Plexara
Use Cases
The Whole Year Comes Down to One Week
13
Governed connections
Databases, APIs, metrics, and logs behind one MCP endpoint
3
Storage engines in one query
Search-index aggregations joined to the operational store and the ERP warehouse in a single federated SQL statement
20 min
For a day of analyst work
Ad-hoc analysis that consumed a specialist for a day now takes minutes of agent work plus a human review pass
71
Assets in five months
Dashboards, forecasts, audits, and reports saved to the shared portal
Counts read live from the production platform, July 2026.
The Setup
Everyone Had AI. Nobody Had the Same AI.
Deasil Works develops and supports this retailer's complete stack: the point-of-sale platform, the transactional databases, the ERP integration, the Kubernetes clusters, and the BI reporting on top. AI made every individual on that team faster, and then progress stopped. Every developer, analyst, and administrator had wired up their own tools and their own prompts, with their own private fixes for grounding the model in truth.
The knowledge needed to break through that ceiling was trapped in each silo. Plexara replaced the silos with one governed connection to everything, and the sections below are what happened next. None of it is hypothetical: every artifact described on this page exists on the client portal, dated and attributed.
The Connection
Everything the Agent Can Reach
Thirteen connections behind a single MCP endpoint, each one authenticated, permission-scoped, and audited. An agent can trace a question from a sales number to the database that recorded it, to the pod that processed it, to the log line it left behind.
Data
Operational store
Cassandra: locations, products, users, inventory
Search indexes
Elasticsearch: every transaction, tenant by tenant
ERP warehouse
PostgreSQL: store mappings and card reconciliation
Catalog and objects
Enriched DataHub catalog, S3 exports
APIs
Card settlement
44 governed operations against the processor
Pipeline orchestrator
NiFi: 154 operations for freshness and health
Platform admin
Self-configuration, every write audited
Operations
Kubernetes, read-only
132 operations across the serving clusters
Prometheus, two stacks
Cluster metrics and the database ring
Loki, two stacks
Application logs and database host logs
One governed MCP endpoint
Authentication, per-persona permissions, semantic enrichment, and a full audit trail on every call
- Claude Desktop
- Claude Code
- IDE assistants
- Any MCP client
One Question, One Query
Three Engines in a Single Round Trip
How is the season tracking, by location, right now? Answering that means an aggregation over millions of transactions in the search index, enriched with store names and live status from the operational database. The agent does it in one federated SQL statement, not three queries stitched together by hand.
Search indexes
Aggregate millions of sales events in place
Operational store
Resolve location names, formats, and live status
Business definitions
Cash-basis rules captured from the finance team
One federated query
Joined across engines by the platform, not by the analyst
Season to date, by location
The finance team's number, computed the same way every single time
The definition is the point. Cash-basis net revenue here means netting out returns, loyalty-point tenders, and gift card sales, rules that used to live in one analyst's head. They now live in a saved prompt, so anyone who asks gets the finance team's number, not an approximation of it.
The Season
What the Agent Shipped Under Pressure
A sample from the seventy-one artifacts on the client portal, every one generated in conversation and saved with full provenance.
The peak-day capacity model
Built from live telemetry across both metric stacks and anchored to a June load test. Projected peak-day load came in near one percent of the measured ceiling: a number the client could plan against instead of a guess.
The datacenter-loss analysis
The agent read live cluster state and documented the three-site topology, quorum by quorum: lose an entire datacenter and the peak still gets served. The operations team reviewed the analysis; they did not have to write it.
Release-day verification
A point-of-sale version rolled out wide in the middle of the season. By end of day the agent had confirmed a 99.6 percent cutover, zero ingestion gaps, and six data-integrity checks passing, with the three straggler registers identified by name.
Loyalty fraud scoring
Asked to examine manager-override-locked loyalty accounts, the agent scored 672 of them: risk tiers, transaction-velocity histograms against the fraud-system limit, and a worked list of the accounts worth investigating first.
Proof on a record day
A record sales day looked too good, and the client said so. The agent fingerprinted every transaction on the day, by identity and by content, and proved none were duplicates. Skepticism answered with evidence, the same day it was raised.
The return-rate anomaly scan
Returns per thousand transactions, per location, season over season, scored against the system baseline. Built to surface the locations that are genuinely anomalous instead of flagging rare-return noise.
Compounding
Five Months in Production
The portal tells the story in timestamps. The early assets are simple dashboards. By the peak, the agent is doing capacity engineering, fraud analysis, and live operations reporting, because every month taught it more of the business.
March
First dashboards
Loyalty utilization year over year, discount analysis across store formats, top locations. The agent learns the sales data and its first business definitions.
April
Into the ERP
Inventory operations dashboards against the ERP warehouse, and the first knowledge-capture engagement report: the platform measuring its own learning.
May
Growth analytics
Footprint versus same-store growth decompositions, and an architecture diagram of the backend that the agent drew itself from what it had learned.
June
The hard part
A load test, the capacity model, release-day rollout verification, the fraud dashboard, and the duplicate-sales proof, all in the three weeks before the peak.
July
The peak, and after
A deep operations report on the peak day itself, spanning cluster metrics, logs, and the database ring. Two weeks later, a new card-settlement API connection was added and briefed to the team the same day.
The Handoff
Then the Client's Own Agents Connected
For months, Plexara was internal to Deasil: a way to serve this client faster. Then we connected the client's own agents. Questions that used to be a ticket to us, they now ask directly, and the answers come from the same governed connections, the same definitions, and the same accumulated knowledge.
Our role shifted from doing the analysis to teaching the agents. A question answered once stays answered. When the client doubted a number, the proof was one conversation away. That is the economics of the platform: the work of finding an answer leaves durable value behind instead of evaporating into a closed ticket.
Beyond Retail
The Expert Your Next Model Inherits
Today's frontier models are extremely capable, and they know nothing about your company: the fiscal calendar, the loyalty-point math, the test register that has to be excluded from every report. Plexara exists to close that gap. It is the team member who knows everything, is eager to learn, and shares by default.
Whatever capability next year's models arrive with, an organization running Plexara hands them the entire operation's context on day one.
Next
Public Media Use Case
The other side of the practice: years of analysis expertise turned into knowledge an agent can hold.