Connect AI to your data. Build knowledge that lasts.
Enterprise Data Integration on MCP for the AI Era
Your AI should know your business as well as your best people do.
Benchmark
Empirical rigor, quantitative and qualitative
In a controlled test, we held the AI model and the data fixed and changed only whether Plexara was in the loop, then graded thousands of repeated runs. In a second study, we started the platform from an empty knowledge layer and watched it learn each fact it was taught. We report the numbers with confidence intervals, and we are just as careful about what they do and do not prove. On questions that turn on a business rule, the difference is large.
Knowledge-trap accuracy
+56 pts95% CI +44 to +67
Tool calls to answer
fewer stepsmedian on knowledge traps, lower is better
47%→91%
a fresh install, learning as it is taught
Starting from an empty knowledge layer, accuracy climbed as business facts were taught one at a time, each question class jumping from floor to ceiling at its own lesson.
The benchmark ablates the platform, not the model, with ground truth generated from a fixed seed and every number read from the platform’s own audit log. Full methodology, figures, and reproduction commands are published.
Architecture
Five services, one endpoint
Plexara composes DataHub, Trino, S3, and two gateways behind a single MCP endpoint. Trino federates your databases. The gateways reach anything else that speaks MCP or HTTP. Every tool call returns enriched with catalog context and applied knowledge.
AI Assistants
Plexara Enrichment
Plexara Capabilities
Via Trino Federation
Why this stack
Tools, meaning, and memory in one envelope
Most agent platforms are tool-callers wrapped in auth. They route the call but pass nothing about what the data means. Plexara's MCP envelope carries three things at once: the tool, the catalog context that explains the response, and the knowledge captured from how this kind of question was answered before.
Tools
How the agent acts
- MCP Gateway
- API Gateway
- Trino + S3
Meaning
What the response is for
- Semantic Enrichment
- Catalog context
- PII + ownership
Memory
What was learned before
- Knowledge Capture
- Past sessions
- Applied corrections
Tools
Any MCP server, any REST or GraphQL endpoint, behind one MCP endpoint. Existing investments plug in without rewrite.
Meaning
Catalog context, ownership, PII flags, deprecation notices, and glossary terms attached to every response. The agent does not have to ask.
Memory
Corrections from past sessions feed the catalog. The next agent inherits what the last one learned. The platform improves with use.
Other stacks force the agent to reason across separate products. Plexara makes meaning a property of every tool call.
Intelligence
Search, Context, and Health You Can See
The platform grounds every answer in your business context and keeps the semantic search behind it healthy and measured.
One Search Across Everything the Platform Knows
A single query fans across the DataHub catalog, canonical knowledge pages, memory, captured insights, saved assets, prompts, connected API endpoints, and connections, grouped by source with a coverage summary and balanced so the largest source never drowns the rest. Your agents call the same federation you see here.
Intelligence
Memory, Insight, Knowledge
One pipeline turns what your team knows into shared, governed knowledge. Memory is captured automatically, the facts worth sharing become insights for review, and approved insights are promoted into canonical knowledge.
Everything the Platform Learns Starts as Memory
Corrections, business context, and preferences shared during sessions land in memory, classified by type: preference, event, business knowledge, operational rule, or schema and entity fact. Most memory is personal and stays with you. It is the raw substrate everything else is promoted from.
Connectivity
Connect Any API, Federate Any MCP Server
Bring every REST API and MCP server your agents need under one governed, audited, context-enriched endpoint.
Turn Any REST API Into Agent Tools
Point the platform at an API and it reads that API's own OpenAPI description to learn every operation and input. Ten APIs do not add a thousand tools: the whole surface is served through four tools, backed by versioned catalogs many connections can share.
Portal
Durable Assets, Curated and Reviewed
Every insight an agent generates becomes a managed, rendered, shareable asset that people can curate, share, and review in place.
AI Output That Renders, Not Just Text
Dashboards, reports, and charts an agent produces are saved as versioned assets and rendered natively in the portal: HTML and JSX as interactive components, SVG as crisp vector graphics, Markdown formatted, CSV as sortable tables. Each carries a record of the tool calls that produced it.
