MVP TIMELINE
10–16 weeks
from first discovery call to deployed, tested MVP in production
↓ faster than internal builds which average 9–14 months for equivalent scope
MVP BUILD COST
$15K–$60K
depending on scope, data complexity, and integration requirements
↓ typically 3–5× less than equivalent in-house team build
IN-HOUSE VS AGENCY SPEED
4× faster
agency build vs comparable in-house team for AI product MVPs
↓ no hiring, no ramp-up, no infrastructure set-up overhead
FEATURES USED AT 6 MONTHS
30%
of originally scoped features actually used in first six months post-launch
↑ validates minimal scoping — the rest is never needed
Where AI Products Fail Before Launch
CB Insights research consistently finds that building before validating is the primary cause of failed AI products. Three failure modes account for the majority of cases:
Building before validating. The assumption that users want what founders have built. AI products require user behaviour data to improve — and without early validation, they are often solving a problem users have worked around and are no longer willing to change behaviour to fix.
Over-scoping the MVP. The 30% feature usage figure is consistent across product categories. MVP scope should be ruthlessly minimal. Every additional feature added before launch extends timeline, increases cost, and reduces the probability of on-time delivery.
Ignoring data requirements. AI products depend on data. The quality, volume, format, and accessibility of that data should be assessed before a line of code is written. Projects discovered data inadequacy at week 6 are often unrecoverable without a complete restart.
The most valuable outcome from a discovery phase is occasionally the decision not to build — because the data does not support it, the market does not validate it, or a simpler solution exists. A £5,000 discovery that prevents a £40,000 failed build is the highest-ROI outcome on the project.
The Four Stages from Idea to AI Product
| Stage | What Happens | Timeline | Deliverable |
|---|---|---|---|
| 1 — Validation | User interviews, data audit, problem/solution fit, kill-or-build decision | 1–2 weeks | Validation report + go/no-go recommendation |
| 2 — Scoping | Minimum viable feature set, data architecture, integration map, cost estimate | 1 week | Fixed-scope spec document with cost and timeline |
| 3 — Build | Sprint-based development, weekly demos, data pipeline, model integration, testing | 6–10 weeks | Tested, documented AI product MVP |
| 4 — Launch | Production deployment, user onboarding, 30-day monitoring, iteration plan | 2–3 weeks | Live product + 90-day roadmap from real usage data |
What We Do Differently
Two practices distinguish how Agency Company approaches AI product builds. First, discovery is fixed-scope and fixed-cost — you know exactly what you are buying before signing. It includes a kill option: if validation reveals the product should not be built, we tell you that and the engagement ends. No pressure to proceed to a build that is unlikely to succeed.
Second, we scope MVPs to the minimum viable feature set based on user research, not wishlist. The features that are not built in the MVP are documented and prioritised for later iterations based on actual user behaviour data. This approach consistently produces better products faster at lower total cost than full-scope initial builds.
Sources
- CB Insights: Why Startups Fail — AI Product Edition 2024 (cbinsights.com)
- Product Hunt: AI Product Launch Benchmarks 2024 (producthunt.com)
- Lean Startup methodology benchmarks and practitioner data