Automated correction→retrain loop for production AI deployments

Automated correction→retrain loop for production AI deployments targets a $4.5B = 150,000 organizations deploying customer-facing AI × $30K ACV for continuous maintenance and governance total addressable market with medium saturation and a year-over-year growth rate of 28% YoY — based on combined MLOps/AIops market estimates from MarketsandMarkets and industry analyst coverage (2023-2025 forecasts).

Key trends driving demand: Model proliferation — companies are deploying many small LLM-based features, increasing operational maintenance load and creating demand for automated upkeep.; Shift to continuous delivery for ML — teams are adopting CI/CD patterns for models, which enables safe, frequent updates that an automated loop can orchestrate.; Regulatory and compliance pressure — auditors demand traceability and verifiable change logs for model updates, making governed automation a must-have rather than a luxury.; Lower-cost tuning and inference — parameter-efficient fine-tuning and cheaper inference reduce the cost barrier to frequent model updates, making automation economically viable..