Semantic Knowledge Search
Semantic search across your connected docs, wikis, and tickets: ask in plain language and get ranked, cited results from your own pgvector index.
The problem
Keyword search fails the moment the asker uses different words than the author. Knowledge is fragmented across a wiki, a docs tool, and a ticket history, and each has its own separate search box. People give up and ask a person instead, which does not scale.
How it runs on Neblex
Neblex indexes content from Confluence, Notion, and Zendesk into a single semantic index in your own Postgres/pgvector database. Connectors pull the content, poll-based change detection keeps the index current as pages change, and embeddings can run on self-hosted models.
Ask in plain language from embeddable web chat, Slack, or Microsoft Teams and get ranked results with citations pointing back to the exact source page or ticket. Because the search is semantic, a question phrased nothing like the source document still finds it.
The index lives in your own database, not a third-party service. Run it on managed cloud workers, or keep indexing and search inside your network with On-Prem Workers connecting over an outbound-only connection.
Step by step
Connect your sources
Point connectors at Confluence, Notion, and Zendesk.
Build the index
Content is embedded into your own Postgres/pgvector database, with embeddings optionally on self-hosted models.
Keep it fresh
Poll-based change detection re-indexes content as it changes, on a schedule you set.
Search in plain language
Ask from Slack, Teams, or web chat and get ranked, cited results.
Follow the citations
Each result links back to the source page or ticket for verification.
Platform capabilities used
- Semantic search over Postgres/pgvector
- Confluence, Notion, and Zendesk connectors
- Poll-based change detection for re-indexing
- Slack, Teams, and web chat channels
- Self-hosted embedding models
- On-Prem Worker deployment
Common questions
How is this different from the search built into each tool?
It searches all connected sources at once and matches meaning rather than keywords, so results surface even when the question uses different words than the document. Every result carries a citation back to its source.
Where is the index stored?
In your own Postgres database using pgvector. Embeddings can run on self-hosted models, and On-Prem Workers keep indexing and search inside your network over an outbound-only connection.
Who can search, and is usage tracked?
Access to the platform is governed by role-based access, SSO, SCIM, and 2FA, and you control which sources are indexed. Activity is logged to a tamper-evident hash-chained audit trail.
Related use cases
Knowledge Agent
An agent answers employee and customer questions from your docs, wikis, and past tickets, citing sources and flagging gaps in the documentation.
AgenticSupport Triage Agent
A triage agent reads each Zendesk ticket, judges urgency and sentiment, drafts a reply, and escalates anything it is unsure about to a human for review.
AgenticCustomer Feedback Synthesis Agent
An agent gathers feedback from Zendesk tickets, app store reviews, and G2 reviews, finds recurring themes, and sends a cited digest to leadership.
Want this running on your stack?
Neblex Integration Fabric is in beta: full platform, free while in beta. Bring this workflow and we will map it to your systems.