Faster discovery
Retrieve relevant passages across documents and systems without requiring users to know the exact filename or terminology.
Grounded knowledge access
The chatbot is the surface. Information quality is the system.
Business knowledge is often split across shared drives, intranets, manuals, policy documents, product files, CRM notes, tickets, project folders, and the experience of a few long-tenured employees. Search returns too much or too little, and people spend time asking the same questions or recreating work that already exists.
A knowledge assistant can retrieve relevant passages from approved sources and use a language model to produce a focused response. Reliability depends on source selection, document preparation, permissions, metadata, retrieval, freshness, evaluation, citation behavior, and a clear boundary between answering and taking action.
Naper Solutions designs the assistant as part of an operating workflow. We identify the questions users actually ask, the evidence a good answer requires, what content each person may access, how updates become available, and when the assistant must defer to a subject-matter expert.
Knowledge workflow
Reduce search time while keeping people close to the source.
The goal is faster, more consistent access to approved information—not an assistant that sounds certain about everything.
Source-grounded answers
Present citations, links, or excerpts that let users check the underlying policy, procedure, or record.
Permission-aware access
Respect audience, role, system, and document boundaries so retrieval does not bypass existing controls.
Managed freshness
Establish how approved content is added, updated, removed, re-indexed, and monitored over time.
Scope
Retrieval, assistant experience, evaluation, and operations.
We can deliver a focused internal assistant or embed grounded knowledge into an existing application, portal, support tool, or workflow.
Knowledge and retrieval layer
Prepare and connect the approved information so relevant evidence can be found consistently and securely.
- Source inventory, permissions, metadata, and content lifecycle
- Document processing, chunking, indexing, and retrieval
- Hybrid search, filtering, ranking, and context assembly
- Connectors for files, databases, CRM, tickets, or custom systems
Assistant and quality system
Turn retrieval into a usable, measurable experience with appropriate guidance and escalation.
- Conversation or embedded application experience
- Grounded answer prompts, citations, and refusal behavior
- Evaluation set for representative and adversarial questions
- Feedback, analytics, monitoring, cost controls, and support
How we work
Evaluate retrieval and answers against real questions.
A polished demo is not enough. The assistant must find the right evidence for the questions and permissions that exist in day-to-day work.
Define the job
Identify users, recurring questions, approved sources, permissions, evidence needs, risks, and escalation paths.
Prepare and retrieve
Connect content, add metadata, design indexing, and test whether the right passages are found.
Answer and evaluate
Build the assistant experience and score groundedness, completeness, citation quality, safety, latency, and cost.
Pilot and govern
Release to a controlled audience, review failures, manage content freshness, and monitor production behavior.
1999
Built for continuity, not a handoff.
Naper Solutions has solved business technology problems since 1999. The same senior team can carry an engagement from assessment through implementation, deployment, and ongoing support.
Common questions
AI knowledge assistant questions.
What is a retrieval-augmented generation assistant?
Retrieval-augmented generation, often called RAG, first finds relevant information from approved sources and then gives that context to a language model for the response. It can improve grounding and freshness, but retrieval and answer quality still need explicit testing.
Can the assistant cite the source of an answer?
Yes. The experience can include document names, links, page or section references, and relevant excerpts where the source system and format support them. Citation usefulness should be part of the evaluation, not added only for appearance.
Can different employees see different information?
Yes, if the source systems and solution architecture provide reliable identity and permissions. We design retrieval filters and access checks so the assistant does not turn a search experience into a route around existing controls.
How do you reduce hallucinations?
We limit the task, retrieve approved evidence, require grounded responses, test representative and difficult questions, present sources, define refusal behavior, and monitor failures. No single prompt eliminates risk, so the workflow must help users verify important answers.
Does our information train a public AI model?
That depends on the selected vendor, service tier, configuration, and contract. We review data-use and retention terms as part of architecture selection and can design for approved enterprise services, private networking, redaction, or other controls when required.
Related expertise
Keep exploring.
The strongest solution often combines several disciplines. These pages go deeper on adjacent work.
AI solutions
Build a practical AI roadmap across knowledge, documents, email, applications, and operations.
Learn moreAI document processing
Classify, extract, validate, and route information arriving in documents and attachments.
Learn moreCustom software
Embed the assistant into a secure application, portal, dashboard, or line-of-business workflow.
Learn more
Start with the real problem
Bring the questions, the sources, and the people who judge a good answer.
We can help you assess the information, prototype retrieval, build an evaluation set, and deliver an assistant users can verify and operate.