Building an agent is easy. Getting it to work in production is not.
What separates the two is almost never the model. It is whether the knowledge the agent needs exists only inside your company, and whether its output is supervised and governed for the regulator.
When it fits
An agent fits our technology when these three conditions hold at the same time:
- The knowledge it needs is yours. None of it is on the internet: your policies, your history, your engineering criteria, the decisions nobody wrote down.
- Someone on your team has to approve the result. And to approve it they need to see where every piece of data came from and which version of the criteria was applied, even if the question comes six months later.
- The volume makes reviewing by hand impossible. If it is ten cases a month, hire someone. We are worth it when it is a hundred thousand a year.
The cases
Technical document validation
Can AI validate construction certifications to an engineering standard?
Answers from the internal rulebook
Can an employee ask the internal rulebook a question and trust the answer?
Contract review against your own playbook
Can AI review a contract against your legal team's own criteria?
First-line support that does not invent
Can an agent answer a customer without making the answer up?
Case file processing
Can AI process a case file and leave a record of why it decided what it decided?
Technical knowledge in the field
Can a field technician find in a minute what is buried in ten years of documentation?
Contact
Does your case look like one of these? That is the best way to start the conversation.
Talk to us