Engineering for AI-Native SaaS
End-to-end development for AI-powered products. RAG pipelines, multi-agent systems, LLM integrations, and full-stack SaaS — built for production.
AI SaaS Engineering Challenges
We've solved these problems across 25+ AI products
RAG Quality
Getting retrieval to actually work in production, not just demos
Agent Reliability
Multi-agent systems that don't hallucinate or get stuck in loops
LLM Costs at Scale
Keeping inference costs manageable as you grow
Vertical AI Moats
Building AI products that incumbents can't just copy
Our AI & SaaS Solutions
Production-grade AI engineering from MVP to scale
RAG Pipelines
Retrieval-augmented generation systems that actually work in production
- Chunking Strategy
- Vector Search
- Re-ranking
- Eval Framework
Multi-Agent Systems
Agentic workflows that complete complex tasks reliably
- Agent Orchestration
- Tool Use
- Memory & State
- Human-in-Loop
LLM Integration
GPT-4o, Claude, Gemini, and open-source model integrations
- Prompt Engineering
- Fine-tuning
- Caching
- Fallbacks
Full-Stack SaaS
Next.js + NestJS + Postgres SaaS products with auth and billing
- Auth (NextAuth)
- Stripe Billing
- Multi-tenant
- Admin Panel
AI Data Pipelines
ETL, embeddings, and vector database infrastructure at scale
- Pinecone / pgvector
- Embedding Pipelines
- Batch Processing
- Monitoring
Fractional CTO
Senior technical leadership for pre-seed and seed-stage founders
- Architecture Design
- Investor Calls
- Tech Due Diligence
- Team Hiring
Our AI & SaaS Impact
AI Products Shipped
Avg MVP Delivery
ARR Built
Accelerator-Backed Products
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