We build agents wired into your systems that do the work rather than talk about it. With human control, evaluation up front and a documented handover.
An agent is not a chatbot
A chatbot answers questions. An agent does the work: it reads your CRM, drafts the proposal, files it where it belongs and tells the person who has to sign it. The difference isn’t the model underneath — it’s that the agent has access to your tools and permission to act in them.
The difference is not the model: it is access to your tools and permission to act in them.
That is also why most corporate AI projects never reach production. A licence gets bought, a chat window opens, and everyone is left to invent a use for it. With no access to real data and no specific job to do, the result is one more tool that nobody opens after week three.
What we do
Agents wired into your systems. We build agents on Claude, GPT and whichever models suit each task, connected over MCP or API to your CRM, your inbox, your document store, your spreadsheet or your database. The agent doesn’t “know” your company: it reads your data when it needs it.
One specific job, not a generic tool. We start from a task someone does by hand today and that fits in a sentence: qualify inbound leads, prepare the weekly report, answer the first email of a quote, check invoices against the contract.
Human control where it matters. We decide with you what the agent does alone and what goes past a person before it leaves the building. An agent that drafts and waits for approval gets adopted; one that sends without asking gets switched off after the first scare.
Evaluation before trust. We build a set of real cases with known correct answers and measure the agent against them. Without that test there is no way to know whether it works — only the impression of whoever ran the demo.
Documented handover. The agent stays in your house, with its instructions, its permissions and its limits written in plain language, so your team can change it without calling us.
How it starts
Before building anything we look at which tasks your team repeats, how many hours go into them and which ones have a verifiable answer. That last part is what decides: a task whose output nobody can score cannot be automated safely, however repetitive it is.
Out of that comes a short list ranked by hours saved and by risk. We start with one, put it into production and measure it for a few weeks before touching the next.
What we measure
We agree the number before we start. Depending on the case it is usually hours freed per month, response time to the customer, share of tasks the agent closes without a human, or errors caught that used to slip through.
We don’t report “number of model calls” or cost per token as if they were results. They are costs, we watch them, but they are not what you bought.
Why us
We use agents in our own work every day: in campaign data analysis, in content production and in maintaining the sites we run. What we propose is what already works inside the agency, not a service line invented for the brochure.
We also know where it breaks. An agent with access to your systems can do damage if it is given broad permissions and allowed to read text that comes from outside: an email or a document can carry instructions aimed at the model. So we separate reading from writing, scope permissions per task, and log everything the agent does.
