Agents engineered around your exact process. Not general tools that roughly fit what you do.
This means building a custom AI agent: software designed around one specific process, rather than a general tool pointed roughly at it.
We design and build these agents for governance-led businesses. An agent isn't a chatbot, and it isn't an app your team logs into. It's trigger-based. It activates when a defined event happens, works through a sequence of logical steps, and produces a structured output for a person to act on.
It's delegated decision-making, but tightly bounded. The agent makes only the decisions you've defined, against the rules and the standard you set, and nothing beyond that. Building one that holds up is a job of engineering, not configuration.
Every agent is built for one specific process. These are the stages the build generally moves through.
Mapping the trigger. Pinning down the steps, the decision points, the rules the agent works to and the shape of the output. Working out where the agent's judgement should stop and a person's should start. The unglamorous detail that decides whether the finished agent is any good.
A specification precise enough to build from, and clear enough that you recognise your own process in it.
Prompt wording, evaluation criteria, output formatting and the order the checks run in. None of it left on a default setting. A few words changed in a prompt can move output accuracy by a point or two, and across a few thousand documents, a point or two is a lot of work to get wrong.
An agent that holds the same standard on its thousandth run as on its first.
The stack our parent company, Synextra, has worked in for many years. Your agent runs in an isolated Azure tenancy rather than on shared infrastructure. Senior engineers do the build, not juniors learning on your project.
An agent on foundations suited to work that has to stand up to scrutiny.
Running the agent against real past cases. Checking its outputs against the standard you agreed. Refining the prompts and criteria until the results hold up. Most AI fails the first time it's tested seriously, so we do that testing before go-live, not after.
An agent you can rely on from its first day of real work.
It would be easy to assume our job finishes the day the agent goes live. It doesn't. An agent sits inside a process, and a process keeps moving, so an agent that nobody maintains slowly stops matching the work.
We run every agent we build. Monitoring its output, retraining it as your process changes and holding quality to the standard you agreed. The build gets the agent working. The running keeps it that way. Together, they're what makes this a managed AI agent service, not a one-off project.
A custom AI agent sounds like a long, expensive project billed by the hour. This one isn't priced that way.
You start with a refundable commitment per agent. It's a prepayment that goes towards the work, returned in full if we don't deliver the agent to the spec we agreed. There's no separate build fee stacked on top.
Once the agent is live, you're billed per output: a finished piece of work the agent produces. And if an output doesn't meet the standard we agreed, you're not billed for it.
Here's an example. A firm wants to automate the first review of incoming applications, the kind of form a person fills in and submits for a decision.
It looks like a three-step job: someone reads the application, checks it for problems, and either passes it on or sends it back.
Seven versions of the form in regular use. Two checks that depend on a second system. A rule that changed last year that staff still apply inconsistently by hand.
A configured template tool handles the surface and misses the rest. An engineered agent is built around every version of the form, the cross-system checks, and the rule as it stands now, then tested against real past cases until its results hold. What goes live does the actual process, not a simplified sketch of it.
This is an illustrative example. Every build is scoped around your firm's own process during a Discovery session.
Built around your process. There are common patterns we reuse, but the agent itself is engineered for one specific way of working. A generic tool configured to roughly fit is a different thing, and usually the thing that disappoints.
The build covers the agent itself. Connecting it into the systems you already run, your CRM, case management or document platform, is the AI integration side of the work. It's scoped alongside the build so the two fit together, rather than being bolted on afterwards.
It depends on the process, mostly on how many steps it has and how much genuine judgement sits inside it. We scope that before quoting anything, and there's no rate card. The priority is getting the agent right before it goes live rather than hitting an arbitrary date, because an agent that's rushed out is an agent you can't rely on.
You're the source of how the process actually works, so we'll need time with the people who run it, during scoping and again during testing. You won't need engineers, and you won't need to manage the build. That part is ours.
Yes. Mapping the process is part of scoping the build. If you're earlier than that, still working out which processes are worth automating at all, that's more of an AI readiness conversation, and a good thing to raise in a Discovery session before any build starts.
It goes into service, and we run it: monitoring output, retraining as your process changes and keeping quality where it needs to be. A built agent that nobody maintains drifts out of date, so the build and the running are designed to go together.
Book a free Agent Discovery Session. Thirty minutes, no obligation. Bring the process you've got in mind, and we'll map what building an agent for it would actually involve, and tell you honestly if it isn't worth building.