AI & Automation
AI automation means handing repetitive, judgement-light work to software: reading documents, drafting replies, routing requests, moving data between systems. The value is not the model. It is the hours it removes from a person who should be doing something else.
How we approach it
Find the workflow that pays for itself
One process, measured before we touch it. If we cannot state what it costs today, we cannot prove it improved.
Ground the model in your data
A model answering from general knowledge will be confidently wrong about your business. It has to read your documents, your records, your rules.
Design for being wrong
Confidence thresholds, human review on the cases that matter, and an audit trail. The question is never whether it will make a mistake but what happens when it does.
Measure cost per run
Token cost at real volume, not demo volume. Plenty of AI projects work and are still not worth running.
Common questions
What business processes are worth automating with AI?
Anything high-volume, rule-heavy and low-judgement: intake forms, document classification, first-line support replies, data entry between systems. Work needing genuine judgement or carrying real consequences is better assisted than automated.
Will an AI assistant make things up about my business?
It can, and that is the central engineering problem. We ground responses in your own data, constrain what the system is allowed to assert, and hand off to a human for anything commercial or contractual rather than letting it improvise.
Do we need our own AI infrastructure?
Almost never. Most businesses are best served calling a hosted model through an API. Running your own becomes worth it at high volume or where data genuinely cannot leave your environment.
Often paired with
Talk it through
Tell us what you're trying to do and we'll tell you honestly whether this is the right way to do it.