $150K–$200K and 4 months. We delivered for $80K in half the time, with production-grade architecture.
Read the full case study →Outcome
60%
cost reduction vs. original quote
Industries we ship MVPs in
Featured MVP case studies
Frequently asked
Common questions.
How long does it take to build an AI MVP?
Most AI MVPs at AISD ship a usable version in 4–8 weeks. Week 1 is a discovery sprint. Weeks 2–6 are the build, with weekly demos and a working version by week 4. Weeks 7–8 harden, document, and hand off.
What does an AI MVP cost?
AISD AI MVPs typically range $45,000–$120,000 depending on scope. Drivers: number of model integrations, complexity of retrieval/data layer, custom UI surface area, and compliance requirements. We publish indicative bands on the pricing page so buyers can budget before the first call.
How do you ensure AI features are reliable in production?
Five layers: an offline eval harness with golden test sets run on every PR; confidence thresholds and structured-output validation that gate downstream side effects; runtime observability — every model call logged with inputs, outputs, latency, cost; circuit breakers and deterministic fallbacks for every model dependency; and a weekly review ritual where prompt regressions get caught before they become incidents.
How do you handle hallucinations in production AI?
Hallucinations are the wrong mental model — the issue is ungrounded generation. Mitigations applied in layers: ground every factual claim in retrieved sources, returned alongside the answer; structured outputs with schema validation; confidence scoring with thresholds — low-confidence answers are escalated, not surfaced; human-in-the-loop checkpoints for high-stakes actions; continuous eval against a golden set.
What is AISD's discovery sprint?
A paid 1–2 week engagement to scope a project with rigor before committing to a full build. Outputs: a written scope doc with success metrics, a technical architecture, a 1-week throwaway prototype that proves the riskiest assumption, and a fixed-price quote for the build. Typical price: $8,000–$18,000. Customers who run a discovery sprint with us are 3× more likely to ship on time and budget than customers who skip it.
How does pricing work — fixed-price, T&M, or retainer?
All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.
How are deliverables handed off?
Every engagement ends with a handoff package: production deployment, architecture documentation, eval harness with golden test sets, observability dashboards with documented thresholds, on-call runbook, model upgrade procedure, and a recorded walkthrough. Plus a 30-day post-handoff window for questions and clarifications at no cost.
How is success measured on an engagement?
Success metrics are defined in writing during scoping and reviewed monthly. Project engagements measure: feature shipped on date, eval-harness pass rate, target business metric (e.g. 'auto-resolve rate ≥35% on customer-support tickets'). Staff augmentation engagements measure: PR throughput, code-review acceptance, and customer-side satisfaction. We do not measure success in hours billed, lines of code, or generic velocity points.