AI assistants on your own data
The answers your staff and customers look for are often already written down somewhere: in a policy, a manual, a contract, an old ticket or an internal system. Finding the right one takes time.
We build assistants that search this material, answer in plain language and show where each answer came from. Access follows the permissions you already have. Nobody sees a document they aren't allowed to open.
What an assistant on your own data does
A general chatbot answers from what it learned in training. An assistant built on your data answers from your own documents and records instead. When a question comes in, it first finds the relevant passages (retrieval, often called RAG), then writes the answer from them.
This matters most when the answer has to be right for your organization: which version of a procedure is current, what a specific contract says or where an order stands.
What we build
Internal knowledge assistant
Answers staff questions from policies, procedures and handbooks, with a link to the exact paragraph it used.
Customer support assistant
Handles routine customer questions from your help content and order data, and passes the conversation to a person when it should.
Technical documentation assistant
Helps engineers and field teams find the right section in long manuals, specifications and service notes.
Contract and policy lookup
Finds clauses, compares versions and answers questions about what a document says, quoting the text.
Assistant inside existing tools
Placed where people already work, such as an intranet, a support desk or a messaging tool.
Onboarding assistant
Answers new staff questions about tools, processes and who to ask, without waiting for a colleague.
When an assistant is the right choice
An assistant pays off when people keep searching for answers that already exist somewhere in writing. Something simpler is often better when:
- the questions are few and stable enough for a well-organized help page or search box
- the answer depends on a calculation or a rule that ordinary software can apply exactly
- the source documents contradict each other and nobody owns keeping them current
- the main need is to take actions in other systems, which is closer to an AI agent than an assistant
How a project runs
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Collect the questions people ask
We gather questions from tickets, emails or interviews and decide with your team what a good answer looks like for each.
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Sort out the sources
We decide which documents are authoritative, which are outdated and who may see what. This step often matters more for quality than which model is used.
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Build and test against the question set
We test the assistant on the collected questions before anyone uses it and go through every wrong, incomplete or unsourced answer.
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Pilot with a small group
Selected users try it in their daily work. Before it goes to more people, we fix the problems their feedback and the failed questions point to.
Keeping answers trustworthy
The main risk with an assistant is an answer that sounds right and is wrong. We design and test the assistant with that risk in mind.
Every answer has a source
Answers link to the passages they are based on, so people can check them quickly.
It can say "I don't know"
When the sources don't cover a question, the assistant says so or hands over to a person instead of guessing.
Permissions are respected
Search results are filtered by the user's access rights before the answer is written.
Quality is measured on every change
A fixed set of test questions is re-run whenever documents, settings or the model change. Regressions show up there before users run into them.
What you receive
- A working assistant connected to the sources and tools in scope
- The set of test questions and the assistant's results on them
- A simple way to add, update or remove source documents
- Documentation on how to run and maintain it
Frequently asked questions
Can an AI assistant use our documents without sending them to an outside provider?
Yes. The assistant and its documents can stay on your own servers or private cloud. If you'd rather use an outside model provider, we agree with you in advance on what can be sent to it.
What do we need to build an assistant on our own documents?
Access to the documents or systems the assistant should use, a sample of questions people have asked and someone who can judge whether the answers are right.
How soon can we try the assistant?
A first version for a narrow topic can usually be tested early in the project. How quickly it reaches wider use depends mostly on how tidy the sources are.
Can the assistant answer from business systems as well as documents?
Yes. Besides your document stores, it can read from your existing business systems, for example to answer with the current status of an order.
Work with us
Tell us in a few lines what you're working on. We'll get back to you.
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