Internal AI tools grounded in your company’s information.
Jegnia builds knowledge assistants, research workbenches, and analysis tools that help teams work with their own documents and data. Our Toronto-based team combines retrieval, application engineering, and review controls for businesses in Canada and the US.
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An internal tool is useful when people repeatedly search the same sources, assemble context, or summarize information to make a decision. The first version should have a defined audience and a narrow set of questions it is expected to answer.
Adding a chat interface will not resolve contradictory policies or inaccessible source material. If nobody owns document quality, the first task is to agree on trusted sources, permissions, and how outdated information will be replaced.
How we build the system
Define users, sources, and boundaries
We identify who will use the tool, the questions it should handle, and the systems that hold the relevant information. Access rules belong in the retrieval design, so a useful answer does not accidentally reveal material a user is not allowed to see.
Retrieve evidence before drafting
A knowledge assistant can retrieve relevant passages from approved sources and provide citations alongside its answer. We design behavior for missing evidence, conflicting documents, and stale information rather than treating every confident-sounding answer as correct.
Build a usable review interface
Teams need more than a text box. The interface can show source passages, filters, saved research, and review states. Actions that change company records or send information externally need explicit controls appropriate to the workflow.
Evaluate with representative questions
We test whether the system finds the right evidence, respects access boundaries, and answers questions accurately enough for its intended use. Evaluation examples include questions it should decline or escalate, not just questions with easy answers.
What the scope can include
Information architecture, retrieval design, secure application, evaluation suite, and training.
Tools and integrations
Document stores, knowledge bases, PostgreSQL, vector search, authentication, and permissions.
Example: a recruiter evidence workspace
Our recorded recruiter-tool build turns a candidate CSV into a workspace for reviewing role requirements, supporting evidence, missing information, and outreach drafts. The walkthrough separates local prototype features from the access controls and storage needed for production.
Read the recruiter workspace walkthroughAgree on what success looks like
We establish a baseline before launch and choose measures that reflect the work your team needs to improve.
- Answers supported by the cited source
- Relevant source retrieval on a reviewed question set
- Correct refusal and escalation behavior
- Time to a reviewed answer compared with the current process
Bring this to the first conversation
Bring the questions your team repeatedly asks, a sample of approved source material, and the access rules for different roles. Avoid sharing sensitive production data before a secure handling process has been agreed.
Common questions
What is retrieval-augmented generation?
Retrieval-augmented generation, often called RAG, looks up relevant information before an AI model drafts an answer. It can help connect an answer to company sources, but source quality, permissions, and evaluation still determine whether the result is useful.
Can the assistant cite its sources?
Yes. Source citations and review flows are part of the internal tools we design. A citation helps a reviewer check the answer; it does not by itself guarantee that the answer correctly interprets the source.
Can different teams have different access?
Yes. We design authentication and role-based access around your existing rules. Source-level permissions and retrieval behavior need to be tested as part of the application, not added only to the interface.
Will our data be used to train a model?
Data handling depends on the providers, deployment, and contract selected for your project. We review these requirements during architecture and document the intended data flow before connecting company information.
Related services
Tell us where the work gets stuck. We’ll discuss the next useful step.
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