Software · Data and AI
AI agents
Software agents that carry out multi-step tasks across your systems, with people approving the steps that matter.
An agent is useful when a task needs several steps across several systems: look up the order, check the policy, draft the refund, ask a manager. We build agents with a short list of approved tools, firm limits on what they may do alone, and a person approving anything that touches money, customers or judgement. Every action is logged.
What we build
- Support agents that look up orders, check policy and draft refunds for staff approval
- Operations agents that reconcile records between your ERP, spreadsheets and email
- Research agents that gather facts from approved sources and cite every one
- Tool connectors to your APIs and databases with read-only or tightly scoped write access
- Approval queues where a person accepts, edits or rejects each proposed action
- Evaluation sets built from your real past cases to measure accuracy before launch
DemoInteractive · sample data
Try it live.
A working sample with made-up data. It runs in your browser; nothing is sent anywhere.
Ops Agent I can look up orders, check the returns policy and prepare refunds for a person to approve.
Order 48213 arrived damaged, refund it
Who has to approve it?
What it does well
Scoped tools
The agent can call only the functions you approve, with limits on amounts and records.
Human approval
Refunds, customer emails and data changes wait for a person before they happen.
Full action log
Every prompt, tool call, result and decision is stored so any run can be replayed.
Measured accuracy
We test against your real past cases and report where and how the agent gets it wrong.
Safe fallbacks
When the agent is unsure or a tool fails, the task goes to a person with context attached.
Model choice
Hosted or self-hosted models are picked per task for cost, speed and data residency.
Questions
How is an AI agent different from a chatbot?
A chatbot answers questions. An agent takes actions: it calls your systems, fills in forms and moves work along. That makes it more useful and also riskier, so we limit its tools, cap what it can change, and route anything consequential to a person for approval.
How do we know the agent is getting things right?
Before launch we run it against a set of your real past cases and compare its results with what your staff decided. You see the accuracy, the kinds of mistakes and the cases it should hand over. After launch, reviewer corrections are added to that test set.
Can it run on our own servers?
Yes, if your data rules require it. Open-weight models can run on your own GPUs or in a private cloud, often with a smaller model per task. We compare quality and cost against hosted models on your cases, so the trade-off is clear before you decide.