AI demo needed in 48 hours.Built. Deployed. Investor-ready.

A production-ready AI MVP not a prototype is achievable in 30 days (Sprint Pack: 10 Full Day sessions). What is deliverable in that time: a LLM-powered application with a working user interface, a RAG pipeline connected to your knowledge base (PDF ingestion, vector storage in Pinecone or Weaviate, semantic search), user authentication, conversation history stored in a database, basic usage limits per user, and deployment to a cloud environment your users can access. What requires more time: complex multi-step agent systems, extensive fine-tuning, and high-scale infrastructure that needs load testing. PM scopes precisely what is achievable by the end of session 1 you know the exact deliverable before any code is written.

Yes fundraising-ready AI demos are one of our most common engagements. What makes a demo compelling for investors: a working product (not slides or mockups) that demonstrates the core AI capability on real data relevant to your domain, a clear user flow showing the before-and-after value the AI creates, response quality that is consistently accurate on the demo queries you will walk through, and infrastructure that does not visibly lag during the demo (response under 3 seconds). PM works backward from your pitch date to scope the sprint: if you have 2 weeks, what is the minimum viable demo? If you have 30 days, what additional features strengthen the narrative? Engineers focus on demo-path quality first, then breadth.

Non-technical founders successfully ship these AI products in 30 days with QuickHire: document Q&A (users upload PDFs, the system answers questions from them using RAG), AI-powered search that replaces keyword search on a product catalogue or knowledge base with semantic search, an AI assistant for a specific vertical (legal, real estate, healthcare) that answers domain-specific questions from a curated knowledge base, automated content generation with brand voice controls (marketing copy, product descriptions, email drafts), and AI-powered data extraction (parse unstructured documents and extract structured data into a database). PM translates your product vision into the engineering deliverable you describe the user experience you want, PM defines the technical architecture, and engineers build it.

PM explicitly scopes production architecture decisions in session 1, not prototype decisions. This means: choosing a vector database that can handle your projected data volume (Pinecone for managed simplicity, Weaviate or Qdrant for self-hosted control at scale), designing the data model correctly from the start so you do not need to re-architect the database when you hit 10,000 users, implementing authentication and multi-tenancy correctly from day 1 so each user's data is isolated, using streaming responses from the start rather than waiting for full LLM responses (streaming is much harder to retrofit than to build in), and selecting an LLM provider and model that fits your cost model at your projected usage volume. The MVP is built to grow, not thrown away and rebuilt.

The standard QuickHire AI MVP stack: Next.js App Router for the frontend and API layer (React Server Components for data fetching, route handlers for LLM API calls), OpenAI or Anthropic API for the LLM (GPT-4o-mini for cost-sensitive use cases, Claude 3.5 Sonnet for quality-sensitive), Pinecone or Supabase pgvector for the vector store (Pinecone for managed simplicity, pgvector if you want everything in one Postgres database), Postgres via Supabase or Neon for user data and conversation history, Clerk or Supabase Auth for authentication, Vercel for deployment (zero infrastructure management), and LangChain or the Vercel AI SDK for the LLM orchestration layer. Stack is adapted to your existing infrastructure if you have one, or recommended fresh if you are starting from zero.