AI Agent Development

Repetitive tasks that need some reading and judgment: processing supplier invoices and delivery notes into the ERP, answering order and account questions on WhatsApp and email from live data, logging and routing enquiries in the CRM, preparing reconciliations for review, and pulling answers from several systems into one reply. The best first workflow is one with high volume, clear rules for what a good result looks like, and an obvious person to approve the output.

Yes, as long as the system can be reached through an API, a database view or a supported integration. We give the agent a small set of tools (for example look up an order, create a draft invoice, update a contact), each with its own permissions, so it works on live data without broad access to everything. If your ERP has no API, we look at a secured integration layer first and treat screen automation as a last resort.

The main risks are a document read wrongly, an action taken on the wrong record, instructions hidden in an incoming email or file (prompt injection), data reaching someone who should not see it, and an agent looping or running up model costs. We control them with narrow tool permissions, validation of every output against your rules, confidence thresholds that send uncertain cases to a person, approval steps for any action that changes data or reaches a customer, input filtering, spending and step limits, and an evaluation set that is re-run after every change.

Wherever you want one to. You decide which actions the agent may complete on its own (for example a read-only lookup) and which must wait for approval (posting an invoice, changing a price, sending a message to a customer). Approvers see what the agent read, what it proposes and why, and can approve, edit or reject it. Many teams start with approval on everything and relax it only for steps that prove reliable.

Yes. Every run is logged: the input it received, the data it looked up, the decision it made, each tool call, the output and the person who approved it, with timestamps. The log is searchable, so you can answer "why did this invoice get posted?" from the record rather than from memory.

You choose one workflow. We build the agent for it as the 2 to 3 key modules that prove it, such as intake, the ERP or CRM lookup and the approval screen, and run it on a sample of your real data, free of charge. Your team tests it and sees where it is right and where it needs a person. The full build starts only after you approve the pilot.

The pilot measures how often the agent gets your sample cases right, shows the integration with your systems working, and lets your reviewers try the approval flow. It does not prove accuracy on case types missing from the sample, behaviour at full production volume, or running cost at scale. Those are checked in a controlled rollout after you approve the full build, with the test set re-run as real cases arrive.

We scope the data path before building and document which data goes to which provider. The agent uses a provider-agnostic model layer, so it can run on a commercial model API with the retention settings you choose, where the provider offers them, or on self-hosted open-weight models when data must stay under your control. Access is limited by role and every call is logged. If you work under specific data-protection rules, we design to support those obligations; compliance remains your organization’s responsibility and is confirmed by your own assessment.