GSAI · 2026 · 06 · 0024
AI Solution BriefTurn scattered enterprise knowledge into a queryable, trusted, citable private AI expert
A RAG-based platform that unifies Confluence, SharePoint, Notion, shared drives, and business databases — on-prem deployment, full citation chains, and inherited ACLs. Let your team query the company the way they query a senior expert.
Enterprise knowledge management is moving from document piles to expert systems
For two decades enterprises have piled knowledge into Wikis, SharePoint, and shared drives. Searchable ≠ usable. People still ping senior coworkers, ask in group chats — 90% of internal knowledge sits idle. RAG combines retrieval with LLM reasoning to give enterprise knowledge its first real Q&A interface.
Four old problems generic LLMs can't solve
Generic LLMs (ChatGPT / Qwen / ERNIE) have zero knowledge of your private data — and they don't admit it. In legal, compliance, or technical decisions, this 'confident hallucination' is fatal.
Knowledge scattered across a dozen systems
A single workflow's docs may span Confluence (specs) + SharePoint (templates) + group files (changes) + senior memory (tacit). Point search misses; cross-system search fails. New hires burn three months stepping on the same rakes.
Senior staff are the search engine; attrition = knowledge loss
How the workflow really runs, why this client is special, how that bug got fixed — it's all in 1-2 people's heads. When they leave, they take not just experience but the company's Q&A capability.
Generic LLMs hallucinate with confidence
They know nothing about your private knowledge but answer as if they do. In legal, compliance, or decision contexts, one wrong answer that sounds right is enough to cause real damage.
On-premise vs. AI is a compliance dead zone
Customer data, contracts, IP, HR records can't leave the company. Yet teams genuinely need AI. The two demands looked irreconcilable. RAG is the third path.
One stack, five immediately landable scenarios
RAG isn't a chatbot. It's the knowledge layer of the enterprise. Below are five landing points the same stack covers.
Onboarding assistant
Onboarding docs + policies + workflow charts feed RAG. New hires 'ask the company' instead of pinging seniors.
IT / process self-service
Reimbursement, VPN, hardware requests — daily process questions stop chewing IT time.
Sales knowledge copilot
Product handbook + standard responses + competitor compare + case library on tap during live calls.
Tier-1 customer support
RAG drafts replies from the ticket history; agents either approve or send directly.
Legal / compliance lookup
Templates + precedents + regulations. Ask 'is this clause OK' and get a cited answer.
Three core capabilities, broken down with real UI
From employee 'asks' to system 'answers', from answer to source, from permission to audit — we break down RAG's three highest-value paths so every step is visible, verifiable, and auditable.
Standard accounts get 3–5% [1]; strategic accounts up to 8% [2]. See rebate matrix v2026.Q3 [3].
Cross-source Q&A with 100% sourced answers
Every fact opens to the original passage
Users ask in natural language; the system runs hybrid search over Confluence / SharePoint / Notion / drives. The LLM is forced to cite every fact with [n] markers, hoverable to the original passage. Low-confidence queries return 'not found' instead of fabricating.
- Hybrid search across Confluence + SharePoint + Notion + drives + DBs
- 100% enforced citation — every fact carries an [n] chip
- Hover the citation to reveal the source passage + path + last modified
- Answer confidence score (retrieval quality + LLM self-rating)
- Feedback loop (👍 👎) feeds continual weekly iteration
Hybrid retrieval with cross-encoder rerank
A visible recall pipeline, not a black box
A single query fires BM25 full-text + vector semantic in parallel; candidates merge and dedupe, then a cross-encoder reranks. The diagnostic panel visualizes hit scores, similarity scores, and filters at every step — any recall miss is traceable to a specific stage.
- BM25 keyword + BGE-M3 vector search in parallel
- Cross-encoder rerank lifts Top-K precision by +24pp
- Multilingual embeddings align EN/ZH terminology — cross-lingual Q&A
- Metadata filters (department / time / permission / doc type)
- Recall diagnostics panel — every query is replayable
ACL inheritance to the field, full audit trail
Data stays in; every question is logged
RAG inherits ACLs live from source systems (Confluence Space / SharePoint Site / Notion Workspace) — what a user sees in RAG strictly equals what they see in the source. Out-of-scope access is auto-blocked + alerted. All Q&A is logged and traceable to user / question / cited docs / model version.
