Plexara
Product
The AI-First Data Platform
One platform
Your AI Knows the Business. Plexara Runs the Work.
A company needs two things from AI: answers it can trust, and work that keeps happening after the conversation ends. Plexara does both, under one set of access rules and one audit log.
The context
AI that knows your business
The AI reaches your warehouses, lakes, files, SaaS APIs, and other MCP servers through one connection. Answers arrive in your terms, and a correction your team reviews is used by every teammate’s AI from then on.
- Connect
- Know
- Remember
The AI builds it, you agree to what it does, Plexara runs it
The platform
A data platform the AI builds on
What the AI works out does not stay in a chat. It becomes a dashboard that refreshes, an automation that runs on a schedule, a webhook source that collects events, or a feed into the CRM, all running on Plexara with no AI in the loop.
- Data
- Dashboards
- Automations
- Webhooks
- Delivery
The loop closes in the portal: corrections people leave on a dashboard or report go back into the knowledge base, so the next answer starts from them.
What the AI works with
Reach Your Data, Know What It Means, and Build on It
Plexara sits between the AI and everything it is allowed to touch. Every request runs as the person asking, every answer arrives with the definitions, owners, and caveats that make it correct, and the work worth keeping is saved on the platform to run without the AI.
Connect
Integrations
PostgreSQL, MySQL, BigQuery, MongoDB, the lake, and the rest answer one SQL query, with object storage on the same connection.
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API Gateway
Salesforce, Stripe, and your own internal services become tools the AI can call, logged the same way as your data, with nothing custom to build per service.
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API Connections
The APIs already in service, from Salesforce and Google Analytics 4 to Blackbaud, each with the organization’s own knowledge attached.
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MCP Gateway
MCP servers your vendors or teams already run join the same connection, limited by persona and logged per call.
Read the pageKnow
Semantic Enrichment
Query a table and the answer already carries its owners, quality, sensitive-data warnings, and glossary terms. One call does the work of five.
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Memory
What each person teaches the AI is kept across sessions and recalled when it matters, by name, by meaning, or by the data it concerns.
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Knowledge Capture
A correction made in passing becomes knowledge the whole organization holds: reviewed by a person first, and reversible if it turns out to be wrong.
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Catalog Governance
Tags, business areas, and the glossary are curated in the same portal, under the same access rules and the same audit log.
Read the pageBuild
Automations
A process the AI works out once is saved as an automation and runs on a schedule, as often as once a minute, with no AI in the loop and no token spend. Every one is saved with tests.
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Script Libraries
Code many automations repeat, such as paging through an API, date windows, and shaping tables, is written once as a versioned library. Each automation names the version it uses, so a library update never changes it unannounced.
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Webhooks
Point any system’s webhooks at Plexara. Every sender is verified, every event is kept, and each source becomes a table the AI and your automations query.
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Scratch Tables
A CSV, JSON-lines, or Parquet file someone uploads, or one the AI builds, becomes a table in one step. Every one is listed with the name to query, the file behind it, and whether it is current with its file.
Read the pageIn our studies, a fact taught by one person reached a different teammate's AI 98.9% of the time, and made-up company definitions in answers fell from 75% to 0% once Plexara delivered the real one. The research
The knowledge base
Memory, Insight, Knowledge
What one person teaches the AI becomes something the whole company's AI knows, after a person reviews it.
Everything the Platform Learns Starts as Memory
Corrections, business context, and preferences shared in conversation are kept as memory. Most memory is personal and stays with the person who taught it. Everything the organization ends up knowing starts here.
From conversation to production
How an Answer Becomes Work That Keeps Running
The AI does the building, you agree to what it does, and Plexara runs it. The work stops depending on someone remembering to ask again.
- 1
Someone asks
A person asks for something they need every week: a regional sales summary, a sync from the CRM to email marketing, an alert when returns spike.
- 2
The AI works it out
Using your data and your knowledge base, the AI builds the report or the process and runs it against real data with nothing saved.
- 3
You agree to what it does
The AI tells you in plain language what the automation will do, and saves it with tests drawn from real data. A later version that changes what it does waits until you agree to the change. Nobody has to read code.
- 4
Plexara runs it
It runs on a schedule, as often as once a minute, including over the events other systems send to a webhook. No AI is in the loop and no tokens are spent.
- 5
Results reach people and systems
Dashboards refresh, feeds land in the CRM or the warehouse, and messages go to email or team channels. A failed run alerts a team channel.
Where results go
Dashboards, Reports, and Delivery to Other Systems
What the automations produce reaches the people and systems that use it: dashboards and reports that refresh on a schedule in the portal, and data written back into the CRM, email marketing, the warehouse, and team channels.
Dashboards and Reports
An answer worth keeping becomes a dashboard or report that an automation refreshes on a schedule, shared with named people or by link, every refresh kept as a dated version.
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Delivery to Systems
Results go back into the CRM, email marketing, the warehouse, and team channels, by email, chat, or a signed webhook the receiver can verify.
Read the pageThis is work that otherwise needs a BI tool, a flow tool such as Apache NiFi, cron jobs, and the glue between them. On Plexara it runs in one place, under one set of rules, and it is run for you. Plexara and pipeline tools
Dashboards and reports
Dashboards and Reports That Stay Current
What the AI builds is kept, shared, and reviewed in the portal, and the dashboards keep themselves current.
