Enterprise Knowledge Base Solution
Clean up the source material first, then let AI answer accurately
01Overview
Knowledge base projects usually fail on the content, not the technology: the same spec written three ways, outdated policies never retired, tables trapped in scanned PDFs. Loading that into a vector store just produces wrong answers faster and with more confidence.
Our solution makes inventory and clean-up step one: map sources, deduplicate, confirm the valid version and add metadata before chunking and embedding. Retrieval uses RAG with cited sources, and role-based permissions ensure staff only retrieve what they are allowed to see.
Before launch we build a test set from real questions supplied by your teams; after launch we set up update and expiry routines so the knowledge base stays maintained.
02The problems
Scattered, conflicting versions
Files sit across drives, chat groups, email and laptops, with no clear latest version.
Hard formats
Scanned PDFs, table images and slides lose structure when parsed naively.
Permission leaks
Finance, HR and contract material mixed in with general content can surface to the wrong people.
No upkeep after launch
Content goes stale, the AI keeps citing it, and staff stop trusting the tool.
03Architecture
Inventory, owners, deduplication, version confirmation and metadata such as scope, expiry and sensitivity.
Parsing, OCR and table recovery, semantic chunking, embeddings and keyword indexes with source positions.
Hybrid retrieval with reranking, then grounded answers with citations.
Filtering by department, role and sensitivity at retrieval time, synced with your org chart.
Web, WeCom or DingTalk access, query logs, feedback, regression tests and expiry reminders.
04Capabilities
Inventory and clean-up
Mark valid, outdated, duplicate and unclear material with each department; only confirmed content goes in.
Complex document parsing
Word, PDF, Excel, slides and scans, with tables and numbered clauses restored.
Hybrid retrieval
Semantic plus keyword search, handling both part numbers and casual questions.
Cited answers
Every answer links to source passages; if nothing supports it, the system says so.
Tiered permissions
Scope by department, role and sensitivity, synced from WeCom, DingTalk or LDAP.
Test sets
Real questions and expected answers checked before launch and after every change.
Feedback loop
Staff flag bad answers, which route to the content owner for correction.
Ongoing maintenance
Expiry reminders, intake workflow for new content and reports on unanswered questions.
05Typical scenarios
Typical situations — not specific client cases.
Support knowledge base
For example, a manufacturer whose support staff need install guides, fault codes and spare parts by model.
Policy and process Q&A
For example, a larger company where HR fields the same questions daily.
Sales and bid library
For example, a firm that bids often and needs the latest proposals, certificates and standard clauses.
06Technology
- RAG
- PostgreSQL + pgvector
- Elasticsearch
- PaddleOCR
- bge 向量模型
- DeepSeek
- 通义千问
- 企业微信 API
07FAQ
Can we skip the clean-up?
We advise against it: conflicting sources produce conflicting answers and erode trust. Clean-up can be done department by department.
Will our documents train public models?
Documents stay in your database and only relevant snippets are sent per query, using APIs that exclude training; private deployment is an option.
How is quality measured?
Against a test set of real questions: correctness, correct citations and honest 'not found' answers.
Related development services
Industries
Tell us about your project
Leave your details and we’ll reply within one business day. We start with your needs, then a proposal; pricing is discussed directly.
- Reply within one business day
- Free initial conversation, no hard sell
- Handover with admin and docs