Enterprise Knowledge Base Search with AI

Document metadata (creation date, last modified date, owner, version) feeds into retrieval ranking, so more recent documents surface first for topics where recency matters. The agent is prompted to prefer recent sources explicitly for time-sensitive questions (e.g. current policies, benefit enrollment windows). When multiple sources genuinely contradict, the agent surfaces the contradiction with both positions cited rather than picking one and hiding the other. For organizations with significant documentation debt, we offer a content audit as part of scoping: we identify the most problematic overlap areas (policies updated in 3 places, procedures in 5 different documents) before indexing, and route cleanup to content owners. The agent quality is bounded by content quality.

Yes. We integrate with SharePoint Online and OneDrive via Microsoft Graph API. Permissions are resolved at query time against the user's current Azure AD group memberships and each document's effective ACL. A user who cannot read a document in SharePoint (because they're not in the required group, or because item-level permissions exclude them) will not receive answers sourced from that document. This is enforced in two places: the retrieval filter and the synthesis prompt, so there's defense in depth. Permission enforcement at query time is a non-negotiable design requirement, not an optional feature. We do not cache permissions in a way that could grant stale access.

Three mechanisms. First, every answer includes inline citations to the specific source passages, so users can verify the answer against the source without leaving the interface. Second, a thumbs-up/thumbs-down and a 'report issue' button capture user feedback, which routes to the knowledge owner and to the content team. Third, a quality dashboard tracks thumbs-down rate by topic, re-query patterns, and specific source documents that produce disagreement. When a pattern emerges (e.g. the agent consistently cites an outdated policy), we update the source and the index within hours. Users are trained during rollout to verify cited sources for decisions that matter, which is a healthy habit regardless of whether the answer is AI-generated.

There's no practical upper bound for enterprise use cases. Our deployments range from 50K documents to over 2M documents, in both cases with query response times under 3 seconds. Pinecone handles horizontal scaling; the embedding-index size is the cost driver. At 2M documents with ~5 chunks per document the vector index runs roughly $800-1,200/month in infrastructure. During scoping we assess your document volume, growth rate, and query volume and right-size the infrastructure. The architecture is read-heavy and scales linearly, so adding a new source (e.g. a 500K document archive) is a planned indexing job rather than an architectural change.

The agent is explicit about the limits of its knowledge base. When a question has no retrievable match above the relevance threshold, the agent says 'I don't have enough information to answer confidently' and offers to route the question to a knowledge owner or to log it as a gap. For ambiguous questions (multiple interpretations), the agent asks a clarifying follow-up rather than guessing. For questions that fall outside the knowledge base's scope (e.g. personal HR data the agent doesn't have access to), the agent explicitly redirects to the correct system or person. The agent never fabricates an answer. Hallucination is prevented both architecturally (synthesis is constrained to retrieved content) and prompt-level (explicit instructions and grounding checks).

Wrong answers in a RAG system usually trace to one of three root causes. First, the source document is itself wrong or outdated. We fix it at the source and the agent follows. Second, retrieval missed the right document. We tune the embedding model, chunking strategy, or add the document to a higher-priority bucket. Third, synthesis misinterpreted retrieved content. We adjust the synthesis prompt or add grounding constraints. Every user-reported error triggers a small investigation with the output reaching the content or retrieval team. Across deployments, reported error rate trends from 4-6% in the first month to under 1% by month 3 as systemic issues are identified and fixed.

Every query writes to a log: user ID, timestamp, original question, rewritten query, retrieved documents with relevance scores, final answer, citations, and any user feedback. Logs export to your SIEM (Splunk, Elastic, Datadog) or a dedicated analytics database. For regulated environments we add prompt and response logging with configurable retention, PII redaction rules, and access controls on the log itself (not everyone who can query can read query logs). A governance dashboard shows query volume, top topics, coverage gaps, access violations if any, and source attribution frequency. Several financial services clients use the log for FINRA-relevant questions to prove what knowledge was available and when.

A focused deployment indexing 2-3 primary sources (e.g. SharePoint, Confluence, ServiceNow KB) with 50K-200K documents runs 6-8 weeks. Weeks 1-2 are discovery: source access, permission architecture, user interface choice. Weeks 3-4 build the indexing pipeline and run the first full sync. Weeks 5-6 deploy the query interface (Teams bot, web chat, or embedded widget) and run a beta with 30-50 pilot users. Weeks 7-8 incorporate pilot feedback, tune retrieval and synthesis, and roll out broadly. Adding additional sources (a fourth, fifth, sixth) takes 1-2 weeks per source. Most of the complexity is permissions and access control, not the AI. If your access architecture is clean, rollout is faster.