Data & Platform Engineering

End-to-end builds in your cloud. Data pipelines, standardized transforms, governed datasets, and the ops UI that teams actually run.

PipelinesTransformsOps UI

Clean data first, then applied AI. We say that a lot because we keep getting called in after somebody skipped step one and wondered why the model kept hallucinating their inventory.

This is the plumbing and the screens. Ingestion from the systems you already run, transforms that are readable by a human six months from now, governed datasets with lineage, and the operational UI where your team actually does the work. It lands in your cloud or your Foundry instance, not ours.

We build it so it outlives us. Documented, tested, and handed to your team with the repo access to match. If we disappear, the thing keeps running.

Clean data first, then applied AI. We build the pipelines and the operational screens in your stack, documented and governed, so the work outlives the engagement.

01

Ingestion and connectors

ERP, MES, EHR, CRM, flat files somebody emails on Fridays, sensor feeds, and the SFTP drop nobody has touched since 2019. We connect what exists rather than asking you to replace it.

02

Transforms and standardization

Incremental pipelines with tests, health checks, and readable logic. The same part number means the same thing in every downstream dataset, which sounds obvious until you check.

03

Governed datasets

Access control, retention, and lineage on the datasets that matter, so the people who should see a row see it and the people who shouldn't don't.

04

Operational UI

The screens your team runs the day from. Queues, exception lists, approvals, and write-back. Built for the person doing the job at 6am, not for the demo.

05

Data quality and reconciliation

Fuzzy matching across systems that never agreed on a key, duplicate resolution, and quality gates that stop bad rows before they reach a decision.

06

Handover and documentation

Runbooks, architecture notes, and your engineers in the repo while we build. The goal is that your team can extend this without calling us.

Narrow first. Checkpoint often. Nothing you can't walk away from.

  1. Discovery

    A short sprint with your operators. We map the use case, the objects, and the data you actually have, not the data the diagram says you have.

  2. First build

    We pick the thinnest slice a real person can use on a real day, and ship that. Production-ready on the first release, not a prototype we promise to harden later.

  3. Every two weeks

    A checkpoint and a decision. You see working software, you tell us what is wrong, we adjust. You are never locked into the next phase.

  4. Handover

    Documentation, runbooks, and your engineers in the repo while we build. If we disappear, the thing keeps running.

What you're handed

  • Production pipelines with tests, monitoring, and health checks

  • Governed datasets with access control and lineage to source

  • An operational UI your team uses daily, not a dashboard they screenshot

  • Architecture documentation and runbooks written as we go

  • Repo access and a working handover to your engineers

Shipped, running, and measured.

  • Trailer Manufacturing ERP

    FactoryOS

    Ontology-driven inventory and production workflow connecting vendors, purchasing, and build batches on Foundry & AWS.

    157K+

    Parts Governed

    21K+

    Manufacturing Orders

    8K+

    Units Annually

    1,800+

    Vendors Integrated

  • AI-Powered Financial Operations

    Hospital Network FinOps

    A FinOps platform for hospital networks and nursing homes: vendors, invoices, and close workflows unified across every entity, with AI-driven reconciliation that's auditable end to end.

    42

    Facilities

    95%

    Faster Month-End Close

    1,600+

    Hours Saved

    10x

    Faster Invoice Processing

Tell us what's breaking. If we're not the right team for it, we'll say so and point you somewhere better.