AI that earns its place in the real world.
Agents, retrieval, and models wired into the product where they truly help. Built for real inputs and real users, with the evaluation to keep them honest long after launch.
The short answer
What does Zeto Studio's AI Development service include?
It takes an AI feature from idea to production on your real data. You get a retrieval and knowledge system you can extend, agents and workflows with guardrails and fallback behaviour, an evaluation harness that keeps quality measured over time, and monitoring with cost controls. The goal is AI you can put in front of customers, not a demo.
Most AI looks brilliant in a demo and falls apart the second a real customer touches it. The gap between a clever prototype and something you can put your name on in front of real customers is where the real engineering lives, and it is the part most teams skip.
We build the unglamorous parts too. The retrieval that finds the right answer, the agents that know when to stop, the guardrails that keep it on the rails, and the testing that catches mistakes before your users do. AI that helps your product and that you can stand behind.
We are not here to bolt a chatbot onto a homepage. We find the one workflow where intelligence genuinely compounds, prove it works on your real data, and then make it dependable enough to trust with customers.
The work, in plain terms.
LLM products
Chat-with-your-data, copilots, and assistants built into the core product.
Retrieval and RAG
Grounded answers from your own knowledge, with sources you can check.
Agents and workflows
Multi-step automation that knows its limits and hands off to a human.
Evaluation and guardrails
Tests, scoring, and safety so quality holds after launch.
Model integration
The right model for the job, with fine-tuning where it pays for itself.
MLOps
Monitoring, versioning, and the plumbing that keeps AI reliable over time.
You leave with something real.
- An AI feature live and running on your real data
- A retrieval and knowledge system you can extend
- An evaluation harness that keeps quality measured over time
- Guardrails and fallback behaviour for the edge cases
- Monitoring and cost controls
- Documentation so your team can build on it
A rhythm you can rely on.
Find the use case
The one workflow where AI compounds, prototyped on your real data.
Make it real
Retrieval, evaluation, and guardrails until it is trustworthy with real users.
Keep it honest
Monitoring and iteration as inputs and usage change.
The pilot was the easy part. Zeto stayed for the months after, measuring where the automation fell short and fixing it, until our team stopped double-checking its work.
Good things to ask us.
We have a pilot stuck in a demo. Can you get it live?+
Yes, and that is one of the most common reasons teams call us. Moving from a promising pilot to something customers rely on is an engineering problem, and it is the part we are built for.
How do you stop it from making things up?+
Grounding answers in your own data, adding guardrails, and measuring quality with an evaluation harness. We treat correctness as something you test and keep testing.
Do we need to train our own model?+
Usually not. Most value comes from good retrieval and solid engineering around strong existing models. We fine-tune only when it clearly earns its keep.
Let's talk about your AI development.
Bring your objective and your constraints. We will come back with a focused path, and we stay accountable to the outcome.