Assistants, document pipelines and automations built into the tools your team already uses. Grounded in your data, checked before they act, measured on real outcomes.
Much of a company’s work is reading and deciding: emails, invoices, contracts, tickets, manuals. Language models can now take on a useful share of it. They answer from your own documents, turn paperwork into clean data and handle routine steps inside your systems.
We start with one real task and a clear way to measure it. The model sees only the data and tools it needs, every answer points to its source, and anything risky waits for a person. It ships when it beats the current way of working on the numbers we agreed on.
Answers from your own documents, with a source behind every claim.
How we build RAGInvoices, orders and contracts sorted, read into fields and checked.
Routine requests handled end to end. Anything unclear goes to a person.
Every change scored on real test cases before it reaches your users.
Each request goes to the smallest model that handles it well, with repeats served from cache.
Fast, affordable LLMsInside the website, app or ERP your team already works in.
We read the page, pull out each field with a confidence score and check the numbers against each other and against your ERP. A person steps in only where the model is unsure.
An agent works in small steps. It reads the request, calls a tool, checks what came back and repeats until it has the facts. It can only use the tools we give it, and anything that spends money or can’t be undone waits for a person.
Tell us where it is today, what you’re trying to achieve and what’s getting in the way.