Plexara
Use Cases
Teaching the Agent Everything We Know
17
Live connections
The warehouse, the OLAP layer, object storage, the catalog, and thirteen APIs
1,400+
Governed API operations
From a 577-operation CRM API to a nine-operation membership vault, every call authenticated and audited
9
Canonical knowledge pages
Query routing, ratings math, the membership lifecycle: reviewed truth, not tribal memory
67
Assets in five months
Dashboards, audience briefs, recovery reports, and onboarding guides on the shared portal
Counts read live from the production platform, July 2026.
The Expertise
Years of Analysis Made Deasil the Expert
The client is a regional public broadcaster with a national online presence: broadcast, streaming, membership, and events. Deasil manages their data warehouse and operates the pipelines that feed it from more than a dozen sources: Nielsen, Google Analytics, YouTube, Blackbaud, Iterable, zkipster, Hightouch, Sprout Social, Domo, and more.
Years of anomaly hunts, ad-hoc reports, and pipeline debugging made us expert in every idiosyncrasy of that estate. Documentation captured some of it. Onboarding a new analyst still took months, because the real knowledge lived in people. That knowledge is exactly what Plexara is built to hold.
The Estate
Fourteen Systems, One Endpoint
Frontier models already know what Nielsen is and what YouTube data looks like. What they cannot know is how this organization's pipelines shape that data, or what each field means to each department. Plexara puts both within reach: the systems, and the accumulated understanding of them.
Data
Warehouse
Ratings, streaming, web, email, and CRM, federated
OLAP indexes
OpenSearch mirrors for fast aggregation
Catalog and objects
Enriched DataHub catalog, S3 exports
APIs
CRM system of record
577 operations: constituents, gifts, events
Email marketing
148 operations: campaigns, opens, clicks
Video and membership
Media management and the membership vault
Events, social, BI, files
Guest lists, social analytics, reverse-ETL
Operations
Pipeline orchestrator
NiFi: 102 operations for flows and freshness
Kubernetes
131 operations across the platform workloads
Platform admin
Self-configuration, every write audited
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
The Method
Ask the Agent First
Deasil adopted a rule for every client request: ask the agent before reaching for tribal knowledge. The loop below is what turns that habit into an asset the whole organization owns.
- 1
Ask
Every client request goes to the agent first, with the context we would give a new hire.
- 2
Fail visibly
A human mentor forgets to hand over context. The agent makes the omission obvious: it gets the answer wrong.
- 3
Capture
The correction becomes a captured insight: what counts as a donor, why the prospect feed has a v2, which metrics must never be summed.
- 4
Review
An expert reviews the queue, merges related insights, and resolves contradictions before anything becomes shared truth.
- 5
Know
Approved knowledge lands in the catalog and the knowledge pages. Every future session, by anyone, starts from it.
Nine canonical pages exist today, covering query routing, ratings mathematics, the membership lifecycle, and the pipeline stack. Every pass through the loop leaves the platform knowing more than the last.
In Practice
Work That Used to Be a Project
A sample of the sixty-seven artifacts on the client portal. Each one was a conversation, and each one stayed: dated, attributed, and reusable.
A win-back audience in an afternoon
Expired members who were fans of one original series: 991 people, built from consented viewing data joined to the prospect feed, with hours watched and episodes per fan attached. The privacy constraint was honored in the query, not in a caveat.
An acquisition brief that argues back
Asked for a conversion audience, the agent tiered 37,000 prospects by streaming device and email engagement, and flagged that viewing history was not a usable signal for non-members. It knew the consent rules better than the request did.
Cross-system correlation
Membership records joined to 336,000 email click events, with bots and two high-volume scanner accounts excluded. The kind of analysis that used to need two specialists and a week of calendar time.
Root cause across the fence
Monthly analytics were missing browser users. The agent traced the gap through the pipeline to an upstream vendor intermittently dropping a required tag, and shipped the evidence queries along with the verdict: the fix belongs to the vendor.
Pipeline recovery, verified
After a webhook ingestion bug was fixed, the agent inventoried all forty affected event syncs, confirmed the two-month backlog had recovered, and left a cross-reference report behind for the next person who asks.
A first look at every connection
Every new API connection gets an agent-authored capability brief the same day it lands. The newest one was connected, tested against thirteen live profiles, and documented for the team in a single session.
Compounding
From First Question to Working Cadence
The portal tells the story in timestamps: exploration first, then capability briefs, then audience engineering, then a monthly rhythm the client runs with us.
March
First questions
Streaming dashboards, constituent geography, and the first data-quality root cause: a missing-users issue traced upstream with evidence attached.
May
A first look at everything
Ten agent-authored capability briefs, one per connection, plus a team onboarding guide. Each new API arrives already explained.
June
Audience engineering
The new prospect feed launches with a brief for engineers, a guide for marketing, and the win-back and acquisition audiences built on top of it within days.
July
A working cadence
A monthly review with the client, ratings reach reports after a vendor fix, and a social analytics connection added and briefed the same morning.
The Pattern
The Second Request Is Cheap
Client questions are often iterations on questions already answered. That ad-hoc report from last year: we need it again, but with one new dimension.
Two things make the second request cheap. Developing the report the first time produced insights that were captured and synthesized into knowledge: what the data means, where it lives, how to get it. And the report itself has requirements that are not business knowledge at all: which sections, which columns, the language, the audience. That belongs in a saved prompt, not the knowledge base.
Knowledge describes the data and the business. Prompts describe what to do with them.
The Handoff
Handing the Client the Keys
Giving the client this workflow meant connecting their agent to Plexara, a five-minute task per seat. Any employee with an AI assistant can now make that assistant an expert on the business. The onboarding guide is an asset on the portal, and adoption is reviewed with the client every month.
Our role shifted from answering questions to curating what the platform knows. The arc of our practice describes the change: from ad-hoc data analysis, to prompt engineering, to knowledge engineering.
The Practice
What Deasil Uses First
Plexara is something Deasil offers its customers, and it is what we use first ourselves. Sometimes the exchange runs the other way: the agent has found things in the data that even we did not know were there, gaps nobody had thought to look for.
This is what the partnership between a human expert and an agent looks like in practice. The client did not just get faster answers. They inherited the expertise that produces them.
Next
Use Cases
The capabilities behind both stories: using data and invoking APIs, and how they compound.