AI Output That Renders, Not Just Text
Dashboards, reports, and charts the AI produces are saved in the portal and shown as they were meant to look: interactive dashboards, formatted documents, sortable tables. An automation can refresh one on a schedule, and every refresh is kept as a dated version.
Governance
One Set of Rules for the AI and the Work It Leaves Running
Access is decided when a tool runs, by the same platform that runs it. The rules that limit a person’s AI in a conversation also limit the automations it saves.
- 01
Personas decide what each person’s AI can use
Tools and data are granted by persona, mapped to your identity provider. Anything not granted is denied.
- 02
Every call is tied to a person
Each query, API call, and automation run is logged with who asked, under which persona, and what came back.
- 03
An automation never reaches further than its author
Saved work cannot reach data or tools that the person who made it could not reach.
- 04
A change in behavior waits for agreement
A new version that changes what an automation does is held until the person it runs for agrees to a plain-language summary of the change.
- 05
Knowledge is reviewed before it spreads
A correction becomes shared knowledge only after a person reviews it, and it can be reversed.
- 06
Your admins see all of it in the portal
Personas, connections, the audit trail, and mail settings are sections of the same portal, shown to the people on your team who hold the admin role.
How a workload moves
One Workload at a Time, Checked Before Cutover
Both clients moved work the same way: the replacement ran beside the original, and its output was compared with the old output before anything depended on it.
- 1
The AI rebuilds the workload
Working from the existing flow or report, the AI writes an automation, runs it against real data with nothing saved, and writes tests from those runs.
- 2
It runs beside the old one
The new automation runs on its schedule while the legacy flow keeps running, so both produce output from the same data.
- 3
The outputs are compared
Rows, columns, and figures are checked against the old output. Every difference is explained or fixed before anything else happens.
- 4
A person approves what it does
The owner reads what the automation does in plain language and approves it. A later version that changes its behavior waits until that person agrees to the change.
- 5
Cutover
Reports, dashboards, and downstream systems switch to the new output. Every run is recorded, and a failed run alerts a team channel.
- 6
The old flow is switched off
Once the new output has held up, the legacy flow is turned off. The next workload starts the same way, when your team is ready for it.
Fully managed
Deasil Works Runs Your Deployment
There is nothing to install and no platform team to hire. Each customer gets a dedicated, isolated Plexara deployment that Deasil Works sets up, hosts, upgrades, and monitors.
Deasil Works handles
- Provisioning a dedicated deployment for your company
- Hosting it in US data centers Deasil operates
- Upgrades as new platform releases ship
- Monitoring, backups, and keeping it healthy
Your team does
- Connects Claude, ChatGPT, Gemini, or another MCP client
- Works with dashboards, assets, and knowledge in the portal
- Approves automations and reviews shared knowledge
- Manages people, personas, and connections in Admin
Under the hood
For the People Who Will Ask How It Works
Architecture
How a request moves through the platform and where access is enforced.
Developer Hub
The MCP and REST surfaces, the test utilities, and the API reference.
Research
Open, reproducible studies of what the platform changes about an AI.
Changelog
A plain-language record of the features added to your deployment.
Common questions
Plexara Product FAQ
Both, on one platform. Plexara gives your AI access to your data and a knowledge base of what that data means, and it runs the dashboards, automations, webhook sources, and data feeds the AI builds. The same access rules and audit log cover the conversation and the work it leaves running.
Any tool that speaks the Model Context Protocol (MCP), including Claude, ChatGPT, Cursor, and agents your team builds in-house. Every one of them gets the same data, the same knowledge, and the same access rules.
No. The AI writes an automation once and tells you in plain language what it will do, and Plexara runs it on a schedule or over incoming webhook events with no AI in the loop and no token spend. The AI comes back only when someone wants to change it.
Learn more: AutomationsWork that usually needs separate tools: dashboards built in a BI tool, flows in tools such as Apache NiFi, cron jobs, and the syncs that push data into a CRM or email platform. Clients move that work one workload at a time, comparing the new output with the old before anything depends on it.
Learn more: Plexara and pipeline toolsPlexara is a managed deployment operated by Deasil Works on infrastructure it runs across six US data center facilities. Each customer gets a dedicated, isolated deployment. There is nothing to install: your team connects its AI tools and works in the portal.
Learn more: Our data center locationsYes. One SQL query can join PostgreSQL, Iceberg, Snowflake, S3, Kafka, Elastic, and dozens of other sources, along with the tables Plexara keeps for you. The AI writes standard SQL without needing to know where the data lives.
Learn more: Trino Query: analytics and insightsPersona-based access control restricts which tools and which data each person can reach through their AI, mapped to your existing identity provider. Anything not explicitly allowed is denied. Every tool call is logged with user identity, persona, and result, so access to sensitive data is auditable end to end.
Learn more: Closed by default: least privilege as the starting pointYes. Plexara has a REST API covering assets, collections, knowledge, memory, prompts, personas, audit, governance, and tool execution, so systems that do not speak MCP can use the same platform. The MCP server and the REST API share one identity, audit, and persona model.
Learn more: Is MCP just an API wrapper?
Deep Dive
Semantic Enrichment
How every answer arrives with the business context that makes it correct.