AI & Machine Learning
Enterprise Machine Learning Development
Production ML pipelines your risk team signs off: feature stores, drift monitoring, explainability, and audit trails on every prediction.
What you get
Three outcomes we commit to before we start.
01
MLOps that survives audit
Every model has a lineage: which data trained it, which code shipped it, which config runs it in production. Reproducing any prediction from six months ago is one command, not a two week archaeology dig.
02
Feature store as the single source of truth
Feast or Tecton feature stores prevent the classic 'training serving skew' bug. The same feature definitions that trained your model are the ones that score in production: with online/offline consistency guarantees.
03
Model risk documentation on delivery
For regulated deployments we ship SR 11-7 / OCC 2011-12 aligned model risk docs: development validation, monitoring plan, challenger model, and adverse action reason mapping. Your model risk team gets what they need without a scramble.
The Guaranteed Production Pilot
Fixed scope · Written targetA production Enterprise ML system in your VPC: audited, documented, owned by your team.
Not a slide deck and not a sandbox demo: a working Enterprise ML deployment inside your own cloud boundary, mapped to your compliance controls and handed over with the schema, the eval harness, and the runbook.
Architecture and success criteria signed off in week one. First working slice running in your environment inside 30 days.
Fully done for you. Our senior squad owns ontology, build, evals, and compliance mapping: your team reviews and signs off, nothing more.
Fixed scope, fixed price, and a measurable success target agreed in writing before we start. Miss the target and you don't pay for the pilot.
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Where Enterprise ML lands in production.
Service FAQ
People also ask about enterprise ml.
We build on whatever your data team already operates. SageMaker for AWS native shops. Vertex AI for GCP. Databricks for teams already invested in Spark. Self hosted (Kubeflow / MLflow / BentoML) when compliance requires it. No 'you must migrate to our preferred stack'.
Not sure which lane is yours?
Which lane needs enterprise ml right now?
A 30 minute call and we'll tell you whether this service or a different starting point fits your team best.
Enterprise
Fortune 500 & Regulated Industries
Scale ups
Founders & High Growth Product Teams
Brands
CMOs, D2C, Franchises & Retail
Small Teams
Trades, Agencies, Founders & Solopreneurs