Turn scattered content into answers people can trust.
Most enterprise knowledge is trapped across documents, collaboration tools, tickets, data stores, and email. OvatioIQ Knowledge brings that material into a governed retrieval layer, keeps provenance visible, and gives teams answers with citations, evidence traces, and access controls intact.
What this gives you that generic chat over documents does not
Knowledge is built for work where "the answer sounds right" is not enough. It preserves provenance, applies role-aware retrieval controls, weighs source authority, and exposes answers through both a console and an MCP server so other AI systems can call it as a governed truth layer.
| What teams need | What Knowledge provides |
|---|---|
| Find the right answer faster | Search and question-answering across indexed content, graph relationships, document types, and source metadata. |
| Show where the answer came from | Source citations, evidence traces, proof objects, and source-authority scoring so reviewers can inspect the chain. |
| Respect access boundaries | Tenant-aware ACL/RBAC evaluation and redaction controls carried into retrieval. |
| Fit different deployment requirements | Provider flexibility for hosted or self-hosted model paths, plus local air-gap composition with Ollama, Qdrant, and SQLite. |
Validation pipeline
The validation pipeline is there for the buyer who has to defend an answer later. It checks whether the response is grounded, cited, timely, and allowed for the requesting principal. The recorded test catalog shows 1,213 tests passing across ingestion, retrieval, validation, security, MCP, and API surfaces.
A principal asks across approved enterprise content and source metadata.
Apply tenant, ACL/RBAC, and redaction controls.
Compare the answer to retrieved source material.
Attach evidence traces and proof objects.
Check timeliness, source quality, and allowed use.
The answer includes citations, evidence traces, and access controls that reviewers can inspect.
Source authority
Not every document deserves equal weight. Knowledge can prefer authoritative systems of record, separate stale or lower-confidence material, and keep the citation chain visible to the reviewer. That matters when teams are reconciling policies, contracts, procedures, client records, or technical documentation.
Policies, tickets, files, messages, records, and drafts can all mention the same topic.
High authority, reviewed, recent.
Trusted operational evidence.
Useful context, lower authority.
Low confidence or excluded.
The final answer favors trusted sources while keeping the citation chain visible.
Ingestion that matches how knowledge actually lives
Knowledge includes 21 concrete source connectors across file, collaboration, repository, storage, database, web, media, and enterprise content systems. That breadth lets an evaluation start with real content instead of a hand-built demo folder.
- Document and file sources: PDF, CSV, HTML, filesystem, cloud storage, and S3.
- Collaboration and work sources: Slack, Teams, email, mailbox, chat, Jira, Confluence, GitHub, SharePoint, and Google Drive.
- Structured and specialized sources: SQL, SEC EDGAR, AlphaSense, media, and video.
Built for regulated environments
- ACL and RBAC enforcement during retrieval.
- Built-in redaction masks sensitive fields during ingestion.
- Multiple model-provider options: OpenAI, Anthropic, Ollama, vLLM, and a stub provider for repeatable tests.
- Air-gap deployment with local Ollama inference, Qdrant, and SQLite.
- Production deployment with PostgreSQL and Qdrant.
Extensible by design
The OvatioIQ Plugin SDK gives client, partner, and third-party developers a supported path to extend Knowledge without turning each new source or rule into custom core code. Developers can add proprietary source connectors, post-ingestion enrichers, retrieval validation passes, workflow integrations, MCP-callable tools, reusable skills, and model-provider adapters.
| Extension type | What it lets teams create | Why a buyer cares |
|---|---|---|
| Connectors | Ingestion from proprietary systems, internal APIs, databases, file stores, or industry-specific platforms. | More of the organization's trusted knowledge becomes searchable and citable. |
| Enrichers | Post-ingestion metadata, entity extraction, classification, or domain scoring. | Content arrives with the tags and signals reviewers actually use. |
| Validation passes | Custom retrieval filters, ranking rules, clearance checks, or citation requirements. | Answers can reflect local governance rules instead of generic search behavior. |
| Workflow integrations | Outbound and inbound automations for N8N, Zapier, Google Cloud, AWS EventBridge, OpenAI Actions, and MCP. | Verified knowledge events can trigger downstream work in existing tools. |
| Tools and skills | MCP-callable tools and reusable chatbot skills that bring Knowledge into guided Agent workflows. | Users can ask, investigate, and act from the same governed context. |
| Model adapters | Provider paths for hosted, self-hosted, or organization-specific inference backends. | Deployment can match security, cost, and residency requirements. |
Those extensions inherit framework-level protections such as encrypted secret configuration, tenant-scoped storage, HMAC-verified inbound events, restricted outbound calls, and plugin lifecycle auditing.