Plexara
Why Plexara
The Hard Problems with MCP, Solved
AI Transparency Through MCP
What it does
MCP is a standard interface between AI models and your data tools. Every tool call is observable, typed, and logged. You bring your own AI model. Plexara governs the data layer, not the intelligence layer.
How it works
Every tool call is logged before execution with a typed schema describing inputs and outputs. Results come from live data systems and are returned with full provenance. Nothing is hidden, summarized, or transformed outside the audit trail.
Why it matters
When any AI fabricates a number, the damage depends on whether you can trace it. Black-box systems make fabrication hard to spot and impossible to diagnose. Showing the math matters, but showing where the numbers came from is what counts. MCP provides that provenance on every interaction.
Contextual Enrichment
What it does
Query any data source and receive business context alongside your results. Every response includes ownership, quality scores, PII warnings, deprecation notices, and glossary definitions.
How it works
When a query executes, Plexara intercepts the result set and cross-references each entity against the metadata catalog. Relevant context is embedded in the response before it reaches the AI agent. No additional queries required.
Why it matters
AI stops treating data as anonymous rows and columns. It knows whether a field is deprecated, who owns the data, what the quality score is, and what the business definition means. Automatically, on every query.
Knowledge Capture, Synthesis & Application
What it does
Domain knowledge shared during AI conversations (column meanings, business rules, data quality observations) is captured, reviewed, and written back to your metadata catalog.
How it works
Insights are detected during sessions, then routed through a governance workflow for review. Approved insights become catalog updates applied to the appropriate entities. The process is: capture, review, synthesize, apply.
Why it matters
Each AI conversation improves the metadata catalog, which improves the next conversation. Tribal knowledge stops walking out the door.
Lineage-Aware Metadata Inheritance
What it does
Downstream datasets automatically inherit documentation, quality indicators, and business context from their upstream sources.
How it works
Plexara tracks data lineage at both the dataset and column level. When upstream entities are documented or updated, those annotations propagate downstream through the lineage graph. No manual documentation required.
Why it matters
Document a source table once. Every derived dataset picks up the context automatically.
Persona-Based Access Control
What it does
Define who can access which capabilities based on roles mapped from your identity provider.
How it works
Personas map identity provider roles to platform capabilities. Analysts get analytics tools. Executives get high-level exploration. ETL services get pipeline access. Machine-to-machine workflows get governed API endpoints. All controlled through your existing identity infrastructure.
Why it matters
Security is the default operating mode, not an afterthought. Access is defined by business roles, not technical permissions.
Enterprise Security
What it does
Fail-closed authentication with OIDC, API keys, and a built-in OAuth 2.1 server. Every request is verified against your identity provider before any tool executes.
How it works
OIDC tokens are validated against your identity provider with JWKS auto-discovery. API keys authenticate service accounts. The built-in OAuth 2.1 server bridges MCP clients like Claude Desktop to your upstream IdP with PKCE, rotating refresh tokens, and bcrypt-hashed secrets.
Why it matters
AI touching production data without enterprise authentication is a breach waiting to happen. Plexara is fail-closed: no anonymous mode, no bypass, no permissive fallback.
Audit & Administration
What it does
Every tool call, query, and error is captured in a structured audit system with an interactive administration portal for real-time metrics, searchable events, and drill-down detail.
How it works
Audit middleware captures every authorized tool call asynchronously with indexed fields for user, tool, timestamp, and status. The admin portal surfaces this as real-time dashboards, searchable event logs with detail drawers, and a tool explorer with dynamic parameter forms.
Why it matters
An audit log you cannot query is not an audit log. Plexara gives you P50/P95/P99 latency, success rates, top tools, top users, and full parameter detail on every interaction, not grep over log files.
Federated SQL Queries
What it does
Query across PostgreSQL, MySQL, Elasticsearch, Cassandra, BigQuery, MongoDB, Hive, and other sources through a single SQL interface.
How it works
A federated query engine routes SQL across data sources. AI agents write standard SQL without knowing where data physically lives. The federation layer handles routing, optimization, and result assembly.
Why it matters
Your data estate becomes a single queryable surface. No more teaching AI about connection strings, database-specific dialects, or multi-system join logic.
Object Storage Integration
What it does
AI agents discover, read, and write files across any S3-compatible backend (AWS S3, MinIO, SeaweedFS, Ceph) with full metadata enrichment on every operation.
How it works
Nine MCP tools cover the full object lifecycle: list, read, write, copy, delete, and presign. Every read operation is enriched with ownership, quality scores, and PII classification from the metadata catalog. Write access requires explicit opt-in per connection.
Why it matters
Unstructured data stops being a black box. The AI finds files by searching for business concepts, reads them with full context, and stores results back, all under the same governance as every other capability.
Metadata Discovery & Governance
What it does
Search, browse, and govern your data estate through a centralized metadata catalog.
How it works
The metadata catalog indexes all connected data sources: schemas, relationships, lineage, and business context. AI agents discover datasets by searching for business concepts rather than technical table names.
Why it matters
AI finds data by meaning, not by memorizing schema names. Ask about revenue and it finds the right tables across systems, with context about freshness, quality, and ownership.
Common questions
Capabilities FAQ
Plexara ships a governed semantic catalog built on DataHub as a first-class component. If you already run DataHub, Plexara reads from your existing instance. If you do not, the platform deploys one. Other catalogs that expose an MCP server can integrate today through Plexara's MCP gateway, and native support for additional metadata providers is on the roadmap. Every conversation captures new business context as catalog metadata, so the catalog improves with use.
Context rot is when an agent's working memory fills with stale or irrelevant content mid-session and answer quality degrades. Plexara curates what enters the context window through enriched tool responses, persona-scoped tool visibility, and explicit prompts, so the working set stays small and high-signal.
Plexara ships a session-coupled memory and a separate insights pipeline. Within a session, memory accumulates relevant context. Between sessions, multi-strategy recall (entity lookup, semantic search, lineage graph traversal) surfaces prior context on demand. Insights, once admin-reviewed, become organization-wide catalog documentation.
Memory is scoped by persona and user so collaboration agents can share context within a team without leaking across teams. Promoted insights become organization-wide catalog metadata that all future agents benefit from automatically.
Personas extend RBAC by also restricting which tool descriptions an agent can SEE, not just which tools it can call. Fewer visible tools means fewer tokens spent on tool descriptions in the agent context window, which improves accuracy and reduces cost. Default-deny applies to anything not explicitly mapped.
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Use Cases
See how Plexara serves business leaders, data teams, and AI integration scenarios across industries.