Rated 5 star on Google

Data engineering that makes AI possible

Your CRM says one number, your accounting says another, and the report takes three days to assemble by hand. We build the pipelines, integrations, and central data systems that make your numbers agree, your reporting automatic, and your data ready for the AI you actually want to run.

AI starts with data you can trust

Every failed AI project we've reviewed failed here first. The model was fine. The data underneath it was not.

Book a strategy session ($350)

A good fit if you

  • Run several systems whose numbers don't match
  • Build reports by exporting spreadsheets and stitching them together
  • Want AI or analytics and have been told your data isn't ready
  • Have grown by acquisition or by adding tools and now can't see the whole business
  • Need one place where the truth lives

Data engineering is the unglamorous work that makes everything else in AI possible: getting data out of the systems where it's created, cleaning and reconciling it, transforming it into a shape that can be analyzed, and storing it somewhere central that the whole business, and every model, can rely on. When it's done well nobody notices. When it's skipped, every downstream project inherits the mess.

We build the pipelines that move data from your CRM, your accounting system, your field service or practice management software, your ad platforms, your call tracking, and your website into one warehouse. We define the business rules once, so a customer, a job, or a policy means the same thing everywhere. And we make the whole thing run itself, so the Monday report is ready on Monday without anyone exporting anything.

The result is AI-ready infrastructure. Forecasting, churn prediction, lead scoring, and generative AI applications all need clean, joined, current data. We build the foundation so those projects start on solid ground rather than spending their first two months fixing what should already work.

What we build

Infrastructure that runs unattended, documented so your team owns it, and sized to what your business actually needs.

Tools and stack we work in

  • BigQuery
  • Snowflake
  • Databricks
  • dbt
  • Fivetran
  • Airbyte
  • Apache Airflow
  • Postgres
  • Google Cloud
  • AWS
  • Azure
  • Looker
  • Power BI

We build in the cloud you already use or help you choose one, and we prefer managed services your team can operate without a full-time data engineer. Nothing here depends on us to keep running.

  • Data pipelines and integrations

    Automated extraction from every system you run, including marketing platforms, call tracking, CRM, accounting, and operations software, into one destination.

  • Central data warehouse

    One governed home for your business data in a modern cloud warehouse, modeled so a question has one answer.

  • Transformation and business logic

    Definitions of customer, revenue, job, policy, and every other entity applied once, consistently, and documented.

  • Data quality and monitoring

    Tests that catch missing, duplicated, or broken data before it reaches a report or a model, with alerts to the right person.

  • Reporting layer

    Dashboards and scheduled reports that assemble themselves from the warehouse, replacing the spreadsheet ritual.

  • AI-ready datasets

    Clean, joined, historical tables prepared specifically for forecasting, scoring, and generative AI grounding.

  • Governance and access

    Who can see what, retention rules, and audit trails appropriate to your industry, from HIPAA to consumer privacy law.

Where the foundation pays off

Any business whose growth has outrun its ability to see itself clearly.

Insurance agencies

Agency management, carrier, marketing, and call data joined so retention, bind rate, and acquisition cost are one report, and renewal models have history to learn from.

Multi-location home services

Jobs, revenue, crews, and marketing spend by service area and technician in one place, so pacing and profitability decisions stop waiting on the bookkeeper.

Medical and aesthetic practices

Practice management, scheduling, marketing, and patient communication data unified with the access controls a practice requires.

E-commerce

Orders, inventory, ad platforms, and customer behavior joined for true margin by product and channel and for the models that drive recommendations.

Real estate brokerages

Leads, transactions, agent performance, and marketing attribution in one warehouse instead of four systems and a shared spreadsheet.

Professional services and B2B

Pipeline, billing, delivery, and marketing data connected so revenue per client and cost to acquire are visible without a quarterly project.

Why we care about your plumbing

Because marketing attribution is a data engineering problem.

To know what a lead from a given campaign was actually worth, you need the ad platform, the call tracking, the CRM, and the accounting system to agree about who that customer was and what they paid. For most businesses they don't, and so the budget gets allocated on guesses. We got tired of guessing.

Once the data is joined, everything downstream improves at once. Attribution becomes real. Forecasting becomes possible. Lead scoring has something to learn from. Generative AI has clean facts to ground on. Data engineering is the one project that makes every other project cheaper, which is why we often recommend it first even when the client came asking for something flashier.

How we work

  1. Discovery and data audit

    We learn how the business makes money, where the decision or the workflow actually breaks, and what data exists to fix it. If the data isn't there yet, that becomes step one.

  2. Roadmap and architecture

    One plan that names the outcome, the integrations, the guardrails, and the budget. Sequenced so the fastest win funds the longer build.

  3. Build and integrate

    We build inside your systems and your accounts, connect to the tools you already run, and test against real data before anything touches a customer.

  4. Measure and improve

    Monthly reviews on the business number the system was built to move, not on model accuracy in isolation. We keep tuning after launch.

The same standards as everything else we do

  • You own the code and the data

    Source, models, prompts, pipelines, and accounts are yours. If we ever part ways, everything we built stays with you.

  • Revenue model first

    We ask how the company makes money before we ask which model to use. AI that doesn't move a business number is a science project. It's how we work on everything.

  • No hidden fees

    Scope is written before it's priced. Cloud and API costs are passed through at cost, not marked up.

  • Senior people, always

    The engineer who scopes your system is the one who builds it. No handoff to a junior pod after the contract.

  • Guardrails by default

    Human review where decisions carry risk, logging on every automated action, and a kill switch you control.

  • Three languages

    Systems that read, write, and talk in English, Spanish, and Portuguese, because your customers do. See where we operate.

Questions we get asked

If your numbers live in more than two systems and you make decisions from reports, probably yes. A warehouse is where those systems meet and agree. For smaller operations it can be lightweight; the principle matters more than the size.

One place where the truth lives.

Tell us which report takes the longest to build and which two systems disagree most. Thirty minutes tells us what it would take to make that go away.

Book a strategy session ($350)
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