AI MVP Development
Build Your AI Product in 2 Weeks
We build RAG apps, LLM-powered tools, AI SaaS platforms, chatbots, content generation engines, and AI analytics dashboards. Production-ready, cost-optimized, and designed to scale. OpenAI, Claude, LangChain, vector databases — the full AI stack.
AI Products We Build
Six categories of AI product, each with different architecture patterns, model requirements, and cost profiles. We've shipped all of them.
RAG Applications
Retrieval-augmented generation systems that answer questions from your proprietary documents, SOPs, and knowledge bases. We build the embedding pipeline, vector search layer, and citation system so every answer is grounded in your data — not hallucinated.
Real example: A legal tech startup needed their 12,000-page regulatory corpus queryable by non-lawyers. We built a RAG system with role-based access, source citations, and confidence scoring in 12 days.
AI Chatbots & Assistants
Conversational interfaces powered by GPT-4, Claude, or Gemini that integrate with your CRM, helpdesk, or product. Not wrapper chatbots — these understand your business context, follow conversation history, and escalate to humans when needed.
Real example: A B2B SaaS company replaced their Zendesk FAQ with an AI assistant that resolves 68% of Tier-1 tickets without human intervention. Response time dropped from 4 hours to 8 seconds.
Content Generation Platforms
Tools that generate, edit, and manage content at scale — blog posts, product descriptions, email sequences, social media calendars. We build the prompt chains, editorial workflows, and brand-voice calibration that make AI output actually usable.
Real example: An e-commerce brand needed 5,000 product descriptions in their voice. We built a generation pipeline with brand guidelines baked into the system prompt, human-in-the-loop review, and bulk export.
AI Analytics & Insights
Dashboards where users ask questions in plain English and get charts, summaries, and actionable recommendations. We connect to your data warehouse, build the natural-language-to-SQL layer, and surface insights that would take an analyst hours to find.
Real example: A logistics company wanted drivers asking 'Show me my fuel cost trend this quarter' and getting instant charts. We built a text-to-SQL layer over their PostgreSQL warehouse with guardrails preventing data leakage across tenants.
AI SaaS Products
Full AI-powered SaaS applications with user authentication, subscription billing, usage metering, and the AI feature as the core value proposition. We handle the hard parts: cost-per-request tracking, rate limiting, model fallback, and graceful degradation.
Real example: We shipped an AI writing assistant SaaS — auth, Stripe billing, usage quotas, GPT-4/Claude model switching, and a polished editor — in 14 days. The founder launched to 200 waitlist users on Day 15.
Voice AI Applications
Voice-to-text, text-to-speech, and voice-driven workflows using Whisper, ElevenLabs, and Deepgram. We build the real-time transcription, speaker diarization, and voice command interfaces for products where typing isn't practical.
Real example: A screenwriting platform needed voice dictation that understands screenplay formatting commands. We integrated Whisper with custom post-processing rules that convert 'new scene exterior night' into properly formatted screenplay elements.
AI Tech Stack
We pick the right tool for each layer of your AI product — not the trendiest one. Every choice here is based on production experience, not blog posts.
LLM Providers
- OpenAI GPT-4 / GPT-4o — Best all-around model for most production use cases
- Anthropic Claude 3.5/4 — Superior for long-context, reasoning, and code generation
- Google Gemini — Strong multimodal capabilities — vision, audio, long documents
- Open-source (Llama, Mistral) — Self-hosted for data sovereignty or cost optimization
Frameworks & Orchestration
- LangChain / LangGraph — Agent orchestration, tool calling, conversation memory
- LlamaIndex — Purpose-built for RAG — indexing, retrieval, and response synthesis
- Vercel AI SDK — Streaming responses, edge-compatible, React Server Components
- Custom pipelines — When frameworks add overhead, we build lean custom chains
Vector Databases
- Pinecone — Managed, serverless — best for teams that don't want to manage infra
- Supabase pgvector — PostgreSQL-native — keeps your data in one place
- Weaviate — Open-source, hybrid search (vector + keyword) out of the box
- ChromaDB — Lightweight, embedded — great for prototyping and small datasets
Infrastructure
- Vercel / Railway — Edge deployment for low-latency AI responses
- Redis — Response caching, rate limiting, and session management
- BullMQ — Background job queues for long-running AI tasks
- Sentry + PostHog — Error tracking and AI usage analytics
API vs Fine-Tuning vs Self-Hosted
The most expensive mistake in AI product development is choosing the wrong approach. Here's when each makes sense.
