Agents that work inside your systems, not beside them
Document extraction, reconciliation and grounded assistants — connected to your ERP through explicit tools and permissions, with every action logged.
Discuss your projectMost enterprise AI stalls at the demo because the model has no hands and no ground truth. Mont-E AI Agents are built the other way round: first the tools and the permissions, then the model.
An agent is given a narrow job, a set of tools it may call against your systems, and a boundary it may not cross. It reads the invoice and creates a draft entry; it matches the bank statement against open receivables and flags what it could not explain; it answers a question about stock by querying the ERP rather than by remembering.
Every call is logged with its inputs, its outputs and the user or schedule that triggered it. Anything that writes produces a draft a human can approve or reject, until you decide a particular task has earned the right to run unattended.
Models are pluggable and can run against a hosted API or on your own infrastructure where data residency requires it.
The repetitive read-classify-post loop that consumes an accounting team's week, done as reviewable drafts.
Ask about stock, prices, a customer's balance or an order's history in Vietnamese or English, answered from live data.
Agents surface what does not reconcile and why, instead of hiding it in a report nobody opens.
Scoped credentials, logged tool calls and approval gates — the same controls you would demand of a new employee.