The Challenge
Most Organizations Are Not AI-Ready
It is not an AI problem. It is a data problem.
95%
of generative AI pilots are failing, largely due to data infrastructure gaps
Source: MIT NANDA, The GenAI Divide: State of AI in Business (2025)
15%
of organizations have networks fully ready for AI workloads
Source: Cisco AI Readiness Index (2025)
AI sees rows, not meaning
AI can query your data, but it does not know what the data means. It cannot distinguish deprecated tables from active ones, or identify which columns contain sensitive information.
Context lives in people, not systems
Business rules, data ownership, quality caveats: this context exists only as tribal knowledge in people's heads. When they leave, the knowledge walks out the door.
The real bottleneck is understanding
The bottleneck is not AI capability. It is the gap between raw data and business understanding. AI needs context to deliver trustworthy answers.
Infrastructure was not built for AI
Most data infrastructure was designed for human analysts, not AI agents. Connecting AI to existing systems without semantic context produces unreliable results.
The Process
Three Stages to AI-Ready Data
Plexara is both a product and a progressive process. Start where you are, and build toward full AI integration.
Data Platform Foundation
Don't have a modern data platform? We'll build one.
Plexara begins with implementing a data platform tailored to your data and your business. This is not off-the-shelf. It is an architecture designed around your specific data landscape, built on proven open-source technologies that Deasil Works has deployed and managed for over 25 years. Components may include federated SQL query engines, distributed object storage, data pipelines, and the infrastructure to connect your existing databases into a unified, queryable estate.
Semantic Layer & Knowledge Capture
Don't have a semantic data layer? We'll create one. It becomes your AI's training manual.
We configure a semantic and metadata layer that captures and organizes the business details that often exist only as tribal knowledge: the meaning behind column names, the business rules no one documented, the context that makes data useful. People leave. Context is lost. Institutional memory fades. Plexara turns that tribal knowledge into a durable asset, and that asset becomes context AI uses to give better, more accurate, more trustworthy answers.
AI Integration: The Weave
Plexara means interwoven. We take these components and weave them together into the ultimate tool for AI.
Plexara connects your data platform and semantic layer to AI agents through the Model Context Protocol (MCP), the emerging standard for AI-to-data integration. When AI queries your data, it does not just get rows and columns. It gets business context automatically: ownership, quality scores, deprecation warnings, PII tags, glossary definitions, lineage tracking. AI becomes a domain expert on your business.
Differentiation
What Plexara Is Not
Not a chatbot.
Plexara is infrastructure, not a conversational interface.
Not a copilot.
It does not compete with Claude, GPT, or any AI model. It makes them all better.
Not a generic MCP connector.
It does not blindly execute queries against your data. It ensures AI understands the meaning behind your data.
Not an AI product.
AI models evolve rapidly and AI-specific products become outdated immediately. Plexara is integration infrastructure that supercharges the best AI agents of today and tomorrow.
Not proprietary lock-in.
Built on open standards being adopted by all major AI providers.
Standards
Protocols Outlast Products
The most durable technology investments are protocol-level, not product-level.
HTTP
outlasted Netscape
SQL
outlasted every database vendor of the 1990s
TCP/IP
outlasted everything
MCP: The Next Durable Standard
The Model Context Protocol was created by Anthropic in November 2024, donated to the Linux Foundation in December 2025, and co-founded by Anthropic, Block, and OpenAI. Supporting members include Google, Microsoft, AWS, Cloudflare, and Bloomberg.
97M+
monthly SDK downloads
10,000+
active MCP servers
300+
MCP clients
Plexara bets on the protocol layer, not the model layer. Integration infrastructure is more durable than any specific AI product. While Plexara currently provides the richest experience with Anthropic's Claude, it is built on standards being adopted by all major AI providers.
Capabilities
Built for Enterprise Data Integration
Semantic Enrichment
Query any data source and receive business context alongside your results. Every response includes ownership, quality scores, PII warnings, deprecation notices, and glossary definitions.
Knowledge Capture
Domain knowledge shared during AI conversations (column meanings, business rules, data quality observations) is captured, reviewed, and written back to your metadata catalog.
