Machine Learning
Production ML that moves the metrics your business is measured on.
A senior-led approach to machine learning.
We ship applied ML — forecasting, ranking, personalization, propensity, and computer vision — from experiment to production with the MLOps required to sustain it.
- Models in production, not notebooks
- Reusable features across teams
- Automated retraining and monitoring
- Measurable revenue or cost impact
Symptoms we're brought in to fix
If any of these feel familiar, you're not alone — and they're solvable.
Notebook-bound models
Great prototypes that never leave a data scientist's laptop.
Model rot
Models in production degrading silently as the world changes.
No feature reuse
Every model rebuilds the same features from scratch.
Unclear ROI
No baseline, no A/B test, no way to prove the model earned its keep.
A focused playbook, adapted to your stack
Every engagement combines these plays in the sequence that fits your context.
Applied ML use cases
Forecasting, churn, LTV, recommendation, and vision at production scale.
MLOps platforms
Feature stores, model registries, CI/CD, and monitoring.
Evaluation & experimentation
Offline and online evaluation, A/B testing, and guardrails.
ML-powered products
Model-driven features embedded in your customer-facing product.
Measurable impact on the numbers that matter
Models in production, not notebooks
Reusable features across teams
Automated retraining and monitoring
Measurable revenue or cost impact
Fluent in the platforms you already run
We're pragmatic about tools — expert across the platforms that lead the industry.
A predictable path from kickoff to value
Discover
Workshops and audits to map your current state, stakeholders, and success metrics.
Design
Reference architecture, roadmap, and phased plan co-created with your team.
Build
A senior pod ships production-grade increments every two weeks.
Launch
UAT, cutover, training, and go-live with white-glove support.
Scale
Enablement, monitoring, and continuous improvement post go-live.
A typical 12-week engagement
Timelines flex with scope — this is what most first engagements look like.
Transparent, outcome-aligned engagement models
Choose the shape that fits your scope. Every plan includes senior practitioners — no juniors, no bench time.
A focused sprint to prove value on a single high-leverage use case.
- Discovery workshop
- One production use case
- 2–4 week engagement
- Async support
A senior pod embedded with your team for continuous delivery.
- Senior 3-person pod
- Bi-weekly demos
- Dedicated Slack channel
- Roadmap & backlog ownership
Multi-workstream program with governance, compliance, and change management.
- Multiple pods
- SOC 2 / HIPAA aligned
- Executive steering
- Global delivery
Common questions about machine learning.
Have a question that's not here? Our team is happy to help on a short call.
Book a consultationAdjacent practices that often pair with this one
Tell us about your machine learning goals.
Share a few details and a senior practitioner will reply within one business day — often with a first read on scope, timeline, and approach.
30-minute strategy call
Pick a time that works. We'll come prepared, share candid perspective, and leave you with two or three high-leverage next moves — whether or not we work together.
- Senior practitioner on the call
- Tailored to your stack and industry
- No pitch deck, no filler
- Follow-up recap within 24 hours