AI MVPs for early-stage startups.

AI MVP development for founders who need to prove the product works: LLM features, agents and the web or mobile app around them, without a codebase you throw away at Series A.

Start an AI project ↳ AI use cases

What makes startup AI hard.

The startup AI challenges we design for before any code is written, so the AI holds up with real users, real data and real auditors.

Runway is the constraint

Every sprint tests a hypothesis. We scope to the smallest slice that answers it, with evals so “the AI works” is a measured claim.

Demo vs. production

A prompt that wows in a demo breaks on real users. Guardrails, fallbacks and cost per request are sized before launch.

Avoiding the rewrite

Boring, well-supported tech, clean boundaries and swappable model providers mean the MVP grows instead of being replaced.

Startup AI solutions we build

The AI use cases we build for startup teams, and the software platforms they run on. Each starts from a measurable goal, passes an eval gate before launch and reaches users through a gradual rollout.

The model work comes from our LLM and AI agent development team, tested by QA and AI evaluation. For results in production, browse our AI case studies.

Compliance we plan for

SOC 2 readiness
GDPR

✓AI-first MVPs and prototypes
✓LLM features, agents and RAG
✓Web and mobile v1 products
✓Evals, analytics and experiment tracking
✓Cloud foundations sized for early load
✓Technical due-diligence support

Startup AI development services.

The WAMO Labs services most startup AI engagements draw on, from LLM integration to the platform around it.

AI integration

Our AI integration services put LLM applications, AI agents and RAG systems into the products and workflows you already run. Measured by evals, guarded in production, with a cost per request you can predict.

Custom web

Custom web application development for fast, AI-ready platforms that scale with traffic, data and your team. Copilots, smart search and clean APIs are part of the architecture, not a bolt-on.

Mobile apps

Mobile app development for iOS and Android, with native polish and AI features users actually use: on-device ML, LLM assistants, voice and camera intelligence, all backed by solid APIs.

Questions about startup AI projects.

Timelines, cost drivers and compliance, answered plainly. More on how we build and evaluate AI in our AI engineering insights.

Which startup AI use cases do you build?

Startup AI projects we build include AI-first MVPs and prototypes; LLM features, agents and RAG; and web and mobile v1 products. Each one ships with a versioned eval set, guardrails and a gradual rollout, on the platform work listed above.

How long do startup AI projects take?

Most engagements start with a four-week immersion. By week 3 a first slice is live behind a feature flag for a small share of users, evaluated and instrumented. By week 4 you have a quarter-by-quarter roadmap with named owners. Full timelines depend on scope, integrations and compliance review.

What drives the cost of startup AI solutions?

Four things: the systems we integrate with (model providers, analytics and your app stack), the compliance scope (SOC 2 readiness and GDPR), how much evaluation and human review the use case needs, and inference volume, because cost per request compounds with usage. We size all four during the four-week immersion, so you know what it costs to keep going before you commit to a roadmap.

How do you handle compliance and data privacy in startup AI?

We plan for SOC 2 readiness and GDPR from the first sprint and decide which data may ever reach a model provider before anything is built. Every AI feature ships with access controls, an audit trail and human review wherever a wrong answer carries real risk.

Building an AI startup?

Tell us the AI use case or product you're working on. We respond within one business day, with a real human and a real opinion.

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