API Integration (GPT-4, Claude)
Best For
Most AI MVPs, content generation, chatbots, RAG apps
Cost
$0.002-$0.06 per request
Timeline
Days to integrate
Trade-off: Easy to start, ongoing per-request costs. Best when you need general intelligence + your data via RAG.
Fine-Tuned Model
Best For
Domain-specific tasks with thousands of training examples
Cost
$500-$5,000 training + lower per-request costs
Timeline
1-2 weeks for data prep + training
Trade-off: Lower latency and per-request cost, but requires labeled training data. Only worth it when API prompting hits a quality ceiling.
Self-Hosted Open Source (Llama, Mistral)
Best For
Data sovereignty, air-gapped environments, cost control at scale
Cost
$200-$2,000/mo GPU hosting
Timeline
1-2 weeks for deployment and optimization
Trade-off: Full data control, fixed monthly cost. But you manage infra, and quality lags behind GPT-4/Claude for complex reasoning.
AI Cost Optimization
The biggest risk in AI products isn't building them — it's the API bill 3 months after launch. We design every AI MVP with cost controls from Day 1.
Token Economics
Every LLM call has a cost based on input + output tokens. A typical customer support response costs $0.003-$0.01. At 10,000 queries/day, that's $30-$100/day in API costs. We design your architecture to minimize token usage through caching, prompt compression, and smart model routing.
Model Routing
Not every request needs GPT-4. We build routing layers that send simple queries to cheaper, faster models (GPT-4o-mini, Haiku) and reserve expensive models for complex reasoning. This typically cuts API costs by 60-80% with minimal quality impact.
Response Caching
Many AI applications serve identical or near-identical queries. We implement semantic caching — if a user asks the same question in different words, we serve the cached response instead of calling the API again. Redis-backed, sub-millisecond lookups.
Cost Guardrails
We build per-user and per-tenant spending limits, usage dashboards, and alerting. You'll never wake up to a surprise $10,000 API bill. Every AI MVP we ship includes a cost monitoring page in the admin panel.
AI MVP Pricing
Fixed prices. No hourly billing, no scope creep surcharges. AI API costs (OpenAI, Anthropic, etc.) are billed separately by the provider — we help you minimize them.
AI MVP — Standard
One AI-powered feature integrated into a production-ready web app.
AI MVP — Advanced
Complex AI product with multiple models, fine-tuning, or multi-step pipelines.
AI Engineering Resources
Deep dives into RAG architecture, prompt engineering, agent orchestration, cost control, and production AI security.
Explore the AI HubAI Product Architecture Guide (PDF)
Our internal playbook for architecting AI products: model selection decision tree, RAG pipeline design, cost optimization strategies, and security checklist.
Proven Results
Real projects. Real numbers. See what we delivered.
SaaS MVP Shipped in 14 Days: From Napkin Sketch to Paying Customers
$4,200 MRR in month one
How a solo founder went from idea to $4,200 MRR in two weeks with a project management SaaS built on Next.js, PostgreSQL, and Stripe.
Two-Sided Marketplace MVP: From Zero to 200 Listings in 3 Weeks
200 listings, 47 bookings in month one
How we built a services marketplace connecting local contractors with homeowners, complete with booking, payments, and review system.
Mobile App MVP: Cross-Platform Fitness Tracker in 2 Weeks
1,200 downloads in first week
A React Native fitness tracking app with workout logging, progress photos, and social challenges, shipped to both app stores in 14 days.
Frequently Asked Questions
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