AI process automation with language models
We put LLMs to work in document workflows, support and analytics. We start with the processes where the effect is measurable in hours and money, not with a “chatbot for the sake of a chatbot”.
Who we work with and what we solve
What’s included
We cost the operations you run today and pick the process where the effect can be measured.
A working prototype on real data, compared against your baseline figures.
Answers drawn from documentation, procedures and past projects — with links to the source.
We embed it in ERP, CRM, the ticketing system and email — where people already work.
Limits on autonomy, human review, metrics for accuracy and cost.
Rules for working with AI, reviews of what went wrong, handing ownership of the process to you.
Process
Timelines are a guide for a mid-sized company — we fix exact dates after the audit.
We pick the process and pin down baseline figures in hours and money.
A prototype on real data, compared against the baseline metrics.
Integrations, quality controls, autonomy widened step by step.
We track accuracy and cost and keep developing the process.
Stack and tools
Questions
Will our data be used to train models?
No. We use APIs with training on your data switched off and, where needed, masking or local models inside your own perimeter.
How do we know it pays off?
Before the pilot we pin down what the process costs in hours and money, afterwards we compare. You decide on production from the numbers.
Doesn’t the model get things wrong?
It does. That is why we build controls around it: a person confirms the critical steps, accuracy is measured continuously, and the limits of autonomy are set explicitly.
Which process should we start with?
A high-volume, routine one: sorting incoming documents, first-line support replies, preparing recurring reports.