Most AI agents demo well. We build the ones that survive.
The hard part was never the prompt. It is what happens on the thousandth request, with messy inputs, a tool that times out, and a user who asks something nobody anticipated. We design, evaluate and ship agents that hold up there.
Wondering if an agent fits your workflow?
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Teams who have already proved the idea. Someone built a prototype, it impressed the room, and then the questions started: how do we know it is right, what happens when it is wrong, who owns it, and how does it reach customers without embarrassing us. That gap between a convincing demo and something you can put your name on is most of the work, and it is the part we do.
It is also for teams with no prototype at all, but a workflow that clearly should not be done by hand any more.
The whole loop, not just the model call.
Agent Architecture & Tools
We start from your workflow, not from a framework. What decisions does the agent actually make, what is it allowed to do, where must a human stay in the loop. Then we design the tools it calls, because an agent is only as capable as the actions available to it and only as safe as their validation.
Retrieval, Memory & Context
An agent that answers from the model's memory will eventually invent something. One that answers from your documents, your database and your policies can be checked. We build the retrieval layer, decide what the agent should remember between turns, and manage the context budget so quality does not decay in long conversations.
Evaluation & Guardrails
This is the difference between a demo and a system. We build an evaluation set from your real cases and score every change against it, so you can see whether today's version is better than last week's rather than guessing. Guardrails handle the rest: refusals, escalation paths, and limits on what the agent can do unsupervised.
Integration & Deployment
An agent nobody can reach is not finished. We put it where the work already happens: inside your product, on WhatsApp or Telegram, in a dashboard, or behind an API your systems call. It runs in your cloud, monitored, with the cost and latency visible from day one.
Prove it early, then harden it.
Discover
We map the workflow, your data and what success actually looks like, and agree how we will measure it before any code is written.
Prototype
A working agent on the hardest part of the problem first, so the risk surfaces in week two rather than month three.
Build
Tools, retrieval, evaluations and guardrails, with you reviewing real outputs the whole way.
Deploy
Into your infrastructure, monitored, with tuning and support after launch.
Agents running in production.
MingAI
We trained a domain-specific model for metaphysics and built the agents around it, then the whole product surface: a web application and native Android and iOS apps. The model work and the product work were done by the same team, so nothing was lost in translation. Live at mingai.ai.
WAgent
An autonomous web-design agent that lives in a chat thread. It interviews you about your business, a design director agent sets the creative direction, and a draft site goes live for your review, often within the hour. A real test of multi-agent orchestration with a deadline attached. Live at wagentapp.com. Read the case study.
AçaiFlow
An AI ordering assistant woven into a wholefood brand's menu and build-your-own-bowl flow, answering the questions counter staff field all day and guiding customers to an order. Live at acaiflow.my. Read the case study.
The things people ask first.
How long does it take?
Two to eight weeks for most engagements. A focused single-purpose agent can be in front of users in two to three weeks. A multi-agent system with several tools, retrieval over your own data and a full evaluation suite takes longer.
Which models do you use?
Whichever fits the task, the latency budget and the cost ceiling. We benchmark candidates against your evaluation set rather than a public leaderboard, and keep the model layer swappable so a better or cheaper model can be adopted later without a rebuild.
How do you stop it making things up?
Grounding and measurement. Answers come from your sources rather than the model's memory, actions run through tools with validated inputs, and the evaluation harness scores every change against real cases, so a regression is caught before release rather than by a customer.
What happens to our data?
It stays yours. We work in your cloud or a tenancy you control, use providers with no-training guarantees, and can run open models on your own infrastructure where the data cannot leave the building. How data is handled is agreed in writing before we start.
Do we own what you build?
Yes. The agent code, prompts, tool definitions and evaluation sets are yours on final payment, deployed in your infrastructure. There is no runtime you have to keep renting from us for it to keep working.
What does it cost to run?
Model usage plus hosting, and usually less than teams expect. We estimate it during the prototype using your real traffic and design around it: caching, smaller models for routine steps, and a larger model reserved for the work that needs it.
Tell us what you want it to do.
Describe the workflow and we will tell you straight whether an agent is the right tool, what it would take, and where the risks are. If the honest answer is a script and a database, we will say that instead.