What's included

• Core Technical Deliverables

  Integrated AI system embedded into the client’s product or workflow (API, app, backend, or edge device) Trained / fine-tuned models (or configured third-party models) with versioned artifacts Inference pipelines (preprocessing, model execution, post-processing) APIs or SDK interfaces for consuming AI outputs Performance benchmarks (latency, accuracy, throughput, resource usage)

• Infrastructure & Deployment

  Deployment-ready setup (cloud, on-prem, or edge) CI/CD pipelines for model and service updates Monitoring & logging for AI behavior, errors, and performance Failover / fallback mechanisms for reliability

• Data & Evaluation

  Data handling pipelines (ingestion, validation, storage) Evaluation reports (metrics, test cases, edge cases) Bias, quality, and robustness checks (where applicable)

• Documentation & Handoff

  Technical documentation (architecture, APIs, model details) Runbooks & operational guides Setup and deployment instructions Knowledge transfer / walkthrough sessions