What we do

Three things, done properly

We keep the menu short on purpose. Depth beats breadth when the sea gets rough — here's what each one actually involves.

Pipelines, warehouses & migrations

Data Engineering

The backbone. If the data is wrong, everything downstream is confidently wrong — so we build it reproducible, tested, and governed.

What we build

Layered medallion warehouses on Snowflake, BigQuery, Databricks, or Redshift — version-controlled in dbt or Dataform
Ingestion from anywhere to anywhere, with automated tests and monitoring baked in
Validated migrations with per-table row-count reconciliation — and the discipline to leave the dead weight behind
Cost-aware re-platforming: partitioning, clustering, and architectures that scale with margin, not against it
Custom in-browser analytics apps for when an off-the-shelf dashboard won't cut it

How it goes — We map your current waters first, then deliver in small, reviewable increments — and hand it back governed enough to stay clean after we leave.

548 tables migrated with zero data loss; a SaaS product's ~$40-per-customer nightly cost driven toward near-zero. See it in the logbook →

Production AI that earns trust

AI / ML

Models that earn their keep. The gap between a demo and a system is trust — so we treat evaluation and observability as first-class deliverables, not afterthoughts.

What we build

RAG and document-intelligence systems with provenance — every answer cites the exact source page
Natural-language-to-SQL that shows its work and refuses questions it can't ground
Vision-AI pipelines with provider-switchable models (Anthropic's Claude among them) and confidence on every call
Evaluation harnesses that benchmark the model against human graders before it ships
MLOps: deploy, monitor, and retrain — with a human kept in the loop where it matters

How it goes — We scope to a measurable outcome, prove value on a slice, benchmark it against expert judgment, then scale what actually works.

≥80% agreement with human assessors and ~60s → ~20s per document; draft bills of materials in hours instead of days. See it in the logbook →

The decisions that aren't code

Strategic Guidance

A navigator for the parts a pipeline can't fix: what to build, what to skip, when to change course, and which platform won't become next year's ceiling.

What we build

Data strategy and sequenced roadmaps tied to outcomes, not tool wish-lists
Platform, vendor, and licensing analysis — including the two-scenario financial model that times the savings
Governance operating models: roles, a business glossary, certification, and a change cadence you can self-run
Fractional data leadership when you need the judgment without the headcount
Low-cost, AI-assisted prototypes to de-risk a platform decision before you commit

How it goes — Short, high-signal engagements that leave your team with a plan they can actually sail — and the evidence behind it.

Headed off platform-credit overages with a decision-ready two-year model; validated a $99.4M backlog and caught a stale figure before it reached leadership. See it in the logbook →

How we work

Start small. Prove value. Then go big.

We don't open with a year-long roadmap and a six-figure statement of work. We open with a small, sharp engagement that answers two questions fast: can we deliver real value quickly, and do we actually like working together? When both answers are yes, we stop being a vendor and start being a partner.

Start small. Prove value. Then tackle the big problems together.

The three jobs of data

Strip away the buzzwords and every data problem is one of three jobs — or some mix of them. We're fluent in all three, and honest about which one you actually need.

Movement

Get the data where it needs to be — out of ERPs, CRMs, ad platforms, and operational databases, landed reliably in a warehouse. The unglamorous foundation everything depends on. Done right, it's invisible, validated, and never the thing that breaks at 2 a.m.

Transformation

Turn raw data into something a business can trust and use — through SQL modeling, ML, or AI. This is where most of the value is created, and where most of it is lost.

Communication

Put the data in front of people: an app, a dashboard, an API. Where insight becomes a decision, a workflow, or a product. If nobody can use it, the first two jobs didn't matter.

Build for the AI you'll want next year — not just the report you need this week.

Even when the ask is "just a dashboard," we shape the transformation layer toward data that's ready for ML and AI: materialized master datasets at a clear unit of analysis — wide, exhaustively defined, and high quality. Build it once, well, at the right grain, and the dashboard you need today and the model you'll want next quarter run on the same clean foundation. You stop rebuilding your data layer every time the question changes.

We start with the equation that runs your business, then work backward to the data you need.

The hard question isn't how to build a table — it's which table is worth building. So we find the fundamental equation behind how you create value and decompose it:

Fundamental equation → KPIs → supporting metrics → master datasets → source systems

That points us at the highest-leverage experiments — and exposes the holes, where a number you need has no system capturing it. Closing those gaps is often the most valuable thing we do.

Not sure which voyage you're on? That's usually where we start.

Tell us where it hurts →