# Plexara

> Standards-based data integration for the AI era. Plexara transforms enterprise data into a domain-expert training manual for AI through governed, semantically rich integration via the Model Context Protocol (MCP).

Plexara is a product of Deasil Works, Inc. It is not a SaaS tool. It is an engineered solution pairing platform with professional services. Built on open standards (MCP), it provides federated SQL queries, semantic enrichment, knowledge capture, lineage-aware intelligence, persona-based access control, enterprise security, audit logging, object storage integration, and metadata governance.

## Pages

- [Plexara](https://plexara.io/): Plexara transforms your enterprise data into a domain-expert training manual for AI through governed, semantically rich integration.
- [Our Thesis](https://plexara.io/approach/): The thesis behind Plexara: standards over products, composable architecture, and progressive implementation that captures tribal knowledge.
- [Why Plexara](https://plexara.io/capabilities/): How Plexara solves the hard problems with MCP: context rot, hallucination under load, governance gaps, tribal knowledge loss, and missing enterprise audit.
- [Use Cases](https://plexara.io/use-cases/): How Plexara serves business leaders, data teams, and AI integration scenarios across retail, media, financial services, and manufacturing.
- [Services](https://plexara.io/services/): Deasil Works provides custom data platforms, Plexara implementation, and managed services backed by 25+ years of enterprise data infrastructure experience.
- [Contact](https://plexara.io/contact/): Start a conversation about your data landscape. Tell us what you are trying to achieve with AI and we will respond within one business day.
- [Product](https://plexara.io/product/): Plexara unifies query execution, semantic metadata, and object storage into a single governed MCP server with semantic enrichment and enterprise governance.
- [Benchmark Report](https://plexara.io/benchmark/): A controlled benchmark that ablates the platform, not the model: holding everything else constant, the semantic layer lifted knowledge-trap accuracy from 42.7% to 98.7%, a +56-point gain over bare data tools.
- [MCP Data Platforms vs MCP Gateways](https://plexara.io/compare/): Two categories share the MCP label: gateways govern traffic, data platforms serve data. How to grade any vendor, and how Plexara compares to Snowflake managed MCP, Starburst AIDA, gateways, and building it yourself.
- [Plexara vs MCP Gateways](https://plexara.io/compare/mcp-gateways/): MCP gateways route and authenticate tool calls. Plexara executes queries and returns enriched, governed results. Why connecting independent MCP servers does not add up to a data platform.
- [Plexara vs Building It Yourself](https://plexara.io/compare/build-vs-buy/): What a self-built MCP data platform actually contains: federation, enrichment, personas, memory, audit, and a permanent operations tail. When building is right, and what Plexara replaces.
- [Plexara vs Snowflake Managed MCP](https://plexara.io/compare/snowflake-managed-mcp/): Snowflake's managed MCP server reaches Snowflake-resident data. Plexara federates across warehouses, databases, S3, and APIs, enriches every response, and stores your semantics in open formats.
- [Plexara vs Starburst AIDA](https://plexara.io/compare/starburst-aida/): Starburst AIDA is an assistant inside Starburst's interface. Plexara is an MCP platform that makes Claude, ChatGPT, and your own agents experts on your federated data, with knowledge that compounds.
- [Semantic Search and Enrichment](https://plexara.io/product/semantic-enrichment/): Bidirectional enrichment augments every tool response with business context from complementary services. One call replaces four.
- [Knowledge Capture & Application](https://plexara.io/product/knowledge-application/): Every conversation improves your data catalog. User corrections, agent discoveries, and enrichment gaps flow through admin review into documentation.
- [Memory](https://plexara.io/product/memory/): Persistent memory across sessions, structured into five dimensions. Multi-strategy recall via entity lookup, semantic search, and lineage graph traversal.
- [Governance](https://plexara.io/product/governance/): Governance enforced at the point of execution. Fail-closed security, persona-based tool filtering, comprehensive audit logging, and operational safeguards.
- [Portal Tour](https://plexara.io/product/portal/): A guided tour of the Plexara portal: assets, collections, knowledge capture, prompts, personas, tools, connections, audit, and administration.
- [Integrations](https://plexara.io/product/integrations/): Trino federation across 40+ connectors, DataHub catalog operations, S3 object storage, and MCP client compatibility through a single governed endpoint.
- [API Gateway](https://plexara.io/product/api-gateway/): Connect any REST or HTTP API to Plexara and let your AI assistant search and call it directly. One secure connection that handles sign-in, limits access by role, and logs every call.
- [Changelog](https://plexara.io/product/changelog/): A weekly, plain-language record of new capabilities and improvements in the fully managed Plexara platform. Newest first.
- [For Data Leaders](https://plexara.io/solutions/data-leaders/): Reduce AI data access cost and risk. One platform replaces five point solutions with unified governance, catalog improvement, and vendor independence.
- [For Data Teams](https://plexara.io/solutions/data-teams/): Analyst, engineer, and steward workflows powered by enriched data access, knowledge capture, and a shared workspace for versioned assets.
- [Architecture](https://plexara.io/architecture/): Plexara's 8-layer middleware pipeline composes Trino, DataHub, and S3 through provider abstractions into a single governed MCP server with fail-closed security.
- [Locations](https://plexara.io/locations/): Six data center facilities across the United States. Over 30 years of managing real infrastructure, from bare-metal hardware to custom Kubernetes clusters.
- [History](https://plexara.io/history/): Plexara began in 2024 as Methodology, an internal Deasil Works project for text-to-SQL. The timeline from sentence embeddings and StarCoder to MCP and the platform Plexara is today.
- [Team](https://plexara.io/team/): Plexara is a product of Deasil Works, Inc. Meet the engineers behind it: founders Jeff Masud and Craig Johnston and the core team that has built and operated enterprise systems together for over 25 years.
- [Developers](https://plexara.io/developers/): Build on Plexara. Complete REST API for assets, collections, knowledge capture, memory, personas, prompts, audit, governance, and tool execution.
- [Security](https://plexara.io/security/): How Plexara's managed architecture addresses MCP vulnerabilities disclosed in recent security research, including the April 2026 OX Security findings.
- [Trust Center](https://plexara.io/trust/): Plexara as a vendor: single-tenant deployment model, encryption, access control, incident response, subprocessors, DPA availability, and compliance posture.
- [Vulnerability Disclosure Policy](https://plexara.io/vulnerability-disclosure/): How to report a security vulnerability in Plexara, what is in scope, and what to expect: acknowledgment within two business days and coordinated disclosure.

