AI agents for operations
in complex companies.
For operations leaders at complex companies, where planning, scheduling, back office and reporting depend on facts spread across many systems.
AI agents for operations take the gathering, checking and chasing between systems, and leave the decisions with your team. They work from a shared world model of your business and the outside world, in the tools your team already uses.
Who it’s for
Operations teams that hold the company together by hand.
Ortelian fits when operations work depends on context no single system holds: the ERP, the planning sheet, supplier and customer email, the ticket queue, a regulation that changed last month. The people who run operations know how those pieces connect. Most of that knowledge lives in their heads and in spreadsheets they keep themselves.
If your team can show what a correct schedule, a clean month-end report or a well-handled exception looks like on real examples, you have a standard worth building to.
Where agents start
Jobs that eat an operations team’s week.
These are the kinds of operations jobs we look at first. The audit decides which one comes first for you. They run in the tools your team already uses. Access to the platform comes with the engagement; there is no self-serve product and no public API.
- Planning and scheduling prep
Before a planning round, an agent pulls capacity, open orders, constraints and the changes since the last round into one view, and flags the conflicts. Your team makes the plan. The agent does the gathering.
- Back-office checks
An agent checks documents against each other and against the records they should agree with, such as orders, invoices and contracts. The ones that match move on. The exceptions go to a person with the evidence attached.
- Reports that build themselves
Recurring reports pull from the same sources every time. An agent builds them on schedule, notes what moved since the last one and links each number back to where it came from.
Why it works
Agents need a map of the operation, not another window.
Operations work breaks at the joins between systems: the order in the ERP, the delivery date in an email, the supplier contact who changed last week. An agent that only has tools rebuilds those joins on every run and loses them when the run ends. Ortelian builds a world model for knowledge work: your systems of record joined with outside sources, with every fact keeping its source. Agents work from that map first and use tools second, and each agent gets only the tools its job needs. See the platform.
The platform is hosted in the EU. Your team keeps working in the systems it already uses. Ortelian hosts and operates the platform as part of the engagement.
01 · Software
The code holds everything the work needs.
That’s why AI took off in software first.
02 · Internal knowledge
A wiki holds what the company writes down.
That’s enough for questions about the company itself.
03 · Knowledge work
Your work also depends on the world outside.
Customers, competitors, regulations, people moving. We join it with what’s inside, in one model.
Inside the company The world around it
How an engagement runs
Audit, redesign, build, then stay.
A full engagement starts with a two-week audit on site that maps how the operations work actually runs, finds the constraint and redesigns the process with your team around what AI can now do. Before anything is built, we agree the standard on real examples: quality, cost, time and human effort. Then we build, test and deploy in two-week cycles until the first job meets it. We stay to host the platform and keep improving the work. This is what working with an AI-native deployment partner looks like. Full detail on how we work.
Not every step should become an agent. If a step has a fixed input and a fixed output, it stays software. Steps that need judgement within agreed limits go to agents. Approvals and changes of direction stay with your people.
Questions
Questions operations leaders ask.
Does this replace our ERP or planning tools?
No. The ERP, planning tools and spreadsheets keep doing their jobs and feed the world model. Agents work inside the systems you already use and write back only where you have agreed they may.
Which operations work fits agents best?
Work that needs judgement across several sources, such as gathering the inputs for a plan, checking documents against records or explaining why a number moved. Steps with a fixed input and a fixed output usually stay software. The audit decides where to start.
Who approves what the agents do?
Your team. Agents gather, check and draft. Approvals, changes of direction and anything that leaves the company, such as an email to a supplier or customer, wait for a person by default.
What data does it need?
Agreed access to the systems and sources the operations work depends on, such as the ERP, planning files, ticketing and email, plus any outside sources the work relies on. We agree access, model providers and data handling before we start. The platform is hosted in the EU.
How long until the first agent is in use?
The audit takes about two weeks on site. The first build then runs in two-week cycles and goes live when it passes the evaluations we agreed. How many cycles that takes depends on the work and the sources it needs.
Let’s talk about your operations.
Bring the work that takes the most chasing and where it gets stuck. We’ll work out whether an audit is worth it.
