Nearly every project is one of these four, or a combination of them. AI shows up inside all four where it helps, and stays out where it doesn't.
One thing happens, so somebody does five others because of it. An order comes in, so a person updates the sheet, emails the warehouse, checks stock, tells the customer and adds it to the report. We build the system that does those five things — with the approvals your business actually requires and a record of what happened. Steps that need judgment stay with a person. Steps that just need doing, don't.
Invoices, forms, applications, statements, field reports, contracts, emailed attachments. We read the document, pull the fields out, check them against your rules, and route anything doubtful to a person rather than guessing. This is where a language model genuinely earns its place — documents almost never arrive in the same shape twice. Everything around it is ordinary code that can be tested.
Data pulled from several places, joined by hand, checked against last month, then formatted for whoever receives it. Or two sets of records that have to be matched, with someone chasing the differences and writing up why. We build the ingestion, the matching, the validation and the exception queue — and test it against your existing output before you rely on it. If it can't reproduce what you already know is right, it isn't finished.
Sometimes the honest answer is that no product does what you need, and bending the business around one is costing more than building the right thing would. A job tracker shaped like your floor. A portal your customers will actually use. An internal tool that replaces four spreadsheets and the person who reconciles them. Built to be handed over: documented, and maintainable by someone who isn't us.
Not sure which one you have? That's the first conversation. We find the process costing you the most, and start there.