AI Development Services

We analyze the business problem and available data. We determine which ML/AI approach will deliver maximum impact. We assess data quality, define success metrics and acceptance criteria. The result is a technical specification with clear scope boundaries.

We build a minimal working model on real data. We test the hypothesis: are target metrics achievable? The PoC shows whether it's worth investing in full-scale development and reduces risks before the main phase begins.

We design the architecture, select and train the model. Feature engineering, hyperparameter experiments, cross-validation. We version data and experiments via MLflow/DVC. The outcome is a model that has passed all acceptance tests.

We wrap the model in a production-ready service: REST/gRPC API, containerization (Docker/K8s), inference server setup (vLLM, Triton, TorchServe). We integrate with your systems and configure the CI/CD pipeline.

We set up production model metric monitoring: data drift, prediction drift, latency, throughput. Automatic alerts on quality degradation. A/B testing of new model versions with canary deployment.

We retrain models on fresh data, add new features and scale infrastructure. We transfer knowledge to your team: documentation, runbooks, training. We provide SLA-backed operational support.