AI agent development that reaches production - not another stalled pilot.

Software that uses an LLM to make decisions and take actions in your systems: reading a ticket and routing it, qualifying a lead and booking a call, extracting a document and updating a record. The difference from a chatbot is action - agents call your APIs and change state. We design the agent, connect it to your tools, and ship it with evaluation, guardrails and human handoff so it can be trusted with real work.

Sometimes it is hype. If the task is deterministic, a script or a workflow tool is cheaper and more reliable, and we will say so on the scoping call. Agents earn their keep when the work needs judgment at volume: unstructured input, variable steps, decisions a rule engine cannot express. Ticket triage, lead qualification and document processing are proven cases. If yours is not one of them, we will tell you before you spend anything.

We quote a fixed price after one scoping call, based on the workflow, the systems the agent touches, and the accuracy bar it has to clear. No hourly billing and no open-ended discovery phase. The quote includes the evaluation harness, guardrails and handoff logic, because an agent without them is not finished. Running costs are part of the design too: we set a monthly API budget with you and build the agent to stay inside it.

Because the demo is the easy part. A pilot that works on ten hand-picked examples says nothing about accuracy at real volume, what happens when the agent is wrong, or what the API bill looks like in month three. Most pilots skip evaluation, guardrails and handoff because they are invisible in a demo. We build those first, which is less impressive in week one and the reason the agent is still running in year one.

Three layers. Guardrails restrict what the agent can do at all: which tools it can call, which records it can touch, hard limits on actions like refunds or emails. Confidence thresholds route uncertain cases to a human instead of guessing. And the evaluation harness measures error rates on your real data before launch, so you know where it fails and can decide which decisions it is allowed to make alone. High-stakes actions always keep a human approval step.

You own everything: the code, the prompts, the evaluation data and the infrastructure setup transfer to you at handover, with no license fees or lock-in to us. On data: we work under EU contracts, Qorinx is based in Tallinn, and we design for GDPR from the start - EU model hosting options, data minimization in prompts, and documented processing so your compliance team gets answers instead of shrugs.

Yes, that is the normal case. Agents are only useful when they act inside the systems you already run: your helpdesk, CRM, ERP or internal APIs. We integrate with what exists rather than demanding a migration first. Messy data is fine to a point - the evaluation harness will show exactly how much accuracy it costs, and if the honest answer is that the data needs cleaning before an agent makes sense, you will hear that on the call, not after the invoice.