- Live ACL inheritance from Confluence / SharePoint / Notion
- Department / role / field-level granular permissions
- Full Q&A audit traceable to user / docs / model version
- Out-of-scope access auto-blocked + real-time alerts
- Compliance export (PRC Class III / GDPR / ISO 27001)
An observable, intervenable RAG pipeline + on-prem architecture
RAG must be a pipeline, not a black box. Each step has clear inputs, outputs, and degradation strategy. Below are the 5 layers plus the 5-stage processing pipeline.
5-stage pipeline
From document ingestion to answer return, every step is observable, intervenable, and replayable. Quality drops are traceable to a specific stage.
Native connectors pull from Confluence / SharePoint / Notion / shared drives / business DBs. Full bootstrap + incremental sync + webhook push keep document lifecycle aligned with source systems.
PDF / Word / PPT / Excel / scanned docs normalized to structured objects. Chunks split by sections / paragraphs / tables — not fixed character windows — to preserve context.
Multilingual embeddings (BGE-M3 / text-embedding-3) cover EN/ZH and domain-specific terms. Chunks + metadata land in the vector DB; BM25 inverted index is built in parallel for hybrid search.
Queries hit vector + BM25 in parallel; candidates merged and re-ranked by cross-encoder. Metadata filters (department / time / permission) and query rewriting are applied.
LLM generates over the retrieved chunks with citations enforced per fact. Self-RAG validates the answer is supported by the retrieval. When uncertain, return 'not found' instead of fabricating.
5-layer architecture
五层各司其职、可独立替换演进。任何一层都可以从云服务切到自部署,从 SaaS 模型切到自训练模型。
Three representative scenarios, anonymized
Each scenario is an abstracted, anonymized representation to help you judge what RAG can land in your organization.
Mid-size law firm · compliance + precedent
120K historical contracts + regulations + precedents in scope. Lawyers ask 'is this clause supported by precedent in X industry' and get specific case IDs + key citations + risk rating. Review time drops from ~4h to 2.2h.
Manufacturing group · process + equipment library
80K SOPs + equipment manuals + fault histories. Engineers scan a QR and ask 'machine reports E-217 — how do I handle it', RAG returns the SOP + similar cases + owner. New-engineer ramp shrinks from 90 to 35 days.
Mid-size SaaS · company-wide IT self-service
IT policies + processes + FAQs + ticket archive ingested. Employees @ the assistant in Lark, ask 'how do I swap my laptop', RAG answers and auto-drafts the OA request. Monthly IT tickets drop from 1,200 to 540.
Scenarios are representative and anonymized; actual project data is delivered separately under partner NDA.
Data stays on-prem; permissions inherit down to the field
RAG becomes the enterprise's neural hub. Its security equals the security of your digital assets. We treat security as a first-class concern, not a post-launch patch.
On-premise deployment
Full on-prem / hybrid cloud / domestic stack (Xinchuang · Kunpeng · Phytium) supported. Processing, vector DB, and model inference all run inside the enterprise network — data never leaves.
Permission inheritance
ACLs from source systems (Confluence / SharePoint / Notion) propagate into RAG. Users can't see anything they couldn't see in the source. Department / role / field-level control.
Audit & traceability
All Q&A logged in full. Every answer is traceable to user / question / cited documents / model version. Citation chains expand to the original source.
End-to-end encryption
TLS 1.3 in transit, AES-256 at rest. Vector DB and document store encrypted independently. KMS integration so keys never leave the enterprise.
Compliance
Classified Protection Level 3 / GDPR / ISO 27001 / financial regulatory frameworks supported. Compliance checklists and pen-test reports delivered with the system.
Model isolation
100% self-hosted LLM option (DeepSeek / Qwen / Llama). Zero third-party API dependency. All retrieval and generation runs inside the enterprise — no outbound calls.
Related solutions
More Wavesteam solutions
AI, capital-markets docs, OCR, vision, IoT and membership operations — composable for your industry.
Capital Markets Doc AI
Bilingual term checks, version diffing, and workflow automation for prospectuses, annual reports, and offering circulars — review cycles cut from weeks to days.
- AI
- Capital Markets
- Automation
Handwritten Order OCR
Turn handwritten and scanned orders into structured ERP records — 96%+ field accuracy, multimodal validation, and direct write-back into your systems.
- AI
- OCR
- ERP Integration
AI Vision for Security
Edge inference and multimodal models for face, behavior, and vehicle recognition — 99.7% accuracy, sub-50ms latency, deployed 24/7 across cities, plants, and campuses.
- AI
- Edge Inference
- Security
If you have a concrete workflow AI hasn't solved yet, let's figure out the right approach together.
We unpack the workflow with you, judge whether AI is worth using and which approach makes the most sense, then come back within 5 business days with a practical initial plan and estimate.