Lineage-Aware Metadata
Downstream datasets automatically inherit documentation, quality indicators, and business context from their upstream sources.
Enterprise Security
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.
Personas & Access Control
Define who can access which capabilities based on roles mapped from your identity provider.
Federated SQL
Query across PostgreSQL, MySQL, Elasticsearch, Cassandra, BigQuery, MongoDB, Hive, and other sources through a single SQL interface.
MCP Gateway
Bring any MCP-compatible server into the Plexara envelope. Existing MCP investments inherit catalog context, persona-based access, and audit logging without rewrite.
API Gateway
Reach any REST or GraphQL endpoint as a tool. Existing services become first-class agent capabilities, with the same enrichment and governance applied to every call.
Fully Managed Platform
Plexara runs as a fully managed service operated by Deasil. Upgrades, monitoring, and scaling are handled for you. Connect your AI agent to one governed endpoint and start working.
Production
Proven in Production Across Industries
Plexara is not theoretical. It is running in production today.
Retail Analytics
A multi-tenant retail analytics platform integrating point-of-sale data, inventory management, and revenue reporting across multiple data systems.
- Five persona types
- Cross-system query orchestration
- Live knowledge capture and governance
Media & Broadcasting
A media analytics platform spanning six data domains with 141+ cataloged entities, covering video streaming, broadcast ratings, digital analytics, email marketing, audience data, and operational metadata.
- Non-technical leaders asking natural language questions
- Contextually rich answers without SQL
- No data team intermediation required
Partnership
Plexara + Deasil Works
Plexara is a product of Deasil Works, Inc., a technology services company with over 25 years of software development, systems integration, and infrastructure management experience. Deasil Works builds custom data platforms and data warehouse solutions for organizations across media, retail, entertainment, manufacturing, and finance.
The Plexara go-to-market pairs the product with Deasil Works professional services. You get both the platform and the expertise to deploy it in your environment. This is not a SaaS tool you configure yourself. It is an engineered solution backed by a team that has been building enterprise data infrastructure for decades.
From the Blog
Latest Insights
Two benchmarks, one conclusion: the industry is converging on context
dbt Labs benchmarked agents on its semantic layer against text-to-SQL and reached the conclusion our platform ablation reached: govern the context between an agent and the data, and business questions move from unreliable to dependable. Two methods, one finding, and a rule for reading both.
Benchmarking the context layer
We built a benchmark that holds the model constant and varies only the platform. On business-context questions, an agent on raw data tools was right about 43 percent of the time. The same agent on Plexara was right about 99 percent, using fewer tool calls.
Why a Plexara rollout starts small
A first deployment connects two or three data sources and one persona, then expands without rewriting what came before. Phased rollout is not caution for its own sake. It is how a complex system that works actually comes to exist.
Common questions
Plexara FAQ
The Model Context Protocol is an open standard from Anthropic for connecting AI agents to tools and data. Plexara packages your enterprise data behind a single MCP server with semantic context, persistent memory, and governance, so any MCP-capable client (Claude, ChatGPT, custom agents) can answer questions about your business without bespoke integrations per agent.
Learn more: Is MCP just an API wrapper?Snowflake stores and queries data for human analysts. Plexara sits in front of your existing data infrastructure (Snowflake included) and exposes it to AI agents through MCP, with the semantic catalog, governance, and memory those agents need. Plexara does not replace your warehouse. It makes the warehouse usable by AI without bolting on more vendors.
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.
Learn more: Why point-solution catalogs and semantic layers are not enoughPlexara is a managed, fully-engineered solution priced per deployment based on data sources, expected agent volume, and the support tier you need. No per-seat pricing, no marketplace tier. Contact us with a description of your data landscape and use cases for a concrete quote.
Learn more: Replacing the five-vendor data stack with one platformGovernance is enforced when an agent calls a tool, not described in a policy document. Personas restrict which tools an agent can see and use. Default-deny applies to anything not explicitly allowed. Every tool call is logged in a single audit stream tied back to a human user. Connections are managed centrally, with key rotation and revocation as one-click operations.
Learn more: Governance: personas, access, and audit
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Product Overview
See how Plexara unifies query execution, semantic metadata, and governance into a single MCP server.