AI-Integrated MVP Development

The 8-week build covers discovery and architecture design (Weeks 1-2), UI/UX wireframes and frontend scaffold (Weeks 3-4), core backend with AI integration including model selection, prompt engineering, and API wiring (Weeks 5-7), and QA, deployment to production infrastructure, and handoff documentation (Week 8). The MVP is a working, deployed product, not a prototype. It handles real user inputs, connects to your AI model of choice (OpenAI, Anthropic, Google, or open source), and runs on infrastructure you own.

We place an abstraction layer between your application and the underlying AI provider. Whether you use OpenAI GPT-4o, Anthropic Claude, Google Gemini, or an open-source model, the model is called through a provider-agnostic service layer. If model pricing increases or you want to switch providers later, you change one service layer, not the entire application. We also store prompts and system instructions in your own database rather than hardcoding them, so you can iterate on AI behaviour without a code deployment.

Model output quality is assessed in Week 1 alongside architecture planning. We prototype the core AI interaction with your actual data samples before committing to a model. If output variance is too high for your use case, we evaluate fine-tuning, retrieval-augmented generation (RAG) using your own data, or a more constrained prompting strategy. You don't discover the AI doesn't work well enough at Week 7.

Payment is structured in two milestones. The discovery and architecture phase ($2,000) produces a technical specification, Figma wireframes, and a sprint plan, all of which you keep regardless of whether you continue. The main build phase covers the 6-week development sprint and produces the working, deployed MVP. You can exit after any milestone with full ownership of everything produced to that point. Code, designs, and documentation are transferred immediately.

The architecture is designed for growth from Sprint 1. The frontend, backend API, AI service layer, and data storage are independent modules. Scaling the AI layer doesn't require touching the application code. Database schema decisions account for the query patterns at 10,000 users, not just the initial 100. All architectural decisions and trade-offs are documented so the next engineering team has the reasoning behind each choice, not just the code.

Yes. We sign NDAs before any discovery session. Sensitive product ideas, proprietary data, and business logic stay confidential throughout the engagement and after handover. We have delivered AI MVPs for pre-seed founders in the US, UK, and Australia where IP protection was a condition of the project. NDA is standard, not optional.

An AI MVP is a minimum viable product where AI, typically a large language model, a RAG pipeline, a computer vision model, or a voice AI system, is the core feature, not an add-on. The goal is to validate that the AI solves a real problem for real users, at the minimum scope and cost required to generate that signal. Unlike a standard MVP, AI MVPs require a data strategy, an output validation approach, and an architecture that manages model costs from day one. For a detailed walkthrough, see our step-by-step guide to building an AI MVP.

An AI prototype (or POC) validates technical feasibility and gives investors something real to evaluate. It runs in a demo environment, not production infrastructure. An AI MVP is production software: deployed, multi-tenant, and in the hands of real users. You start with a prototype when you're uncertain whether the AI architecture is feasible. You start with an MVP when you're ready to test whether real users want the product.

We work with GPT-4o, Claude 3.5, Mistral, and Llama 3 depending on your latency, cost, and privacy requirements. For multi-step agent workflows we use LangChain and LangGraph. For RAG pipelines we use Pinecone, Weaviate, or Chroma depending on scale and query patterns. For voice AI we use Twilio, ElevenLabs, Deepgram, and Vapi. We recommend the stack based on your use case, not based on which APIs we prefer to work with.

Yes. The biggest cost lever in AI MVP development is scope, specifically avoiding fine-tuning a model when a well-prompted GPT-4o will achieve the same result, and avoiding a RAG pipeline when a simpler retrieval approach will do. The discovery session is where we find the minimum AI build that tests your core assumption. We've taken founders from concept to investor-ready AI MVP at price points that fit pre-seed budgets.