Employee knowledge and onboarding
Help people navigate approved policies, working practices, and internal guidance without searching across disconnected repositories.
Knowledge Assistants
Make your knowledge easier to find, understand, and use.
Important answers are often scattered across documents, internal tools, and the people who know where to look. ProwebX builds knowledge assistants that help users ask questions in context, retrieve relevant information, and inspect the sources behind an answer—with access shaped around their role.
KNOWLEDGE, WITH CONTEXT
SERVICE FLOW
Approved sources: documents, policies, product guidance and business systems.
Access-aware retrieval
Find the answer.
Show the evidence.
The assistant retrieves from approved sources, cites what informed the answer and states when the available evidence is insufficient.
What the experience includesSource context and accessDesigned around the user's permissionsView details
WHAT THE EXPERIENCE INCLUDES
- Source excerpts and citations that help users verify an answer.
- Identity and permission checks before retrieval and source preview.
- Content freshness, ownership and removal rules for connected knowledge.
- A clear path when the evidence is missing, outdated or contradictory.
Where knowledge assistants help
Less time searching.
More context for the task.
Put knowledge closer to the people who need it, in the moments when it matters.
Customer and support knowledge
Bring product documentation and support guidance into the service experience, with a clear path to a person when the available knowledge is not enough.
Product and technical enablement
Help teams compare capabilities, locate implementation guidance, and understand technical material with links back to the relevant source.
Operational knowledge access
Make procedures, playbooks, and business context easier to retrieve while preserving the ownership and permissions of the underlying information.
What we deliver
From scattered sources
to a connected experience.
Enterprise knowledge assistant development across content, retrieval, access, experience, and operations.
01
Knowledge and use-case discovery
Map the questions people ask, the decisions they need to make, and the sources that should inform an answer. Identify knowledge gaps, content owners, and useful evaluation tasks.
02
Content ingestion and structure
Connect approved repositories, extract useful content, retain document metadata, and design update and removal flows so the assistant can work with maintained knowledge.
03
Permission-aware retrieval
Design retrieval around identity, document access, and business context. Test access boundaries alongside relevance so users receive information they are authorized to see.
04
Answers with source context
Build experiences that help users read, compare, summarize, and inspect supporting information. Make citations, source excerpts, and uncertainty part of the answer experience.
05
Evaluation and feedback
Evaluate retrieval relevance, answer correctness, citation support, and behavior when information is missing or conflicting. Use representative questions and user feedback to guide improvements.
06
Knowledge operations and integration
Integrate the assistant into existing applications and workflows. Plan for content freshness, monitoring, access changes, operating costs, and ongoing ownership after release.
Trust in the experience
An answer is useful
when people can assess it.
Source context, access boundaries, and uncertainty belong in the product experience from the start.
Show the evidence
Give users a useful route back to the material behind an answer. A citation should help them verify the information, not merely decorate the response.
Respect the boundaries
Carry identity and access rules through the knowledge experience. Retrieval, answers, and source previews all need to respect the same permissions.
Make uncertainty visible
When evidence is missing, outdated, or contradictory, help users recognize the limit and find the next useful step instead of presenting a guess as certainty.
Knowledge assistant FAQ
Better access.
Clearer expectations.
What to consider before connecting AI to your organization’s knowledge.
Talk through your requirements
01When should we build a Knowledge Assistant instead of a broader AI application or agent?
Choose a Knowledge Assistant when the primary job is helping employees or customers get accurate, source-backed answers from your documents, policies, and internal repositories. If the system also needs to execute transactions across external tools, combine it with Intelligent Workflows or Agentic AI; if you are building broader generative features inside a software product, start with AI-Driven Applications.
02How is an enterprise knowledge assistant different from a generic chatbot?
A generic chatbot relies on unverified prompt responses. An enterprise knowledge assistant is engineered around your approved repositories, document structure, role-based access rules, inline source citations, and explicit refusal behavior when the underlying documents do not support an answer.
03How do you connect existing document repositories and keep permissions and content in sync?
We integrate with your approved document stores, wikis, ticketing systems, and identity provider. The ingestion pipeline tracks document updates, deletions, metadata, and access control lists so users only retrieve answers and source previews they are authorized to view.
04How do you reduce unsupported answers and evaluate retrieval quality?
We build evaluation datasets from real user questions to test retrieval recall, citation accuracy, and handling of outdated or conflicting documents. These checks run before launch and whenever chunking, indexing, or model configurations change.
05Who owns the indexed knowledge, and what drives ongoing maintenance and operating cost?
Your organization retains full ownership of the source documents, vector or search indexes, ingestion pipelines, and application code. Ongoing operating cost is driven primarily by indexing frequency, storage volume, and query volume—which we optimize through incremental indexing and scoped retrieval.
What happens next
A short review.
A clear next step.
We read the request, ask the questions that change the plan and suggest the smallest useful next step.
- Planned US-overlap hours
- Mutual NDA on request
- Direct contact with the delivery team