AI-Native MVP from Day One
The LLM is the product, not a feature bolted on top
An MVP where the core value is AI: without the LLM the product doesn't make sense. It generates content, assists decisions, automates workflows, holds conversations or analyzes data. The difference vs a regular MVP with AI added on top is that architecture, eval, observability and cost all revolve around the model here.
Who it's for
- Founders building an LLM wrapper with real value and clear differentiation
- Vertical AI products: legal, medical, design, marketing or sales
- AI-first companies replicating manual workflows with AI
- Post-OpenAI or Anthropic startups looking to capture early usage
What's included
- Discovery and core AI task definition, concrete and measurable
- MVP-WEB base stack with auth, DB and deploy
- LLM orchestration with Anthropic SDK, OpenAI and Vercel AI SDK
- Versioned prompt engineering
- RAG over user or global knowledge base when relevant
- Structured tool use and function calling
- Streaming UI for chat and content generation
- Memory layer and token cost tracking
- Basic eval suite with a golden dataset
- Guardrails and content moderation
- Pricing model with free, paid and usage-based via Stripe metering
- LLM observability with Langfuse plus onboarding that proves value in under 60 seconds
Metrics that move
What you should expect to track and improve.
Time-to-aha in seconds until the first wow moment
Free to paid conversion
Cost per user in USD per LLM MAU
Per-user unit margin
Generations per user and D7/D30 retention
Product NPS
Your idea is AI-native: the LLM is the product. MVP-AI-NATIVE delivers the full product in 8 weeks: streaming UI, versioned prompt engineering, RAG if you need it, eval, observability, cost tracking and metered pricing. You launch with a unit margin that doesn't bleed from day one.
Stack
Common questions
Questions we get during discovery.
Related SKUs
Natural next steps or complementary services.
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