Core AI Engineering Services
The infrastructure between a trained model and a working product. Most teams underbuild this and pay for it later. We build it once, properly.
Feature stores
The most common bug we're called in to fix: the model saw one thing in training and something different in production. A feature store closes that gap. It serves the same features, computed the same way, to both sides. Boring infrastructure. It saves you from the worst kind of silent failure.
- One definition of each feature, used in training and serving
- Point-in-time correctness, so you don't leak future data into training
- Reuse across models, so the next project starts ahead
Model deployment
A deploy button isn't a deployment strategy. We ship models with the parts that matter when something goes wrong: versioning, staged rollout, and a rollback that works. So a bad model version is an inconvenience, not an incident.
- Versioned deploys with staged rollout, not all-at-once
- Rollback that actually reverts, tested before you need it
- Serving built for your latency and scale, not a generic default
Model monitoring
A model doesn't crash when it goes wrong. It just gets quietly worse while everyone assumes it's fine. We monitor for drift, degraded accuracy, and the slow rot that comes as the world stops matching the training data. You hear about it before your users do.
- Data and prediction drift tracked continuously
- Accuracy and performance watched against a live baseline
- Alerts that reach a person, not a dashboard nobody checks
AIOps
The operational layer that keeps all of it running. AIOps is the difference between a model your team babysits and one that mostly runs itself, with retraining, pipelines, and incident response wired in. So your data scientists build the next model instead of nursing the last one.
- Automated retraining triggered by drift, not by the calendar
- Pipelines and infrastructure managed as code
- Incident response and on-call playbooks built in
