AI agents & automation
An agent is only useful when it can act on the systems your business runs on, and only trustworthy when it cannot act outside its limits. We build both: the assistant, and the controls around it.
Start a projectGeneral models invent tool names, drop arguments and answer with confident prose. We define the exact operations an agent may use, train and test against that real interface, and include the cases where the right answer is to do nothing.
We separate reach from authority. An agent can see a fleet without being allowed to delete from it. Limits are enforced in the tools, so a confident model cannot talk past them.
We make products agent-ready: a command line, a documented API and an MCP server, the standard connection AI apps use to call tools. Agents then find the right operation instead of wading through thousands.
We give agents memory that survives a reset, kept in files and records people can read and review.
We set it up with gates: work must be defined before it starts, consequential actions are checked before they run, and a person decides what counts as done.
The assistant picks the operation, the system runs it, and the result comes back in plain words. Every call and result is recorded.
A command line, an API and an MCP server over your product, with discovery so an agent searches for an operation rather than loading them all.
Training data, test sets and packaging work that teach an open model to call the operations in your interface correctly.
Notes and summaries carried between sessions and projects, with search, backup and restore, and a way to see what is kept.
A scaffold for identity, working rules, memory and roles, so a new agent starts somewhere useful.
Readiness checks, tool gates, quality and security checks on every change, and a human acceptance step.
A console that shows what agents are doing, what they cost and how the machines they run on are doing.
You have an app or service and want an assistant inside it. We build the assistant and the interface it works through.
You want the speed without the surprises. We put the rules in the tools, where they hold.
You run many systems and many repeat tasks. We build agents that act on them within limits you set.
You want agents to use your product. We make it ready for them.
We map the job, the systems involved and what an agent must never do. We write down the operations it may use.
We choose the controls first: permissions, checks before action, records of every call, and where a person decides.
Work runs through tickets that cannot start until they are ready. Quality and security checks run on every change.
We test against real tasks, measure what works and keep adjusting. Releases are recorded automatically.
An assistant inside a server operating system that investigates the real machine, with every tool call and result on record.
How we trained and tested a model to call tools, including what went wrong and how we found it.
A command line and MCP server over 1,843 Atlassian operations, shown to an AI agent through two tools.
Lasting memory for coding agents, with search, backups and restore.
The gates, hooks and checks we use ourselves so that agents stop at QA and people decide what is done.
Not on instruction alone. We enforce limits in the tools, record what the agent did and keep human decisions for anything destructive. We do not promise that a model never errs. We build so that mistakes are caught.
Yes. Most agent work is connecting to what you already run through its API or a command line, then limiting what the agent can do there.
We start with a narrow job that has a clear test, get a working version in front of you early, then widen it. The first job sets the shape of everything after.
You do, outright. Whatever you pay us to build is yours, with full rights and the complete source code. We can keep running and improving it with you, and we write down how it works so you are not dependent on us.
