Index with entitlements
SharePoint, Confluence, tickets, and files are ingested with the same access rules as source systems.
The problem
When knowledge trapped in files nobody can find becomes the norm
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Knowledge trapped in files nobody can find
Years of documents, decks and emails contain critical institutional knowledge. None of it is searchable or accessible at the moment it's needed.
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Every team rebuilds the same information
Without a shared knowledge layer, teams duplicate research, produce conflicting answers and make decisions on outdated information.
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Ungoverned AI retrieval creates risk
Off-the-shelf AI tools connected to company data with no access controls, no audit trail and no accuracy layer are a compliance liability.
运作方式
Find, understand, and act on institutional knowledge—with permissions intact
Answer with citations
Responses quote approved passages; conflicting versions surface for knowledge owners to reconcile.
Close the loop
Gaps and stale pages feed content backlogs so the corpus improves—not rots—in production.
Hybrid search blends lexical and semantic retrieval for your messy real-world corpus.
您的组织所知道的比它能找到的更多。
包含内容
What you get when you run this with Thinkia
A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.
Multi-source ingestion
connects to SharePoint, Confluence, Google Drive, Notion, internal databases and custom repositories
Access-controlled retrieval
users only surface information they are authorised to see — enforced at query time, not at ingestion
Source-grounded answers
every AI response is linked to its source document, with confidence indicators
Knowledge gap detection
identifies queries that return low-confidence answers, surfacing where documentation needs updating
Audit and compliance layer
full log of who queried what, when, and what the system returned
EU AI Act alignment
human oversight, explainability and data governance built into the architecture
技术提供 Thinkia Synapse
成果
What changes when this runs in production
–65%
Reduction in average time spent searching for answers across systems
–40%
Reduction in teams rebuilding information that already exists
90%+
Source-grounded responses vs. hallucinated answers from ungoverned tools
Results vary by knowledge base size, document quality and access control complexity.
合作方式
From search boxes to answers grounded in how your company works
Corpus audit
Corpus audit
Week 1–2
Repositories, permissions, and stale content policies are mapped before anything is indexed.
RAG & safety
RAG & safety
Week 3–5
Retrieval, citations, and PII handling are tuned; red teams probe leakage and drift.
Function pilot
Function pilot
Week 6–9
Support, sales, or engineering uses the assistant; deflection and time saved are measured.
Enterprise scale
Enterprise scale
Week 10+
More sources, languages, and apps (Slack, Teams, ServiceNow) with central admin and analytics.
Permission models and content sprawl dominate effort; expansion follows trust in citations.
延伸阅读
Enterprise knowledge, search, and RAG
开始
Ready to scope this for your context?
We start with a focused session—no commitment—to map constraints and a sensible path.