We build agents, LLM copilots, and analytics on your data — and bring the full-stack engineering to ship and run them in production. Real systems, measured outcomes, from MVP to enterprise scale.
What we do
We lead with applied AI and the data it runs on — then ship it with production-grade full-stack engineering.
Autonomous agents and agentic workflows that take real actions — not chatbots that just talk. We replace brittle RPA with systems that reason, call tools, and keep humans in the loop where it matters.
Copilots, assistants, and chat over your own data — built to ship, not to demo.
RAG over private data · copilots & chat · structured extraction & classification · fine-tuning & distillation
The data foundation AI runs on — plus insight your whole team can use.
Pipelines & warehouses · vector & embedding stores · natural-language BI · predictive ML
The full-stack muscle to deliver and run it all — web and mobile as the surface your AI ships on.
Web & mobile apps · APIs & cloud-native backends · legacy modernization · AWS / GCP / Azure
AI spotlight
Most AI demos die in the gap between prototype and production. We live in that gap. We build agents with tool use and MCP, RAG over your private data, and LLM automation — wrapped in the evals, observability, and guardrails that make them reliable and safe to run.
Model-agnostic by design: we build on frontier models like Claude and GPT, and open or on-prem models when data residency or cost calls for it. We own the quality loop.
Tell us your use-case →Resolves the majority of tickets autonomously, escalates the rest with full context.
Runs repetitive back-office workflows end to end, with approvals where it counts.
Q&A over internal docs, contracts, and tickets — answers with citations.
Domain-tuned for your field — healthcare, finance, logistics — with the right guardrails.
Data & analytics
Most AI projects fail on data, not models. We build the pipelines, warehouses, and vector & embedding stores that make RAG and agents actually work — then add dashboards and natural-language analytics so anyone can ask questions in plain English and get answers they can trust.
ETL / ELT
Reliable pipelines into a clean warehouse.
Vectors
Embeddings & retrieval that power RAG.
NL-BI
Ask in plain English, get the chart.
Predict
Forecasting & ML on your own data.
How we work
A tight loop that finds the highest-ROI use-case and gets it live — then keeps making it better.
We find the highest-ROI AI use-case and define what success looks like.
A working agent or feature in weeks — something real you can use and react to.
Evals, guardrails, observability, and the engineering to make it reliable.
Live in production, measured, and improving — we grow it with you.
Outcomes
Representative outcomes — real case studies on request.
40%
An agent that runs back-office workflows end to end for an operations team.
10k+
A RAG assistant answering questions over a sprawling internal knowledge base — with citations.
6 wks
An AI-native product — agent, app, and data layer — shipped for a seed-stage startup.
24/7
A copilot resolving the bulk of inbound tickets without a human in the loop.
∞ → 1
A legacy monolith moved to cloud-native services, with an AI layer added on top.
NL → SQL
Natural-language BI letting non-technical teams answer their own data questions.
Tech we use
Why us
An AI-native product in weeks — agent, app, and data layer — without building a team first. We move at founder speed and scale with you.
Put AI to work on real busywork and get answers from your own data — a full product team's output without the hiring overhead.
Adopt agents and LLMs safely — on your data, with evals, guardrails, and governance — and modernize legacy systems along the way.
Team
You work directly with the people building your product — no layers, no handoffs.
Tell us what you're trying to do — an agent, a copilot, a data problem, a product. We'll come back with how we'd approach it, a timeline, and a quote.
◉ studio.com