Services

Data your team can rely on, delivered faster with AI.

Foundations, models, monitoring, and analytics, with AI agents doing the legwork and a senior analytics engineer reviewing every change.

Start here · Free · 30 min

Free 30-minute assessment

A focused call on your data, your pain points, and the highest-value next step, whether or not we work together.

Book the assessment Fixed scope · 1–2 weeks

AI-Accelerated dbt Project Audit

A senior review of your dbt project, a prioritized fix list, and example PRs that show the way forward.

Ask about the audit

What I help with

Six outcomes, one senior engineer.

01 · Data foundations & architecture

A platform that fits how your team works.

Warehouse-centred stacks and the pipelines that feed them, built new or modernized from what you already have.

Target architecture and roadmap
ELT pipelines from SaaS tools, databases, and custom sources
Agent-assisted migrations, with old-vs-new validation before cutover

02 · Trusted data models

Numbers nobody has to double-check.

Business logic written once in dbt, tested, documented, and easy for your team to extend.

Layered, dimensional dbt models
Tests, documentation, and lineage
CI/CD across dev, test, and production

03 · AI-accelerated delivery & audits

Know what to fix first, and fix it sooner.

Agents read your repo and draft tests, docs, and fixes. I verify every finding. The fixed-scope dbt audit is the easiest way in.

Written audit report with a ready-to-assign backlog
2–3 example fix PRs and a readout with your team
Agent-drafted, human-reviewed delivery on any project

04 · Data quality & monitoring

Hear about broken data before your stakeholders do.

An agent loop watches your dbt runs and warehouse, triages failures, and drafts fixes. A human approves every change.

Freshness, volume, and test-failure checks
Likely-cause notes and draft fix PRs
A weekly digest; read-only access by default

05 · Analytics & AI for decision-makers

Answers leaders can act on, from one set of metrics.

Dashboards and reporting on governed metrics, plus a plain-English assistant that answers from those same approved numbers.

BI dashboards and reporting
Metrics defined once in a semantic layer or BI model
AI Analyst / Semantic-Layer Assistant Pilot

06 · Data security & governance

Sensitive data protected, analysts kept moving.

Access and compliance controls built into the platform instead of bolted on later.

Row-level security, masking, and tokenization
FedRAMP-level data security controls
SOX and CCPA compliance

Ways to work: a fixed-scope audit, a defined project, or fractional analytics engineering on a monthly retainer, including team enablement through code review, pairing, and mentoring.

dbt, Snowflake, Databricks — and the rest of the modern data stack, whatever you already run.

Credentials · dbt Certified Developer

How I use AI

Agents draft. A human reviews.

AI speeds up the slow parts: reading unfamiliar repos, writing tests and docs, converting legacy SQL. It never replaces review.

Every change is reviewedAgent output arrives as pull requests that a senior engineer reads, tests, and approves.
Read-only by defaultAgents get the narrowest access the work needs. Nothing changes in production on its own.
Your conventions, written downNaming, layering, and test policy are captured as rule files that you keep.
Clear about tools and data[CONFIRM: AI tools used and client-data policy]

Not sure where to start?

The free assessment points to the best next step. Already running dbt? Go straight to the audit.

Free 30-min assessment AI-accelerated dbt audit