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AI agents that act in your stack.

Read a ticket, follow your rule, write the result back. Guardrails and human review where it matters.

What one run actually does

An agent is a sequence, not a reply. This is the shape of every one we ship.

  1. 01

    Read

    Pulls the ticket, the matching record, and your own documents.

  2. 02

    Decide

    Follows the rule you wrote, and cites what it used to get there.

    High stakes: a person approves before anything sends.

  3. 03

    Act

    Writes the result back into the CRM, the queue, or the ticket.

  4. 04

    Log

    Every step is stored, so a run can be reviewed and replayed.

A chatbot replies. An agent finishes the job.

Most organisations do not need another chat window on the homepage. They need the work to be done and written back.

What a chatbot does

  • Answers in a box and stops there.
  • Forgets the thread when the tab closes.
  • Cannot touch the record it is talking about.

What we build instead

  • Opens the ticket and updates the record.
  • Follows the rule you wrote down, and cites it.
  • Leaves a log you can replay next quarter.

Ship the job, not a demo

We scope one high-value workflow first. Qualify a lead, summarise a case, draft a support reply for review. Then we add tools and tests around it. The agent is designed against your data and your permissions, and it is tested on messy real inputs rather than a clean demo set. If it cannot complete the job on real tickets, it does not ship.

Where it runs depends on the job. Inside your product when a person needs to see it, on a queue when it runs unattended, or on n8n when it has to touch many tools at once and the orchestration needs to stay visible. You can see the steps, turn a single tool off, and replay a run when something looks wrong.

When the model is part of something you ship, an assistant in your app or document Q&A over your corpus, we ground answers in your sources and log what was asked. Custom machine learning for forecasting, scoring and routing waits until you have labelled history worth trusting.

What sits around the model

Grounding

Answers come from your own documents and records, with the source attached. If it is not in there, the agent says so rather than inventing something plausible.

Permissions

The agent inherits the logins and record-level access your people already have. No shared super-user quietly reading everything for everyone.

Review

Anything that could send money, medical or legal text waits for a person. You decide where that line sits before we build, not after an incident.

Replay

Every run is stored with its inputs and its tool calls. When a result looks wrong you can reproduce it, instead of arguing about what the model might have done.

When we will not use a model

If a rule, a filter, or a spreadsheet already does the job, we will say so. We do not wrap a simple step in an LLM to make a proposal look current. The cheapest accurate system is the one you should ship, and sometimes that is three lines of logic in the tool you already pay for.

Questions we get before a build

No. We build agents when the job is an action: update a record, send a draft, open a ticket, not only a reply in a box. Chat UI stays where a person still needs to talk to the system.

Not covered here? Ask us directly. No obligation.

Start with one workflow.

Tell us what you are working on. We reply within one business day.