Engineering & Infrastructure
AI Engineering & Model Ops
3–8 weeksModels in productionDemo
Each version trains, tests, and deploys itself — watched 24/7 and retrained if accuracy drops.
Monitoring
24/7
catches any drift
Uptime
99.9%
of the service
Retraining
auto
self-adjusts
Getting a model to production is half the work; keeping it reliable is the other half.
We integrate AI capabilities (prediction, classification, extraction, generation) directly into your existing applications, APIs, and workflows — and establish the operational infrastructure that keeps them running, monitored, and evolving. Without disrupting what already works. Technically: ML microservices (REST/gRPC) with shadow mode and feature flags for gradual rollout; experiment tracking (MLflow, Weights & Biases), versioned model registry, retraining CI/CD, data and concept drift detection (PSI), champion/challenger strategies, and model card documentation.
Use cases
- —You want to add prediction, classification, or extraction to an application already in operation
- —You need to replace manual steps with ML services integrated into your current workflow
- —You have models in production but do not know whether they still perform as expected
- —You need to retrain models in a controlled and automated way
- —You want to version, govern, and document the model lifecycle for audit or compliance
Deliverables
- —Integrated AI microservice or module with documented API contract
- —Shadow-mode or feature-flag deployment and rollback plan
- —Experiment tracking setup and versioned model registry
- —Automated retraining pipeline with configurable triggers
- —Data and concept drift detection system with alerting
- —Champion/challenger strategy and production model cards