## Learning hubs

- [Learning](https://plexara.io/learning/): AI, MCP, and data context education for enterprise teams. A self-serve curriculum on LLMs, the Model Context Protocol, and the governed context layer.
- [Insights](https://plexara.io/learning/insights/): Technical deep dives, architecture decisions, and perspectives on building the governed context layer for enterprise AI agents.
- [AI Concepts](https://plexara.io/learning/ai-concepts/): Plain-language foundations on large language models, frontier models, and the concepts every enterprise team needs before adopting AI-native data tools.
- [Plexara MCP](https://plexara.io/learning/mcp/): A plain-language introduction to the Model Context Protocol, how it works, and how Plexara extends it with semantic enrichment, memory, and governance.
- [Asset Workflows](https://plexara.io/learning/assets/): Hands-on recipes for the Plexara asset system: creating reports and dashboards, exporting data, and more.
- [Prompts as SOPs](https://plexara.io/learning/prompts/): The Plexara 400 series: saving the procedures your team discovers with the agent as reusable prompts. Letting the agent author them, sharing them, improving them with feedback, and running them.
- [Newsletter](https://plexara.io/learning/newsletter/): The Plexara Monthly Dispatch archive: once a month, what shipped, what is worth reading, and one practical tip for teams building on governed MCP.

