Analytics Program Design, Data Infrastructure, and AI Systems

A small consulting team. We work with you directly, one on one.

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AI Analytics Agent What drove the Q3 revenue drop in the West region? Analyzing 847K rows across 6 tables... West region revenue fell 14.2% in Q3. Primary driver: 3 enterprise accounts churned (combined $380K ARR). Seasonal patterns account for remaining variance. West Revenue — Q1 to Q4 Executive KPIs — Live REVENUE $2.4M ▲ 18% NPS SCORE 72 ▲ +4 pts CHURN 2.1% ▼ -0.4% PIPELINE $8.1M ▲ 31% Revenue by Quarter vs Target Actual Target Q1 Q2 Q3 Q4 ↑ Fcst $1M $2M $3M $4M Top Accounts — QTD ACCOUNT TIER REV Acme Corp ENT $184K Meridian Health ENT $142K Frontier Logistics MID $98K Cascade Partners MID $76K Pinnacle Media MID $61K + 41 accounts Export CSV → revenue_by_region.sql SELECT r.region_name, SUM (o.arr) AS total_arr, COUNT (DISTINCT o.account_id) AS accounts, ROUND (SUM(o.arr) / LAG(SUM(o.arr)) FROM orders o JOIN regions r ON o.region_id = r.id GROUP BY r.region_name ORDER BY total_arr DESC ▶ Run Query

The work, in plain terms

No vague deliverables. Here's specifically what we build.

Data Program Architecture

The pattern is consistent: no single source of truth, metrics that mean different things across teams, decisions still routing through inboxes. We design and build the program from the ground up: data contracts, semantic layer, governance model, and the infrastructure to make it run reliably at your scale. The output isn't a dashboard. It's a data organization your team can actually operate.

Data Infrastructure & Pipeline Engineering

Unreliable numbers destroy trust faster than no numbers. We rebuild your data layer — dbt models, ETL pipelines, cloud warehouse architecture — so every downstream report and AI system is pulling from a single source that's clean, tested, and documented. The kind of infrastructure that doesn't break on a Tuesday when someone changes a source schema.

AI Data Agents & Automation

We build LLM-to-data systems that let your team query the warehouse directly — real answers from the actual data, in the time it takes to type a question. Automated pipelines that surface anomalies, generate weekly digests, and eliminate the reporting backlog at the source. Running in live enterprise environments with real users.

Metrics Architecture & Data Strategy

If Finance and Operations are calculating churn differently, you don't have a reporting problem — you have a definitions problem. We establish the canonical metrics your company runs on, document them so they survive turnover, and wire them into your reporting layer so every team is literally looking at the same number. One version of truth, enforced by design, not by meeting.

You deal with the people doing the work.

The same people who scope the engagement build it, and stay through deployment — not an account manager relaying messages to a team you'll never talk to.

Experience at scale, applied where you need it

We've worked inside Amazon's analytics org and across multi-client enterprise engagements. We know what breaks at scale and how to build it so it doesn't — regardless of where you're starting.

Adoption is the deliverable

Success is whether your team makes faster, better decisions next quarter — not whether we delivered a file. We stay until the work is embedded in how you operate.

One engagement, end to end

We handle the full stack — data engineering, modeling, AI layer, and reporting — so you're not coordinating three vendors to solve one problem. One scope, one team, one bill.

AI deployed in enterprise environments

We've built and shipped LLM-to-data agents running in live enterprise environments — handling real queries, for real users, connected to real data. We know what breaks in production and how to keep it from breaking.

No preferred vendors.
No reason to push one.

We work in whatever environment you're already running. If something's missing, we'll tell you what to add and why — not what earns us a referral fee.

BI & Visualization
Whichever BI platform your team already uses — we build in it, not around it. We're not going to recommend switching tools to make our job easier.
Cloud Data Platforms
Cloud warehouses and query engines — we've worked in most of them and get up to speed quickly on anything specific to your environment.
Data Engineering
SQL and Python for the heavy lifting. Pipeline orchestration so the data doesn't just work today — it keeps working six months from now.
AI & LLM Integration
LLMs connected to your actual database — a working system that handles real queries from real users and returns accurate answers. Deployed in live enterprise environments.
Cloud Infrastructure
Comfortable at the infrastructure layer, not just the BI layer. Storage, compute, serverless functions — whatever the pipeline needs to run reliably.
KPI & Metrics Design
Deciding what to actually measure, making sure everyone defines it the same way, and catching it early when the data starts going sideways.

Describe the problem. We'll tell you
if we're the right fit — and if we're not, we'll tell you that too.

Tell us what you're dealing with — numbers that don't match, decisions still made from spreadsheets, reporting that nobody uses. We'll tell you honestly what we can do about it and what we can't.

We read every message personally. If it's not the right fit, we'll tell you that too.