AI chatbots and agents that answer out of your data
Agents grounded in your own documents and your own records, so they answer from what you know rather than from what a model guessed.
Grounded, not generic
A general model will answer anything, confidently, including things it has no basis for. An agent worth deploying answers out of a defined body of knowledge — your documentation, your catalogue, your records — and says which part of it produced the answer.
That is the difference between a demo and a system somebody relies on. It also makes the agent checkable: if an answer is wrong, you can see where it came from and fix the source.
Knowing when to hand over
The most useful thing an agent does is recognise the edge of its competence. A question that needs a person should reach one, with the conversation already attached, rather than being answered badly.
Agents can hold a conversation, answer in the language the visitor picks, and work inside the product rather than beside it — as a copilot in a CRM, a reader in a document pipeline, or a consultation on a storefront.
Keeping one honest after launch
An agent is not finished when it answers well in a demo. Its knowledge goes stale, the questions people ask drift away from the ones it was tested on, and the failure is quiet: a confident answer that is no longer true.
So the build includes the instrumentation to see it. What was asked, what was answered, what it was based on, and where it handed over to a person. That record is what makes an agent maintainable rather than a thing everyone slowly stops trusting.
Questions we get asked
Will it make things up?
That is the failure mode the build exists to prevent. Answers come out of a defined body of knowledge and cite which part produced them, and where a question needs a person the agent hands over rather than guessing.