AI SaaS Builder & Engineer
Integrating LLMs and automation into production SaaS.
Sharon Rosario builds AI-powered SaaS products end-to-end — LLM integration, retrieval, multi-tenant data isolation, and the scalable infrastructure to run it in production. Founding engineer at getconch.ai and ilumiera.ai.
Delivering scalable, intelligent solutions by integrating full-stack development, automation, AI/ML, and cloud—engineered with modern technologies and a focus on architectural excellence. Read the Git worm incident write-up, the multi-tenant RAG architecture, or the About page.
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AI SaaS Builder & Engineer — overview
Bolting an LLM onto a product is easy; making it a reliable, secure, multi-tenant SaaS is the hard part. Sharon Rosario builds the full picture — the retrieval layer, the prompt and agent orchestration, the tenant isolation that keeps customers' data separate, and the async infrastructure that keeps the app responsive when the model is slow.
As a founding engineer building AI products at getconch.ai and ilumiera.ai, Sharon has learned that the interesting engineering in AI SaaS is rarely the model call. It is data isolation, cost control, latency, evaluation, and graceful failure — the things that decide whether a demo becomes a product customers trust.
What production AI SaaS actually requires
Retrieval-augmented generation with correct, provable data isolation between tenants. Background processing so slow model calls never block a request. Streaming responses for a responsive UX. Observability and evaluation so quality can be measured, not guessed. And cost controls so token spend does not quietly eat the margin.
These are the concerns that separate a working AI product from an impressive prototype, and they are exactly what the case studies below dig into.
The stack
Python and FastAPI for AI services, Node.js where it fits, Postgres with pgvector for retrieval, Redis and BullMQ for background work, and React frontends that stream results. LLM orchestration with LangGraph and direct provider integrations.
Related case studies
Zero-Leak Multi-Tenant RAG Architecture
Building a B2B AI product where one customer can never read another customer's data — using Postgres Row-Level Security and pgvector.
Read the case studyOrchestrating Multi-Agent Workflows with LangGraph
A deterministic, self-correcting Researcher-Writer-Verifier agent pipeline in LangGraph — with state management and LangSmith observability.
Read the case studyFrequently asked questions
Retrieval, multi-tenant data isolation, background processing for slow model calls, streaming UX, evaluation and observability, and cost control. The model call is the easy part — the surrounding engineering is what makes it a trustworthy product.
Using database-enforced isolation such as Postgres Row-Level Security combined with pgvector for retrieval, so one tenant mathematically cannot read another tenant's data. The zero-leak RAG case study below documents the full approach.
Python and FastAPI, Postgres with pgvector, Redis and BullMQ for async work, LangGraph for agent orchestration, and React for streaming frontends — deployed on cloud platforms with observability built in.
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