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AI SaaS Development
↗End-to-end SaaS, AI at the core
Full-stack platforms with LLM integration, retrieval pipelines, vector search, authentication, billing, and scalable cloud infrastructure. Database to deployed product.
A senior product studio for founders who need real infrastructure — not prototypes, not demos. One architect. Full system ownership. Deployed, documented, yours.
Prompt chains, context windows, memory layers, hallucination guardrails, token economics — these are not features you bolt on later.
Frontend, backend, AI, infra — every handoff doubles your timeline and halves your conviction. One owner is faster than four collaborators.
By the time most teams reach a soft launch, runway is gone and the market has moved. We deliver in the validation window, not after it.
Code that passes a screen-share collapses under real load. We deliver systems that survive contact with users — and grow from there.
Full-stack platforms with LLM integration, retrieval pipelines, vector search, authentication, billing, and scalable cloud infrastructure. Database to deployed product.
AI pipelines that process data, generate content, and make decisions across your existing toolchain. Hours of human labor compressed to minutes of compute.
Systems where multiple agents collaborate — each with a defined role, shared memory, and coordinated decisions. Built on Google ADK, LangChain, and bespoke orchestration.
Validate with a working, deployed product — never a mockup. Authentication, core features, AI integration, cloud deployment. Shipped before the market changes its mind.
The challenge. Off-the-shelf chatbots couldn't hold therapeutic nuance or recall a patient between visits.
The system. Seventeen specialized agents on Google ADK + Vertex AI, with two-tier memory (session + long-term), real-time WebSocket delivery, and clinical safety guardrails enforced at the orchestration layer.
The challenge. Manual analysis couldn't track the speed of a modern derivatives book — signals arrived hours late.
The system. A multi-agent trading architecture with BigQuery ML forecasting, ARIMA+ models, 120+ technical indicators, and automated execution via the Binance API. End-to-end latency in the low hundreds of milliseconds.
The challenge. Static curricula failed students at the edges — those moving too fast, too slow, or simply differently.
The system. Personalized learning paths driven by real-time progress analytics. Infrastructure scales to thousands of concurrent students without operator intervention.
The challenge. Enterprise clients — including Rio Tinto and Caterpillar — needed sentiment and signal extraction across volumes their existing tools couldn't ingest.
The system. Cloud-native ETL pipelines with NLP-powered sentiment analysis. Built for throughput, observability, and graceful degradation. Hand-tuned for cost-per-record.
The challenge. Manual phone-and-spreadsheet quoting couldn't scale — a national vehicle-buying operation needed instant, accurate offers, twenty-four hours a day, with no human in the loop.
The system. A live multi-step quoting platform at sell.junkcarboys.com. VIN decoding, market-data ingestion, and a tuned valuation model produce an offer in under three seconds. Routing, dispatch, and payout flow through the same pipeline. Currently clearing 2,000+ transactions per day.
Not a sales script — a system shape. Stack choices, top risks, and an honest timeline. Generated live.
A vector-search retrieval layer (Qdrant) feeds a prompt-chained Claude reasoning service behind a thin API. Background workers index and embed new content; the read path stays under 400ms p95.
Next.js + FastAPI + Postgres + Qdrant + Vertex AI Gemini 2.0 Flash for embeddings, Anthropic Claude Sonnet for generation. Cloud Run on GCP. Pino + Sentry.
4–6 weeks for one senior engineer to first production traffic.
Send your requirements. You receive a working prototype and a fixed-price architecture proposal with milestones — at no cost. If the work isn't right, you walk away with the prototype.
System topology, model selection, data pipeline, infrastructure. The blueprint is approved before a line of production code is written.
Working software at every checkpoint. No status reports, no slide decks — only demos against running infrastructure.
Production deployment, full documentation, complete source ownership, and a knowledge transfer that leaves your team able to operate the system independently.
I have been building software since before AI meant large language models — fifteen years across enterprise platforms, trading systems, large-scale data pipelines, and SaaS products.
I've held CTO and senior engineering roles at companies processing billions of records, building multi-agent AI systems, and shipping software used by thousands. Enterprise clients have included Rio Tinto, Caterpillar, and Revuze.
Today the studio focuses exclusively on AI-powered SaaS products. Every engagement benefits from patterns refined across more than fifty production deployments. You do not pay for learning curves.
Thirty minutes is enough to map the system. We will tell you, candidly, whether we are the right studio for it.