Build an AI SaaS MVP

Build an AI SaaS MVP
LLM, RAG, Billing. 2 Weeks.

An AI SaaS product needs more than a ChatGPT wrapper. It needs a reliable LLM orchestration layer, a RAG pipeline that actually retrieves the right context, usage based billing that tracks tokens and API calls, guardrails that prevent hallucination and prompt injection, and a user experience that makes AI feel like magic, not a chatbox. We build all of it in 14 days.

14 day delivery
AI guardrails included
Full source code

What Ships in Your AI SaaS MVP

Every feature below is included in the fixed price. This is a production AI product, not a prototype with hardcoded prompts.

LLM orchestration with model routing, fallbacks, and streaming responses via OpenAI, Anthropic, or open source models
RAG pipeline with document ingestion, chunking, embedding, and vector search for context grounded answers
Vector database (pgvector or Pinecone) for semantic search across your users' uploaded content
Conversational UI with streaming responses, conversation history, and context management
Usage based billing via Stripe with token tracking, plan limits, overage charges, and usage dashboards
Auth with team workspaces, API key management, and per user usage quotas
AI guardrails: prompt injection detection, output validation, content filtering, and confidence scoring
Document upload and processing: PDF, DOCX, CSV, and web URLs ingested into the knowledge base
Tool use and function calling so your AI can take actions: search databases, call APIs, generate reports
Admin dashboard with token usage, cost tracking, user analytics, and model performance metrics
Caching layer for repeated queries to reduce latency and API costs by 40 to 60%
API endpoints so your customers can integrate your AI into their own workflows programmatically

Why This Is Not a ChatGPT Wrapper

Not a ChatGPT wrapper

We build production AI with guardrails, evaluation pipelines, and model routing. Your users get reliable, grounded responses, not raw LLM output.

Usage billing from day one

Token tracking, per user quotas, and Stripe integration baked in. You can charge per query, per document, or per seat from launch.

Your data, not theirs

RAG pipeline grounds every response in your users' actual content. No hallucinated answers, no generic responses, no data leaking between tenants.

AI that takes action

Tool use and function calling let your AI search databases, call APIs, generate documents, and trigger workflows. Not just text in, text out.

AI SaaS Architecture

The frontend runs on Next.js with streaming server components for real time AI responses. The backend orchestrates LLM calls through a unified API layer that supports model switching, retry logic, and cost optimization. Documents uploaded by users are chunked, embedded, and stored in a vector database (pgvector for simplicity, Pinecone for scale). Every query hits the RAG pipeline: user input is embedded, relevant chunks are retrieved, and the full context is sent to the LLM with system prompts and guardrails. Stripe tracks token usage per user and enforces plan limits. Redis caches frequent queries. The entire system deploys as three services: frontend on Vercel, API on Railway, and the vector database alongside PostgreSQL on Railway.

Tech Stack

Battle tested AI infrastructure. No experimental frameworks, no vendor lock in, no black boxes.

Next.js
Frontend and streaming UI
TypeScript
End to end type safety
Hono
API server and LLM orchestration
PostgreSQL
Primary database
pgvector / Pinecone
Vector search
OpenAI / Anthropic
LLM providers
Stripe
Usage based billing
Redis
Caching and rate limiting
Drizzle ORM
Type safe queries
Vercel + Railway
Hosting

14 Day Build Timeline

Day 1 to 2

Scope and Foundation

60 minute deep dive on your AI use case, data schema, auth setup, LLM provider configuration, CI/CD pipeline

Day 3 to 5

AI Core

LLM orchestration layer, RAG pipeline, document ingestion, vector database setup, streaming chat UI

Day 6 to 8

Product Features

User dashboard, conversation management, document upload, API endpoints, tool use integration

Day 9 to 11

Billing and Guardrails

Stripe usage tracking, plan limits, AI guardrails, prompt injection defense, caching layer, admin panel

Day 12 to 13

Testing and Optimization

Evaluation test suite, latency optimization, cost reduction tuning, security audit, edge case handling

Day 14

Launch

Production deployment, monitoring setup, analytics, founder walkthrough, first user onboarding test

What Founders Are Building

AI SaaS products we have built or scoped in the last 6 months.

AI writing assistants with brand voice enforcement
Customer support copilots grounded in product docs
Legal document analysis and contract review tools
Code generation and developer productivity platforms
Research assistants that search and synthesize papers
Sales intelligence tools that analyze prospect data
Content platforms with AI powered editing and generation
Data analysis tools that query databases in natural language

AI SaaS MVP, Fixed Price

$9,999

14 day delivery • RAG + billing included • 30 day support

50% upfront, 50% on delivery • Complex builds from $14,999

Start Your AI SaaS MVP

See an AI SaaS We Built

An AI content platform that uses RAG to generate brand voice consistent copy from uploaded style guides. Launched in 14 days with usage based billing and multi tenant knowledge bases.

Read the Case Study

Frequently Asked Questions

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50+ products shipped$10M+ funding raised2-week delivery

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