Multi-tenant RAG: isolating client data in AI agents
The problem When you build an AI agent for one client, RAG is straightforward: embed their docs, store them, query…
Not buzzwords — the actual architecture decisions behind every build.
Fast apps that hold up under real traffic, not just a demo.
Data protection and secure-by-default practices, not bolted on after launch.
Continuous deployment with zero downtime — this site is the proof.
A few of the integrations, apps, and systems I've shipped — each one solving a real problem and shipped.
Real constraints, tradeoffs and things that broke — the short version of the work the case studies don't tell.
The problem When you build an AI agent for one client, RAG is straightforward: embed their docs, store them, query…
The problem Every AI image model I've used mangles text. Quotes, numbers, names — they come out as gibberish far more…
The problem A dentist I work with spends half the consultation typing notes into a patient record. The other half,…
Every layer of the stack in one set of hands — AI to motion, connected and shipped.
Explore the services →No handoffs, no account managers. You talk to the person writing the code.
A working product in weeks, not quarters — so you validate before you over-build.
Founders validating an idea with real users, and teams that need a first version fast.
Turn the repetitive weekly grind into pipelines that run themselves.
Ops leaders whose teams retype, reformat, and reconcile the same data every week.
Assistants and copilots grounded in your documents — that stay quiet instead of hallucinating.
Businesses with documents, tickets, or records they want turned into answers and automated work.
Based in Santiago, Dominican Republic. I've shipped 8 projects this year alone — AI integrations, multi-tenant SaaS, mobile apps, and automations. Bilingual EN/ES, and I build for the Dominican market as fluently as the US one.
If you need a smart layer between your data and your users, that's exactly what I build.
More about Pedro →