AI agents that act in your systems, not just chat about them.
An agent earns its name when it can take an action and be held to the result. The difference between a chatbot and an agent is not the model — it is whether it has tools, permissions, and a record of what it did.
We build agents around a specific job: qualify this lead, reconcile this invoice, prepare this brief. Narrow scope is not a limitation, it is what makes an agent reliable enough to leave running. An agent with four well-defined tools and a clear success condition will outperform one with thirty tools and a vague mandate, every time.
Each agent gets its own credentials and its own blast radius. It can read what it needs and write only where you have said it may. Every action it takes is logged with the reasoning that led there, so when something looks wrong you can find out why rather than guessing at a prompt.
What we build
Tool-using agents
Agents wired into your CRM, inbox, database, and internal APIs — able to look something up, cross-check it, and write the result back where your team will see it.
Retrieval over your own knowledge
Grounded in your documents, tickets, and product data, with citations back to the source so an answer can be verified instead of trusted blindly.
Multi-step task execution
Jobs that take several dependent steps and need to recover sensibly when step three fails, rather than starting over or stopping silently.
Scoped permissions and audit trail
Per-agent credentials, explicit write scopes, and a log of every action with its reasoning — the difference between an agent you can deploy and a demo you cannot.
Evaluation before it goes live
A test set drawn from your real historical cases, so we can measure whether a prompt or model change actually improved things instead of relying on it feeling better.
How the engagement runs
- 01
Define the job
One agent, one outcome, one success condition you can check.
- 02
Build the tools
The actions it can take, scoped and permissioned, before any prompting.
- 03
Evaluate
Run it against historical cases you already know the answer to.
- 04
Deploy behind approval
Live with a human gate on consequential actions; loosen it as the record earns it.
This is a good fit if
- A repeated decision that needs context from more than one system
- Work where a knowledgeable person takes 10-40 minutes and follows a describable method
- You can supply historical examples of the task done well
- Tools with APIs the agent can be given scoped access to
We’d turn this down
- Open-ended "do anything" assistants — scope is what makes agents trustworthy
- Decisions with legal or safety consequences that cannot carry a human approval step
- Workflows where nobody can articulate what a correct answer looks like, because then nothing can be evaluated
Questions we get about this
- How is this different from an AI chatbot?
- A chatbot answers. An agent acts — it calls tools, changes records, and produces a result you can audit. The engineering effort sits in the tool definitions, permissions, and evaluation harness, not in the conversation.
- Which model do you use?
- Whichever fits the job, and we design so it can be swapped. Model choice moves faster than any engagement, so the tool interfaces, evaluation set, and permission model are built to outlive the specific model behind them.
- How do we stop an agent doing something we did not intend?
- Scoped credentials, an explicit list of permitted write actions, and approval gates on anything consequential. The agent cannot reach systems it was never given access to, and the audit log means an unexpected action is diagnosable rather than mysterious.
- Do you use our data to train models?
- No. Your data is used to ground and evaluate your agent. Every engagement starts with an NDA and a contract assigning you full IP ownership of what we build.
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