AI features in new and existing products
Search, generation, recommendations, document processing and other features built with language, vision or speech models. We can add them to an existing product or build the product around them.
TriaChain Solutions / AI development
AI products and agents, past the demo stage.
Demos are cheap now. Anyone can produce something impressive in a week, and it usually falls over the first time ten real people use it at once. We do the unglamorous half: the failure cases, the cost per request, the audit trail, and whether the thing is still standing a year from now.
// what we take on
Four kinds of AI work we take on
AI connected to your data and systems
Models made useful with your documents, databases, APIs and business rules. We can use hosted models or run them inside your infrastructure when the data cannot leave, and the result fits the workflow instead of living in another chat window.
Agents that do more than chat
Agents that use tools, take several steps and work across systems. We design approvals, failure handling and audit trails around the parts where a wrong action costs money.
Agent platforms and MCP infrastructure
MCP servers, multi-agent coordination, messaging, task tracking and persistent shared memory. We built Orc for this work and run it every day across models, machines and operating systems.
// our own infrastructure
Orc keeps multi-agent development under control
Orc is a distributed coordination system we wrote in Go. It supports Codex, Claude Code, Grok Build, Antigravity and Kimi Code out of the box, and coordinates live sessions across machines and operating systems through messaging, tracked tasks, persistent shared memory and a record of what happened.
Different model families can work on the same problem separately. A coordinating agent compares their results, resolves disagreements and assembles the final proposal. It does not guarantee correctness, but it reduces dependence on one model's assumptions and blind spots.
The point is not to turn agents loose as a swarm. Orc keeps a person in charge of the whole development process, from architecture and task planning to code and review. Agents can work in parallel, but every request, owner, decision and result stays visible.
We built it because multi-agent development breaks in boring, expensive ways: context disappears, requests lose their owner, two agents do the same job and nobody can reconstruct the result. For clients, that means we design agent systems around control and accountability from the start. We do not discover those problems on your budget.
// how it works here
What has to work after the demo
- The model will be slow, wrong or unavailable. We design for that, and for ten real users arriving at once. The data model, authentication, error handling and deployments are part of the product from the start.
- The model is a decision, not a default. We compare hosted APIs, models running inside your infrastructure and custom training against accuracy, running cost and data rules. We estimate cost per user before committing to the architecture.
- Data rules come first. We decide what can leave your infrastructure, which vendors can see it and what must run locally before those choices become expensive to undo.
- Actions need limits and a record. When AI can spend money, change data or contact a customer, we add permissions, approvals and an audit trail.
// questions
What clients ask first
What is an AI agent, in business terms?
A program that uses tools and takes several steps on its own toward a goal. Useful when a task takes a person many small steps across different systems. Risky when nobody is watching what it did, which is why the audit trail matters as much as the model.
What is MCP and do we need it?
MCP is a standard way to expose your tools and data to a model. You need something like it as soon as agents touch real systems, because the alternative is a pile of one-off integrations nobody can audit.
Can you turn our AI prototype into a real product?
That is a good share of our work. We read the code, write down what will not survive real users, and give you a plan with a price before anything is rewritten.
Do you train custom models?
We can, and sometimes it is the right call: a narrow domain, data nobody else has, or costs that only work with a smaller model you own. We will tell you honestly when your case is not one of those, because most are not.
What does an AI project cost?
There are two bills: building it and running it. We look at the scope and your data before agreeing the build budget, and we estimate the cost per user before you commit. The second number is the one that surprises people later.
Do we need our own GPUs?
Usually not. Most business problems are solved by using existing models carefully rather than by buying hardware. When your case genuinely needs local inference, we will say so and explain what it costs to run.
// contact
info@triachain.com
Tell us what you want the system to do
Write in plain words: what task it should take over, what data it needs, and who will be using it. One of the three of us answers, usually the same day.
We also do MVP development · Blockchain development · Mobile apps · Web platforms · Technical due diligence
Technical: Rust and Go engineering