Keyword search returns documents. Semantic search returns answers — when the asker phrased the question in words your firm doesn't use.
The standard search box has been a productivity tax for a decade. The user knows what they're looking for; they just don't know the exact words the firm used to file it. The senior associate calls it a "policy"; the records team filed it as a "standard operating procedure"; the AI vendor calls it a "guideline." 3 terms, one document, one query that returns nothing useful.
Semantic search is the architectural fix. Hybrid retrieval — vector similarity for meaning, keyword for precision, entity graph for relationships — across the federated estate. The user phrases the question naturally and the right document surfaces, regardless of which dialect the firm used to file it.
Talk to a solutions engineer · See DocuTalk · Read the AI copilot
What "hybrid" actually means.
Pure vector search returns near-misses that aren't precise enough for regulated work. Pure keyword search returns nothing when the asker doesn't know the firm's exact phrasing. Hybrid is the architectural answer that delivers both.
| Retrieval layer | What it does | When it's the right answer |
|---|---|---|
| Vector similarity | Documents whose meaning matches the query | "What's our position on third-party AI vendors?" → returns the policy regardless of whether the title contains "AI" |
| Keyword (BM25) | Documents containing the exact terms | "Section 7.5 of our procurement standard" → returns the precise document |
| Entity graph | Documents connected to the query's entities (people, contracts, accounts, products) | "Everything related to vendor X across the last 2 years" → returns documents linked to the entity |
| Permissions filter | Every result bounded by the asking user's permissions | The CISO's question — "did the AI return what it shouldn't?" — has the answer "no" by architecture |
The hybrid blend produces results the user can actually use. The architectural layer underneath — same platform, same audit chain, same permission model as everything else on TeamSync — produces results the auditor 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; permissions enforced at retrieval |
| Box / Drive | Native connector; same enforcement |
| Legacy ECM (OpenText, Hyland, Documentum) | Connectors with full text + metadata |
| LOB systems (CRM, ERP, EHR, PLM) | Per-system connectors |
| Vertical platforms | Industry-specific connectors (Veeva, Procore, Bentley, etc.) |
| Email and chat archives | Native, with retention-window awareness |
| Custom sources | REST + webhook support |
The result: one search bar, the federated estate underneath, no copying.
What the audit chain captures.
Every search interaction writes to the chain. The CISO's question — "what was searched, by whom, and what was returned?" — has a chain-segment answer.
| Event | What's anchored |
|---|---|
| Search query | Query text, user, timestamp |
| Retrieved candidates | Document IDs, version IDs, source-system attribution |
| Permission filtering | Which candidates were excluded and why |
| Result interaction | Which results the user opened |
| Citation use | When a search result was cited in a downstream artifact |
This is what makes the search defensible in environments where the audit pack 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 (zero results that should have returned a document) | 30–50% reduction |
| Cross-source coverage (queries that should reach into legacy systems) | Near-complete |
| Re-keying (manually copying information from one system to another) | Cuts 30–60% |
| Time-to-onboard a new hire to the corpus | Weeks → days |
How customers compare TeamSync for search.
The semantic-search evaluation usually compares against:
- Glean — strong on enterprise search; the regulated-content platform and the cryptographic audit are weaker
- Microsoft 365 Search + Copilot — strong inside M365; cross-source coverage is partial
- Coveo — strong on commerce search; the regulated-content audit story is weaker
- In-house Elasticsearch / OpenSearch — most flexible; the permissions enforcement, the cross-source connectors, the audit anchoring need to be built
For specific comparisons: - TeamSync vs Glean - TeamSync vs SharePoint + M365
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