AI / GenAI Engineering
ML pipelines and MLOps
Model lifecycle done properly — versioned, evaluated, monitored, retrained on a schedule.
The problem
Sound familiar?
- 01Models deployed like static code: no versioning, no rollback, no provenance.
- 02No drift detection — the first sign of a bad model is a customer email.
- 03Retraining is manual and irregular; benchmarks rot.
What we deliver
Concrete outputs.
Model registry with versions, lineage, and provenance
Eval-gated deploys: a regression in any tracked metric blocks the release
A/B routing infrastructure with statistical guardrails
Drift monitoring on inputs, outputs, and label feedback
Retrain triggers (drift, cadence, or manual) wired into CI
Experiment tracking with MLflow, W&B, or SageMaker
Methodology
How we run it.
Phase 1
Baseline
Current lifecycle, gaps, golden datasets.
Phase 2
Build
Registry, eval harness, CI integration, monitoring.
Phase 3
Operate
Retraining cadence, drift response, model retirement.
Related capabilities
What pairs well with this.
- AI / GenAI Engineering
LLM applications and RAG systems
Retrieval-augmented generation pipelines that ground LLMs in your data with citations, audit trails, and a private deployment option.
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Eval harnesses and continuous evaluation
Domain-specific evals that gate every deploy — no vibes-based shipping, no silent regressions.
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Data science and analytics
Pragmatic analytics and ML for business questions — not papers. Forecasting, classification, anomaly detection, and BI you can self-serve.
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Get started
Ready to scope ml pipelines and mlops?
Book 30 minutes — we’ll tell you honestly whether the partnership model fits or whether an SOW is the better path.