## Insights

- [Five kinds of memory, and how each comes back](https://plexara.io/learning/insights/five-kinds-of-memory/): A fact, an incident, a person, a link, and a habit are five different kinds of knowledge. Plexara stores each as its own dimension and recalls it the way that kind needs, which is what makes memory across sessions feel like memory rather than search.
- [Two front doors, one governed surface](https://plexara.io/learning/insights/the-developer-surface/): Plexara exposes the same governed surface through an MCP server and a REST API. SDKs connect to both, custom tools extend it, and every path shares one identity, one audit log, and one persona model.
- [What you keep if you leave](https://plexara.io/learning/insights/what-you-keep-if-you-leave/): Metadata you author through Plexara lives in DataHub in open formats. If you stop using the platform, you keep the catalog, the lineage, and the definitions your team wrote. Portability is a property of the storage, not a promise on a slide.
- [Why a Plexara rollout starts small](https://plexara.io/learning/insights/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.
- [Own the learning loop, not just the model](https://plexara.io/learning/insights/own-the-learning-loop-not-the-model/): A frontier model never learns your institutional knowledge. It only gets better at using what your people supply and frame, so the durable equity is built outside the model: a loop that captures human and model coordination and keeps it inside your own ecosystem. That loop is most of what Plexara already is.
- [When prompts become shared infrastructure](https://plexara.io/learning/insights/governed-prompts-and-relevance-search/): A good prompt is only useful if other people can find it, trust it, and run it. Treating prompts as governed, searchable assets turns one person's good question into everyone's.
- [Closed by default: least privilege as the starting point](https://plexara.io/learning/insights/closed-by-default-access/): Access control that starts open and gets locked down later never actually finishes. Closing connection access by default means every role sees exactly what it was granted and nothing more.
- [Search the capability, not the manual: how Plexara keeps a wide platform light](https://plexara.io/learning/insights/search-the-capability-not-the-manual/): Loading every API spec into context does not scale. A small, fixed tool footprint plus semantic endpoint discovery keeps cost tied to the task, not the size of the platform.
- [Letting the agent find the right tool](https://plexara.io/learning/insights/intent-driven-tools-and-memory/): An agent that rereads every tool description on every turn is slow and error-prone. Selecting tools by intent, and remembering context across sessions, is what makes a wide platform feel fast.
- [The combinatorial platform: when an agent can see across the whole stack](https://plexara.io/learning/insights/when-an-agent-can-see-the-whole-stack/): A warehouse connection tells you what the data is. The valuable questions need an agent that reasons across query, catalog, orchestration, and source layers at once.
- [Meeting enterprise systems where they are](https://plexara.io/learning/insights/enterprise-authentication-and-isolation/): Real enterprise APIs authenticate in messy ways: client certificates, basic auth, second credential headers. Supporting them, while keeping each user activity isolated, is what makes an agent usable at work.
- [Why more tools won't make your agent smarter](https://plexara.io/learning/insights/why-more-tools-wont-make-your-agent-smarter/): An agent with fifty tools and no context is a confident intern with root access. Capability scales with understanding of the data, not connector count.
- [Why an agent needs a versioned API catalog](https://plexara.io/learning/insights/versioned-api-catalogs/): Pointing an agent at an API is the easy part. Making it dependable when the API changes, returns surprises, or needs a second credential is the hard part. A versioned catalog is what turns a connection into something you can rely on.
- [Why proximity matters: tools, meaning, and memory belong together](https://plexara.io/learning/insights/why-proximity-matters-tools-meaning-memory/): Most AI agent stacks are gateways wrapped in auth. The hard work is not routing tool calls; it is making sure context arrives with them.
- [The context gap in AI data access](https://plexara.io/learning/insights/context-gap-in-ai-data-access/): AI agents can execute SQL, but without business context they generate inaccurate queries and untrustworthy results. Not a better model. Better context.
- [Protocols outlast products](https://plexara.io/learning/insights/protocols-outlast-products/): MCP, Trino, and DataHub are open protocols with communities larger than any vendor. Building on protocols, not proprietary platforms, is the durable choice.
- [How knowledge application turns usage into documentation](https://plexara.io/learning/insights/knowledge-application-turns-usage-into-documentation/): Most data catalogs are empty because documentation is a separate task. Plexara inverts this: documentation happens as a byproduct of people using data.
- [Governance at execution time vs. catalog time](https://plexara.io/learning/insights/governance-at-execution-time/): Traditional governance creates policies in a catalog and hopes they are enforced. AI agents expose the gap. Closing it unifies governance with execution.
- [Token efficiency in enterprise MCP deployments](https://plexara.io/learning/insights/token-efficiency-in-enterprise-mcp/): Most MCP implementations waste tokens through tool explosion, redundant metadata fetches, and repeated context. Three mechanisms eliminate these costs.
- [Replacing the five-vendor data stack with one platform](https://plexara.io/learning/insights/replacing-the-five-vendor-data-stack/): The modern data stack costs $300K-$1M per year across 5+ products. Plexara consolidates catalog, query, governance, enrichment, and agent framework into one.
- [Why MCP gateways are not enough](https://plexara.io/learning/insights/why-mcp-gateways-are-not-enough/): MCP gateways solve the plumbing problem but not the meaning problem. A gateway authenticates a tool call. It cannot tell you what the data means.
- [Why incumbent AI assistants are not enough](https://plexara.io/learning/insights/why-incumbent-ai-assistants-are-not-enough/): Every major warehouse vendor has an AI assistant. They work well within their own ecosystem. The problem is that your data does not live in one ecosystem.
- [Why point-solution catalogs and semantic layers are not enough](https://plexara.io/learning/insights/why-point-solution-catalogs-are-not-enough/): Data catalogs document data but cannot execute queries. Semantic layers define metrics but delegate execution. Neither provides unified context and access.
- [Benchmarking the context layer](https://plexara.io/learning/insights/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.

