Training & inference pipelines
Reproducible, orchestrator-native pipelines on SageMaker, Databricks, Vertex, Kubeflow, or Airflow — with clear seams between data, model, and serving.
Services — MLOps & AIOps
Make shipping models a routine event, not a quarterly project.
We build production ML and AI infrastructure for organizations that need deployments, rollbacks, and monitoring to be boring — even under regulatory and audit pressure.
<1d
target time to roll back a misbehaving model on platforms we deliver.
1
owner per pipeline, with documented contracts to data and serving.
∞
audits survived. Lineage, approvals, and evidence are emitted automatically.
What we build
The boring infrastructure that makes ML possible.
We are platform-agnostic. We pick what your team can operate, not what makes for a good vendor demo.
Feature stores & data contracts
Online/offline parity, point-in-time correctness, and contracts between data producers and ML consumers that survive team turnover.
CI/CD for ML
Model registries, automated promotion gates, and rollback paths — so a bad release is a five-minute event, not a five-day incident.
Drift, fairness & performance monitoring
Production monitors wired to your observability stack: data drift, concept drift, calibration decay, and subgroup performance.
AIOps for LLM & agent systems
Eval harnesses, prompt registries, traces, and guardrails for generative systems — treating LLM apps as first-class production software.
Cost & latency optimization
Rightsizing, batching, caching, and quantization strategies that bring inference cost and tail latency under control.
How we work
Strengthen, then standardize.
01
Audit the current path to production
We map how a model gets from experiment to serving today — and where it gets stuck. Most platform problems are workflow problems.
02
Pick the smallest viable platform
We resist the urge to greenfield. Whenever possible we strengthen what you already run; when not, we choose tools your team can own.
03
Migrate one lineage end-to-end
We prove the platform on a single model — pipelines, registry, monitors, rollback — before opening the floodgates.
04
Hand over the runbook
Documentation, on-call patterns, and training so your team operates the platform after we leave. No managed-service lock-in.
Models stuck between experiment and production?
We run a focused MLOps audit on one model lineage and deliver a roadmap your team can actually execute.