Asapi AI provides an agentic machine‑learning platform that transforms raw business data into specialized ML agents accessible via versioned REST/GraphQL APIs. The agents handle data ingestion, feature engineering, model selection, continuous retraining, and monitoring, enabling enterprises to embed predictive functions such as lead scoring or churn forecasting directly into workflow and CRM systems without custom serving infrastructure.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often rely on static machine‑learning models that require extensive manual tuning, generate frequent production errors, and demand ongoing maintenance to stay accurate. Integrating these models into existing business workflows is time‑consuming, limiting the speed at which data‑driven decisions can be operationalized.
Solution
Asapi AI delivers an agentic machine‑learning platform that converts raw business data into purpose‑built ML agents. Each agent exposes a specialized API that can be called directly from a workflow engine, CRM, or back‑office system, eliminating the need for custom model serving infrastructure. The agents continuously retrain on new data streams, automatically adapting to drift without manual intervention. By encapsulating feature engineering, model selection, and monitoring within the agent, the platform reduces production error rates and maintenance overhead. Deployment cycles are compressed to a few days, enabling rapid realization of business value such as improved lead scoring, churn prediction, and process automation.
Target Audience
The primary customers are data‑science and engineering teams within mid‑size to large enterprises that need to embed predictive capabilities—such as lead scoring, churn forecasting, or back‑office automation—directly into their operational workflows.
Features
- End‑to‑end data ingestion pipeline with schema inference and automated feature extraction
- Agentic architecture that packages a specialized ML model behind a versioned REST/GraphQL API
- Autonomous retraining engine that triggers model updates based on drift detection metrics
- Built‑in observability dashboard with real‑time performance monitoring, error tracking, and explainability visualizations
- Token‑efficient inference layer optimized for low‑latency, high‑throughput enterprise workloads
- Role‑based access control and audit logging to meet enterprise security and compliance standards
- