AI agents

Where an agent actually saves time

The best candidates are repetitive tasks with clear data behind them. If someone spends an hour a day retyping orders from emails, or answering the same status questions over and over, there is room for an agent. These are the cases we come across in manufacturing, warehousing and retail:

Sometimes an agent is the wrong answer. If the data lives in personal spreadsheets, if every employee does the job differently, or if the decision rests on judgement nobody can put into words, that has to be sorted out first. Occasionally a plain report or one well-built integration solves the problem for less money, and when that is the case we say so.

• Customer orders arriving by email or web form are read and turned into a draft order in the ERP. A person only has to confirm it.
• Questions in plain language: “How many of item 4410 did we sell in Plovdiv this month?” The agent writes the query against your database or BigQuery and answers with the source attached.
• Alerts when something drifts: a late delivery, stock below its minimum, a machine with no report from the last shift.
• An internal assistant that knows your own policies, price lists and procedures rather than general knowledge from the web.
• Cross-docking and loading prep: the agent matches inbound shipments against outbound orders and proposes an allocation.

How the agent reaches your data, and where it stops

An agent is only as useful as the data it can reach. We connect it through your existing APIs, through read-only database access, or through a thin layer we write for the purpose. If a system has no API at all — an old warehouse package or a till system, say — we build one. That is exactly why we build both the agents and the integrations underneath them.

Permissions are spelled out. By default the agent can only read. Anything that changes something — a new order, a price change, an email to a customer — goes past a human for approval until you decide that a particular kind of action can run on its own. Every answer and every action is logged: what was asked, which data was used, what was done and on whose behalf.

What the work looks like

We start with one process, not ten. We come over, watch how your people handle it today and collect real examples: twenty order emails, a list of the questions that come up most, the report someone builds every Monday morning. We tune the agent on those and keep them as a test set, so before anything goes live we know how it copes with your cases rather than invented ones.

The first version works alongside your staff. It proposes; they decide. Within a few weeks it becomes clear where the agent goes wrong, what it is missing and which actions are safe enough to automate fully. Only then do we widen its scope.

What you get

A working agent tied into your systems, with its interface where your team already works — a web panel, email, your internal chat or the ERP itself. You also get a written description of its permissions, the action log and the test set we use to check every change. The integration code and the agent's configuration belong to you.

• An agent for one defined process, tuned on your real examples.
• Connections to your ERP, warehouse, inbox or database, including new APIs where none exist.
• A log of every answer and action, open to inspection.
• A permissions table: what the agent may do alone and what it only suggests.

After go-live

Language models get updated and your processes change. We monitor the quality of answers, rerun the test set whenever the model changes and add the new cases the agent has met. When someone on your team flags a wrong answer, it becomes an example the agent is corrected against. Support for critical processes is 24/7; new actions and new data sources are added in short stages, like everything else we do.