## Newsletter

- [Monthly Dispatch: July 2026](https://plexara.io/learning/newsletter/2026-07/): Canonical knowledge pages land, linked like a wiki, and our benchmark goes public: the same agent went from 42.7% to 98.7% correct on business-context questions. Plus a consolidation tip and four outside reads, two of them benchmarks.
- [Monthly Dispatch: June 2026](https://plexara.io/learning/newsletter/2026-06/): A human feedback and review loop lands end to end, the context Plexara attaches to answers now reaches every client, and our flagship piece argues the real asset is the learning loop you own, not the model you rent.
- [Monthly Dispatch: May 2026](https://plexara.io/learning/newsletter/2026-05/): The MCP Gateway reaches general availability and the API Gateway opens in beta. Plus three reads from the Learning section, a reusable-prompts tip, and four outside perspectives on enterprise MCP.

## AI Concepts (100 series)

- [What is a Large Language Model?](https://plexara.io/learning/ai-concepts/what-is-a-large-language-model/): LLMs predict the next token from patterns learned across trillions of training examples. What that means, why it produces fluent reasoning, and its limits.
- [Tokens and your budget](https://plexara.io/learning/ai-concepts/tokens-and-your-budget/): Every LLM interaction is priced and rate-limited in tokens. What a token is, how much text fits a budget, and how to avoid wasting tokens on useful answers.
- [Context, compression, and memory](https://plexara.io/learning/ai-concepts/context-windows-and-tokens/): The context window is a model's working memory. Each session balances keeping, compressing, or clearing it. Plexara adds enterprise memory on top.
- [Frontier models, specialized models, and why enterprise AI uses both](https://plexara.io/learning/ai-concepts/frontier-models-explained/): Frontier models bring world knowledge; small local embedding models power memory and catalog search. Knowing each role designs AI systems that work.
- [What is an AI agent?](https://plexara.io/learning/ai-concepts/what-is-an-ai-agent/): An AI agent is not a chatbot and not magic. It is a short loop (think, call-tool, observe, think again) on top of a language model. Mental model first.
- [Is MCP just an API wrapper?](https://plexara.io/learning/ai-concepts/mcp-vs-apis/): MCP is not a replacement for your APIs and not a thin proxy. It is an application layer on top, like a website is an application layer on top of its APIs.

## Plexara MCP (200 series)

- [Anatomy of a Plexara MCP](https://plexara.io/learning/mcp/what-is-an-mcp/): 201 shows what is actually in the box on a Plexara server: the tools, resources, prompts, memory, and knowledge the agent sees. This lesson is the map.
- [Your first day with Plexara](https://plexara.io/learning/mcp/first-engagement/): A new Plexara user gets connected, sends a first question, and platform_info takes over. Walks through day one and how subsequent turns inherit context.
- [DataHub Search: describing the domain](https://plexara.io/learning/mcp/datahub-search-describing-the-domain/): Plexara enriches every query with semantic context, but a short prompt asking the agent to describe the domain first brings world knowledge to the session.
- [Trino Query: analytics and insights](https://plexara.io/learning/mcp/trino-query-analytics-and-insights/): Plexara reaches customer data through Trino. What Trino is, how it maps to DataHub metadata, why OLAP queries finish fast, and how to export large results.
- [Assets: dashboards, reports, and data](https://plexara.io/learning/mcp/assets-dashboards-reports-and-data/): Plexara's asset system persists dashboards, reports, and exports outside chat. Naming asset tools in a prompt saves tokens and keeps outputs shareable.
- [Knowledge: from memory to insights](https://plexara.io/learning/mcp/knowledge-from-memory-to-insights/): Plexara ships session-coupled memory and a distinct insights pipeline that, once admin-reviewed, promotes observations into org-wide catalog documentation.
- [Governance: personas, access, and audit](https://plexara.io/learning/mcp/governance-personas-and-access/): Governance in Plexara is enforced when a tool is invoked, not described in a policy. Personas, default-deny, layered safeguards, and a single audit log.
- [MCP Prompts: reusable prompts](https://plexara.io/learning/mcp/mcp-prompts-reusable-prompts/): MCP prompts are reusable instructions for agents. Plexara ships a default library and adds user-, persona-, or org-scoped prompts through the Portal.
- [MCP Resources: templates and examples](https://plexara.io/learning/mcp/mcp-resources-templates-and-examples/): An MCP resource is reference material the server makes addressable by URI. Admins add them via the Portal or API so the agent can pull them into a session.
- [Putting it all together: a worked end-to-end example](https://plexara.io/learning/mcp/tool-survey/): A capstone that walks a single real-world question through every Plexara subsystem covered in the 200 series, with a tool-by-tool reference at the end.

