AI answers you can trust.
Governed AI analytics and machine learning, embedded into your data and growth systems.
AI Self-Serve Analytics
Clean data still gives AI confidently wrong answers.
When a question is ambiguous, an analyst asks what you mean. AI picks an interpretation and answers confidently. The difference between a chatbot and a system your leadership can trust is a governed layer of business definitions: what refund rate means, what brand revenue means, what "last quarter" means, encoded once and used everywhere.
21%
Accuracy with a frontier model connected directly to the data warehouse
Same model, after adding a governed layer of data and business context
Source: Anthropic Data Science & Engineering, internal results published June 2026. Consistent with our client implementations.
The same question, two ways
Four days of Slack, or one minute.
TODAY · #GROWTH SLACK
CEO · Mon 9:04am
why did contribution margin dip last week?
Growth lead · Mon 11:37am
pulling it now, Looker and the finance sheet don't match, checking with data
Analyst · Wed 2:15pm
depends how we treat the 3PL rebill, two versions attached
Growth lead · Wed 4:02pm
let's align on Thursday's call
Four days and three competing numbers later, the decision got made anyway.
WITH A GOVERNED LAYER · SAME QUESTION
CEO · Mon 9:04am
why did contribution margin dip last week?
Claude · Mon 9:05am
Contribution margin fell 1.8pts. Shipping cost per order rose $0.84 on a carrier surcharge, and the spring promo lifted discount rate; together that's about 90% of the dip. Product margin was flat.
from governed definitions
Same morning. One number, with the drivers.
Why teams choose us
The other ways to do this, and where they break.
Analytics platforms
Fast to start, and you rent their semantic layer. Definitions built for the median brand break on multi-brand portfolios, subscription models, and anything custom. Leave the platform, lose the definitions.
AI bolted onto your BI
An AI assistant over the warehouse you already have. That's the 21% scenario: no definition layer underneath, so it demos beautifully and then contradicts the dashboard in front of leadership.
Agency bodies
Headcount closes tickets, but no one owns whether the numbers are right. Definitions live in individual heads, quality depends on who's staffed that week, and nothing is maintained after handoff.
The Meridian approach
A small senior team builds the governed layer on the stack you already own, encodes your definitions rather than a template's, and stays accountable for accuracy as the business changes.
You own everything.
Marts, semantic layer, and AI skills live in your repo and your warehouse. No platform, no lock-in.
Your business, encoded.
Multi-brand rollups, licensing revenue, subscription logic: the parts templates can't hold.
Accountable for outcomes.
The deliverable is numbers leadership can trust, not tickets closed.
In production
What this looks like when it ships.
+10% revenue per user
AI that picks each customer's next offer
At a $400M ARR meal marketplace: instead of one promo calendar for everyone, the system decides per customer which offer or nudge comes next, based on their behavior.
Ask-anything analytics
Governed AI self-serve, live in production
At a fitness SaaS: business questions answered in plain English over governed definitions. Decisions stop waiting on an analyst queue, and stop getting made blind while they wait.
How it holds up
The build is table stakes. Staying right is the work.
Foundation
What every implementation needs
One source of truth per metric
Mart models restructured so no question has two answers
Business definitions in code
A semantic layer that is versioned, tested, and auditable
Governed access
Shared vs. user-level integrations, decided deliberately with IT
Keeping it right
Where most systems quietly fail
Definitions ship with model changes
Same repository, same pull request, so drift is structurally impossible
Accuracy measured continuously
Known-answer questions tested on a schedule, not on faith
"I don't know" as a feature
Questions outside governed definitions get declined, not improvised
Accuracy is maintained, not installed.
Definitions drift as the business changes. New brands, new channels, redefined metrics. An unmaintained system doesn't fail loudly; it degrades from accurate to confidently wrong without anyone noticing.
95% → 65%
measured accuracy decay in one month without definition maintenance (Anthropic, 2026)
Answer accuracy over one quarter, with and without definition maintenance
Services
