Hire an AI MVP Developer
Who Builds Products, Not Demos
I build production-grade AI products including chatbots, RAG knowledge bases, content generation platforms, agent systems, and intelligent automation tools. Every project ships with proper cost management, streaming interfaces, structured output validation, and the infrastructure needed to scale beyond your first 100 users.
The AI space is full of impressive demos that fall apart in production. My focus is on building AI products that handle real traffic, manage costs at scale, degrade gracefully when models are unavailable, and deliver consistent value to your users. Production engineering applied to artificial intelligence.
AI Development Capabilities
Every AI product requires different architecture. Here is what I build and how each capability translates into real product value.
AI-powered chatbots and assistants
Conversational AI interfaces with streaming responses, context memory, document-aware conversations, and multi-turn dialogue. Built with the Vercel AI SDK for real-time streaming, these chatbots go beyond simple question-answer flows. They understand context, reference uploaded documents, maintain conversation history, and deliver responses that feel natural and helpful.
Retrieval-augmented generation (RAG)
Knowledge base systems that combine your proprietary data with AI models. I build RAG pipelines that chunk documents, generate embeddings, store vectors in Supabase pgvector or Pinecone, and retrieve relevant context at query time. This lets your AI product answer questions using your specific data instead of generic model knowledge.
Content generation platforms
AI-driven content creation tools for blog posts, product descriptions, email sequences, social media copy, and marketing materials. Includes template management, brand voice configuration, output formatting, revision workflows, and usage tracking. Built to handle high volume with proper rate limiting and cost management.
AI agent workflows
Autonomous AI agents that execute multi-step tasks including web research, data extraction, document processing, decision-making chains, and API orchestration. I build agent systems using function calling, tool chains, and structured output parsing that reliably complete complex workflows without human intervention.
Intelligent automation dashboards
Admin interfaces that surface AI-generated insights, automate repetitive decisions, and provide human-in-the-loop oversight for AI operations. Includes real-time monitoring of AI pipeline performance, cost tracking per query, accuracy metrics, and override controls for edge cases that require human judgment.
Model integration and API layer
Backend infrastructure for AI products including multi-model routing (OpenAI, Claude, Gemini, Mistral), fallback chains, response caching, cost optimization, and structured output validation using Zod schemas. Every API layer is built with proper error handling, retry logic, and rate limit management.
Supported AI Models
I work with every major AI model provider and can integrate multiple models into a single product using intelligent routing based on task requirements, latency needs, and cost constraints.
OpenAI GPT-4o
General intelligence
Claude 3.5 Sonnet
Long-form reasoning
Gemini Pro
Multimodal tasks
Mistral Large
Fast inference
OpenAI Embeddings
Vector search
DALL-E / Stable Diffusion
Image generation
Whisper
Speech to text
Custom fine-tuned models
Domain-specific
AI Technology Stack
Purpose-built tools for AI product development. Each technology handles a specific part of the AI pipeline from model interaction to data storage to user interface.
Vercel AI SDK
Streaming and tools
OpenRouter
Multi-model routing
Supabase pgvector
Vector storage
LangChain
Agent orchestration
Next.js
Frontend and API
Zod
Output validation
Resend
Email automation
Cloudinary
Media processing
Project Types and Timelines
From simple chatbot integrations to full AI SaaS platforms. Here are the most common project types with realistic timelines.
AI chatbot or assistant
2 to 3 weeksStreaming conversational interface with context memory, document upload, and custom system prompts. Includes admin panel for prompt management and conversation analytics.
RAG knowledge base
3 to 5 weeksDocument ingestion pipeline, vector storage, semantic search, and AI-powered Q&A interface. Upload PDFs, docs, or web content and get accurate answers from your data.
Content generation tool
3 to 4 weeksAI writing platform with templates, brand voice settings, batch generation, revision history, and usage dashboards. Built for marketing teams and content creators.
AI agent system
4 to 6 weeksMulti-step autonomous agent with tool calling, web search, data extraction, and structured output. Includes monitoring dashboard and human override controls.
AI-enhanced SaaS feature
1 to 3 weeksAdd AI capabilities to an existing product. Smart search, auto-categorization, content suggestions, anomaly detection, or predictive analytics integrated into your current stack.
Full AI SaaS MVP
5 to 8 weeksComplete AI-powered product with authentication, subscription billing, usage limits, admin dashboard, analytics, and production infrastructure. From zero to launch.
Frequently Asked Questions
Which AI models do you work with?
I work with OpenAI (GPT-4o, GPT-4 Turbo), Anthropic (Claude 3.5 Sonnet, Claude 3 Opus), Google (Gemini Pro), Mistral, and open-source models. I use multi-model routing through OpenRouter to select the best model for each task based on quality, speed, and cost requirements.
Can you integrate AI into my existing product?
Yes. Adding AI features to existing applications is one of my most common project types. Whether you need smart search, content suggestions, automated categorization, or a chatbot widget, I can integrate AI capabilities into your current stack without rebuilding anything.
How do you handle AI costs and rate limits?
Every AI product I build includes proper cost management. This includes response caching to avoid redundant API calls, token usage tracking per user, rate limiting at the application level, model fallback chains for cost optimization, and admin dashboards showing real-time spend analytics.
What about AI hallucinations and accuracy?
I mitigate hallucinations through RAG architecture (grounding responses in your actual data), structured output validation using Zod schemas, confidence scoring, source citation in responses, and human-in-the-loop review workflows for high-stakes decisions. No AI system is perfect, but these techniques dramatically reduce error rates.
What does an AI MVP cost?
A chatbot or single AI feature starts at $3,000. A RAG knowledge base ranges from $5,000 to $8,000. A full AI SaaS MVP with authentication, billing, and infrastructure starts at $10,000. I provide fixed-price quotes after the discovery call.
How fast can you ship an AI product?
A focused AI chatbot or assistant can ship in 2 to 3 weeks. A full AI SaaS MVP with all infrastructure takes 5 to 8 weeks. I work in weekly sprints with daily progress updates so you see real progress throughout the build.
Ready to Build Your AI Product?
Book a free 30-minute call to discuss your AI product idea, model requirements, and timeline. No commitment, no sales pitch.