## Asset Workflows (300 series)

- [Creating reports and dashboards](https://plexara.io/learning/assets/creating-reports-and-dashboards/): AI chat tools produce excellent dashboards and reports in HTML, JSX, and SVG, formats that do not move easily through normal business workflows. Plexara gives them a home in the portal under Assets: your team's catalog of AI-built work, shared like Google Docs, stored in your S3, editable in place, and discoverable by future agent sessions. This article is the working playbook, including the two prompting habits that make the agent produce a saved asset efficiently.
- [Exporting data](https://plexara.io/learning/assets/exporting-data/): When a teammate asks for the data instead of the dashboard: a spreadsheet to pivot, a JSON to feed another system, a markdown table for a wiki. Plexara has a dedicated path for this called Trino Export. It runs the query, writes the file straight to your S3 bucket, and never puts the rows in your chat. This article is the analyst's playbook.
- [Sharing your work](https://plexara.io/learning/assets/sharing-your-work/): Plexara has two share modes (a user share to a named teammate, or a public link anyone with the URL can open) and three controls that apply to either (expiration, notice text, revocation). The mental model is Google Docs. This lesson is the analyst's playbook for picking the right mode, configuring it correctly, and knowing what the recipient actually sees on the other side.
- [Creating collections](https://plexara.io/learning/assets/creating-collections/): A board briefing is rarely one dashboard. It's a dashboard plus a summary plus the underlying data, opened from a single link in the order you chose. Plexara calls that packaging unit a collection. You can ask the agent to assemble one during the same session that produced the assets, or build one by hand on the Collections page. This lesson covers both.
- [Editing what you already have](https://plexara.io/learning/assets/editing-what-you-already-have/): When the dashboard you saved last week is mostly right but needs a fix, you do not re-create it from scratch. You edit the existing asset in place. The link the recipient already has keeps working, the version history accumulates on one asset instead of fragmenting across copies, and any collection that references it picks up the change. This lesson covers the three kinds of edit (metadata, content, revert), what each one does to the version history, and the portal vs agent path.
- [How an asset was built](https://plexara.io/learning/assets/how-an-asset-was-built/): Every asset in Plexara carries two kinds of metadata: descriptive fields the agent fills when it saves the asset (name, description, tags) and that you can edit later, and provenance the platform records on its own. Provenance is the audit trail Plexara captures at the MCP boundary: the catalog searches and queries the agent invoked, with what parameters, in the producing session. This lesson opens that record, names what it can and cannot tell you, and shows how to use it to answer the questions stakeholders ask about a number.
- [Reproducible prompts](https://plexara.io/learning/assets/reproducible-prompts/): Plexara has a first-class prompt object: a saved instruction template with named arguments that you (or a teammate) can re-run later with different values. Manage Prompts (manage_prompt) is the tool. This article covers what a prompt record actually is, how arguments substitute at run time, which scope to pick (personal, persona, global), the four built-in workflow prompts, and the honest limits of what re-running a prompt does and does not guarantee.

## Legal

- [Privacy Policy](https://plexara.io/privacy/): Plexara privacy policy. How Deasil Works, Inc. collects, uses, and protects your information. We do not sell or share your data.
- [Terms of Service](https://plexara.io/terms/): Plexara terms of service. Terms and conditions for using the Plexara platform and services operated by Deasil Works, Inc.