Create
MVP Full Build
From validated concept to deployable AI product.
Discovery → Iterative Build → Delivery & Knowledge Transfer. We take your concept from validated idea to deployable MVP — with your team trained and ready to iterate independently. Includes V2 backlog handover.
How the build works
Three phases. One deployable MVP.
Discovery
We go deep on your use case, your users, and your constraints. Define the MVP scope, select the technology stack, establish the architecture, and create the sprint plan. Nothing is built until the foundation is solid.
Iterative build
We build in short cycles with your team alongside us. Regular demos, regular feedback, regular course corrections. Your team isn't watching from the side — they're learning as the product takes shape.
Delivery & knowledge transfer
Final delivery, full documentation, team training, and the V2 backlog handover. Your team can iterate independently from day one after we leave. That's the goal — and we design every week towards it.
Why handover matters
You own it. We make sure of it.
The V2 backlog is the difference between a handover and an outcome. We don't just deploy a product and walk away — we hand over a prioritised backlog of everything the MVP proved you should build next, plus the documentation and training your team needs to execute it independently.
Most clients have their first independent sprint within two weeks of handover. That's the measure of a real knowledge transfer — not a document, not a demo. A team that builds.
You walk away with
Four deliverables. Yours permanently.
- Deployable AI MVP — live, tested, and production-ready
- Team training & knowledge handover — your team can iterate from day one
- Full V2 backlog — prioritised roadmap for what to build next
- Architecture & operational documentation — complete technical record
Timeline
4–8 weeks. What drives the range?
A well-scoped, well-defined use case with good data access can be delivered in 4 weeks. More complex integrations, multiple user types, or iterative user testing cycles push towards 6–8 weeks. We define the timeline in Discovery — no surprises mid-build.
Ideal starting point
Come with a validated concept.
The AI MVP Full Build is designed for teams who have already validated the problem worth solving — ideally through an Prototype Sprint or their own internal testing. We don't do discovery-from-zero in this engagement.
We had a validated idea and no AI team. We needed to move fast and we needed to own the output. Anteligen built the MVP in five weeks — and by the end, our internal team was running sprints independently. The V2 backlog alone was worth the engagement. We knew exactly what to build next and why.
Common questions
FAQ
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No. That's the point. Anteligen brings the technical expertise — you bring the domain knowledge and the use case. We do require a product owner or project lead from your side to be available throughout the engagement (typically 30–60% of their time during the build). But you don't need to have AI engineers, ML specialists, or data scientists in-house before we start.
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The V2 backlog is a prioritised list of everything the MVP proved should be built next — features, improvements, technical debt, and integration opportunities. Each item is scored for value and effort, with a recommended sequencing. It's not a wish list — it's a build plan your team can execute independently. We also include the rationale for what we chose not to include in V1, so future decisions have context.
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Yes. Some clients continue with an AI Product Coach retainer after handover — especially if the product team is small or the V2 roadmap is ambitious. The coaching engagement is lighter-touch and focuses on keeping the team's AI practice strong as the product scales. We design it so it's optional, not required — independence is the goal.
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We're stack-agnostic. We select the technology that best fits your use case, your team's ability to maintain it post-handover, and your organisation's existing infrastructure. For AI components, we work with the leading foundation models (Claude, GPT-4o, Gemini) and orchestration frameworks depending on the use case. Stack decisions are made transparently in the Discovery phase with full rationale documented.
Every transformation starts with a free AI Maturity Assessment.
30 minutes. Senior consultant. No commitment. Results visible within weeks.
Book my free AI Maturity Assessment