Who we serve

Data-heavy companies whose ambition outran their governance.

Different sectors, same architecture problem: the stack is modern-ish, the definitions are scattered, the analysts are overloaded — and leadership wants AI on top of all of it.

4 profiles·1 composite·1 honest exclusion

Profile 01

Companies with BI, but no governed layer

Tableau or Power BI in production. Snowflake or BigQuery underneath. Maybe dbt. No governed semantic layer — and no AI-ready analytics architecture.

What it feels like

  • Dashboards multiply; trust doesn't
  • Every AI experiment starts from ungoverned definitions
  • Analysts re-validate the same numbers weekly

Director of BI · Analytics Manager · CFO · VP Data

Profile 02

Mid-market SaaS

A modern-ish stack with messy analytics operations. Leadership wants AI; the data team knows the foundation is fragile.

What it feels like

  • Sales, marketing, product, and finance define the same numbers differently
  • Executives ask the same questions repeatedly
  • Analysts stuck in ad-hoc request loops

VP Data · Head of Analytics · RevOps · VP Customer Ops · COO · CFO

Profile 03

PE-backed operating companies

New reporting requirements after acquisition. Data scattered across CRM, ERP, spreadsheets, call systems, and finance platforms. A mandate for operational visibility — fast. For portfolios: the audit is a fixed-fee, standardized instrument — scored output comparable across companies, run one company at a time.

What it feels like

  • Portfolio-level reporting on entity-level chaos
  • “AI transformation” expectations on un-mapped systems
  • Every diligence question is a fire drill

Operating Partner · CFO · VP Operations · Head of Transformation

Profile 04

Healthcare & regulated-adjacent

Organizations where governance isn't optional and hallucination risk is unacceptable. Heavy operational reporting, high documentation burden.

What it feels like

  • AI interest with justified accuracy paranoia
  • Controlled internal knowledge is a requirement, not a feature
  • Data governance scrutiny from every direction

VP Analytics · Director of Data · COO · Compliance-aware AI leads

The composite

Our best-fit client: a 100–2,000 person company on Snowflake/BigQuery with Tableau or Power BI, Salesforce, and Slack or Teams — where the data team is overwhelmed by ad-hoc requests and leadership wants AI but doesn't yet trust the foundation.

A note on fit

We're the wrong firm if you want a chatbot by Friday, a Zapier build-out, generic AI training with no data reality underneath, or a twenty-person transformation bench. And if what you truly need is a full-time data team, we'll tell you on the first call — and make sure that hire lands on a governed layer instead of an archaeology project.

an exclusion stated up front is a scope you can trust

See yourself in one of these?

Five minutes of honest self-assessment will tell you how ready your environment actually is.

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