AI automation for the business processes you already run

Classic process automation follows fixed rules: if an invoice matches its order, approve it. It breaks when inputs vary, documents arrive in different formats or a step needs judgement. AI automation adds models that read documents, classify requests and draft text, and agents that carry a case through several systems. Many processes need a mix of these, plus people:

• Rules and workflows for steps that are the same every time
• Language models for reading documents, extracting fields and classifying requests
• Agents for cases that pass through several systems and need some judgement
• People for decisions that the law, your policy or the risk reserve for them

Good candidates have volume, a clear outcome and data a system can reach. Poor candidates are work that happens a few times a month, steps whose correct outcome nobody can state, and processes that are about to change anyway.

• Incoming documents such as invoices, claims, orders and contracts, read and checked against your systems
• Customer and IT requests sorted, enriched and routed to the right team
• Back-office checks such as invoices against orders, or onboarding documents against requirements
• Reports and summaries assembled from several systems
• Transfers between systems that today depend on copy and paste

Take an accounts payable team that receives supplier invoices as PDFs by email. Today each invoice is opened, typed into the ERP system and matched against its purchase order. Mapped out, the process has five steps. Collecting the attachments and posting matched invoices are rules. Reading the invoice fields goes to a model, tested on several hundred past invoices. Matching against the purchase order is a rule again, with tolerances agreed with finance.

Invoices that don't match, or where the model is unsure, go to a person with the reason shown. The figures that matter to finance are the cost and error rate per invoice, before and after.

We build in your environment and connect to your systems through their APIs, the integration tools you already use or MCP servers. Models run through providers your data owners have approved, or as open-weight models on your own infrastructure. Every automated step writes a log, and every write to a business system can require approval until the test results justify removing it.

• A process map showing which steps a rule, a model, an agent or a person handles
• A test set of past cases with known outcomes, run again before every change
• Monitoring of cost per case, error rate and the share of cases passed to people
• Runbooks, and an owner on your side for each automated process

Some AI automation agencies build workflows in a no-code tool and hand them over, which suits simple, low-risk automations. When a process touches finance, customer data or several core systems, it needs tests on past cases, access limited per step, logs an auditor can follow and a team that can change the automation when the process changes. That is the work we take on. Where a simple workflow tool is enough, we say so.

Scope and price follow the first call. The cost depends on how many steps and systems a process touches, how varied the incoming documents are, how strict the accuracy requirement is, and whether models run with a provider or on your own infrastructure. We report the running cost per case so you can compare it with what the process costs today.