Enterprise Knowledge Base
An AI Gateway-powered enterprise knowledge platform that turns scattered document repositories into a trusted intelligent knowledge partner — natural-language Q&A with citation-grounded answers, real-time incremental sync, controlled permissions, and full-chain auditability.
The more documents, the harder to find answers; the more scattered knowledge, the harder to build trust
Enterprise documents grow exponentially yet live scattered across shared drives, Wiki, OA, and IM — a systemic gap between "storing documents" and "finding, trusting, and using knowledge"
document storage systems
Massive, messy documents trapped in silos
Documents live across drives, Wiki, and IM with mixed formats and versions — no unified entry
avg. lookup time
Retrieval relies on "asking people" and is slow
Finding docs means folder digging and asking colleagues — often hours
docs not updated in a year+
Stale documents, inconsistent content
Copies diverge after revision; outdated info keeps getting cited and misleads decisions
employees distrust AI answers
Generic LLMs: hallucination & overreach risk
No private knowledge (hallucination), no permission control or audit
From "document repository" to "trusted intelligent knowledge partner"
Not just "store and search" documents, but make knowledge answerable, traceable, and trustworthy — employees shift from "finding documents" to "asking knowledge"
A three-layer architecture: from multi-source documents to trusted Q&A
AI Gateway sits between employees and documents — carrying the natural-language Q&A entry upward and unifying heterogeneous data sources downward, completing ingest → parse → index → retrieve → generate → audit end to end.
Employee Interaction Layer
Natural-language Q&A entry across all workplace touchpoints
Enterprise Knowledge Platform
AI Gateway + knowledge engine, hosting all knowledge services
Enterprise Data Sources
Heterogeneous document sources centrally managed, one integration for all
Retrieval-Augmented Generation (RAG) Data Flow
Ingest & Parse
Connectors sync documents, auto-detect formats, and restore layout structure
Chunk & Embed
Clean and denoise, chunk by semantic boundaries with overlap, embed into the vector store
Hybrid Recall & Rerank
Dual-path recall with vector + BM25, reranked to refine evidence
Evidence-Grounded Generation
The LLM generates answers only from retrieved evidence, with citations in output
Real-time Update Loop
Change detection triggers incremental indexing, versioning, and stale rebuilds
RAG is not a naive "retrieve + generate" glue — it is trustworthy engineering
Answer trustworthiness is bounded above by retrieval quality and below by generation constraints — parsing & chunking, hybrid retrieval, reranking, and citation fallback are all indispensable
Parsing & chunking quality caps recall
Layout-aware parsing (tables, headers/footers, TOC) preserves structural semantics; semantic chunking with overlap balances recall granularity and context completeness
Hybrid retrieval covers two query types
Vector Semantic Recall
Vector semantic recall excels at "semantically similar" phrasing while BM25 keyword recall excels at "exact terminology" — the two complement each other to lift recall
Reranking refines the context
Before
After
Rerank models re-rank retrieved passages, trimming irrelevant noise and controlling the quality and Token cost of the context sent to the model
Citation traceability & confidence fallback
5 days of annual leave after one year?
Yes, 5 days of annual leave after one year1, per Employee Handbook §3.22
Answers are forced to carry citations that jump to the source; low-confidence responses decline politely and guide users to refine or escalate — never "faking it"
RAG engineering = solid parsing & chunking + complete hybrid recall + precise reranking + trustworthy citation fallback — all four together decide answer credibility.
Five core capabilities building a fresh, trustworthy, controlled enterprise knowledge foundation
From multi-source ingestion, intelligent parsing, and hybrid retrieval to RAG generation and real-time governance — one-stop across ingest → index → Q&A → governance
Unified ingestion of heterogeneous document sources to end silos
Shared drives, Wiki, OA, and IM connect through adapters with auto-detected formats (PDF / Word / Markdown / tables) and scheduled incremental sync with permission binding.
- Unified connector ingestion
- Multi-format auto-detection
- Scheduled incremental sync
Covering onboarding, R&D, cross-team collaboration, and frontline enablement
Onboarding & Policy Q&A
Natural-language Q&A over onboarding guides and HR/admin policies, with cited answers for instant ramp-up.
R&D & Project Knowledge
Technical designs, API docs, and retrospectives consolidate into a team knowledge base — searchable, reusable experience.
Cross-Team Policy Publishing
Policies and announcements published and updated centrally — consistent across departments, versions traceable.
Sales & Customer Success Enablement
Product manuals, case libraries, and playbooks sync to frontline teams in real time for faster response.
Need a trusted intelligent knowledge partner for your enterprise?
From multi-source ingestion and the RAG engine to real-time updates and permission governance, we provide dedicated deployment and implementation guidance