Find Information By Meaning, Not Just Keywords
Traditional search only works when users know the exact words used in a document. In reality, different teams often describe the same information in different ways.
Semantic search fixes this at the architecture level. It combines vector search (for meaning), keyword search (for precision), and an entity graph (for relationships), across every connected system. Ask the question however feels natural, and the right document surfaces, no matter which term your organisation happened to use.
Talk to a solutions engineer · See DocuTalk · Read the AI copilot
What "Hybrid" Actually Means
Vector search alone often returns close matches that aren't precise enough for regulated work. Keyword search alone returns nothing if you don't know the exact phrasing used. Combining both, plus an entity graph, solves this.
Retrieval layer | What it does | When it's the right fit |
Vector similarity | Finds documents that match the meaning of the query | "What's our position on third-party AI vendors?" → returns the right policy, even if its title doesn't mention "AI" |
Keyword (BM25) | Finds documents with the exact terms used | "Section 7.5 of our procurement standard" → returns that exact document |
Entity graph | Finds documents connected to specific people, contracts, accounts, or products | "Everything related to vendor X over the last two years" → returns everything linked to that vendor |
Permissions filter | Every result is limited to what the user is allowed to see | Answers the CISO's question — "could the AI return something it shouldn't?" — with "no," by design |
The combination gives users results they can actually act on, while the audit chain underneath, shared with the rest of the platform, gives auditors results they can defend.
Where The Search Reaches
The federation surface from Intelligent Repository is what the search reads. The reach is what most enterprise search platforms get wrong.
Source | Coverage |
M365 / SharePoint | Native connector, with permissions enforced at retrieval |
Box / Drive | Native connector, same enforcement |
Legacy ECM (OpenText, Hyland, Documentum) | Connectors with full text and metadata |
LOB systems (CRM, ERP, EHR, PLM) | Dedicated connectors per system |
Vertical platforms | Industry-specific connectors (Veeva, Procore, Bentley, etc.) |
Email and chat archives | Native support, aware of retention windows |
Custom sources | REST and webhook support |
The result: one search bar, the federated estate underneath, no copying.
What The Audit Chain Captures
Every search is logged. That means the CISO's question, "What was searched, by whom, and what came back?" always has an answer.
Event | What's logged |
Search query | The query text, the user, and the timestamp |
Retrieved candidates | Document IDs, version IDs, and which system each came from |
Permission filtering | Which results were excluded, and why |
Result interaction | Which results the user actually opened |
Citation use | When a search result was cited somewhere else downstream |
This is what makes search defensible in environments where the audit trail matters.
What Changes For The Workforce
The productivity gain is measurable and consistent across deployments.
Metric | Typical year-one outcome |
Time per knowledge-work query | 4 documents opened, on average → 1 query, 1 answer |
Search failure rate (searches that return nothing, when they should have) | Reduced by 30–50% |
Cross-source coverage (queries that need to reach legacy systems) | Near-complete |
Manual re-keying (copying info between systems by hand) | Cut by 30–60% |
Time to onboard a new hire to the corpus | Weeks → days |
How TeamSync Compares
Customers typically evaluate this against:
Glean: Strong enterprise search, but weaker on regulated-content architecture and cryptographic audit
Microsoft 365 Search + Copilot: Strong within M365, but only partial coverage across other sources
Coveo: Strong for commerce search, but weaker on regulated-content audit
In-house Elasticsearch / OpenSearch: Most flexible, but permissions enforcement, cross-source connectors, and audit logging all need to be built yourself
For specific comparisons:
Read Further
DocuTalk capability — the conversational AI built on the same retrieval
Intelligent Repository — the platform the search reads from
Why TeamSync — permissions-aware AI — the architectural foundation
Knowledge-worker time recovery — the productivity story