Production ML, embedded not outsourced.
We embedded with the product organization of a venture-backed enterprise software company and shipped ML features used daily by Fortune 500 operations teams — entity matching, intelligent classification, predictive recommendations. Years inside their codebase, their sprints, their release process.
ML on the roadmap, not in production.
A venture-backed enterprise software company had a clear thesis: machine learning should be a core part of the product, not a side experiment. Their platform was already trusted by Fortune 500 operations teams. But ML was living in notebooks and prototypes, not in the product their customers used every day.
Shipping ML into a mature enterprise platform is a different problem than building a model. It has to survive real data, fit an existing codebase, pass a real release process, and keep working after launch. That is product engineering, not a research project — and it needed people who could work inside the product org rather than alongside it.
ML features that ship and stay shipped.
Three production capabilities, built into the product and used daily by enterprise operations teams — plus the pipelines and quality measurement that keep them honest over time.
Records that resolve themselves
A matching system that links records referring to the same real-world entity across messy, inconsistent sources — turning duplicates and near-misses into a single trusted view inside the product.
Intelligent classification at scale
Models that categorize incoming data the way an expert would, so operations teams stop sorting by hand and the platform routes the right work to the right place automatically.
Predictive recommendations in the flow
Recommendations surfaced where the work actually happens — predicting the next best action and giving Fortune 500 teams a head start instead of a blank screen.
Pipelines and quality measurement
The unglamorous part that makes the rest real: training and serving pipelines, quality metrics, and an iteration loop so model performance is measured and improved in production, not assumed.
Inside the product org, on their cadence.
Embed
We joined the product organization directly — their codebase, their sprint cadence, their tools and standups. Not a vendor on the outside; engineers on the inside.
Build
We built ML features as product, scoped with their PMs and engineers, written to fit the existing platform rather than bolt onto it.
Ship
Everything went out through the client's own release process — reviewed, tested, and deployed to production for Fortune 500 customers, then measured and iterated.
Transfer
Their team owns and operates the models and pipelines today. Across a multi-year engagement, the capability stayed in-house, not with us.
ML that the company actually owns.
ML stopped being a roadmap line and became part of the product. Entity matching, intelligent classification, and predictive recommendations run in production today and are used daily by Fortune 500 operations teams — shipped through the client's own release process, measured for quality, and iterated like any other part of the platform.
Because we worked embedded rather than outsourced, nothing left with us. Their engineers and PMs own and operate the models and pipelines — they can extend them, retrain them, and ship the next ones without us in the room. That is the point of embedding: the team is more capable at the end of a multi-year engagement than it was at the start.
Have ML you want in production, not in slides?
Book a 30-minute call. We'll map where ML actually fits in your product, what it takes to ship it, and how your team ends up owning it.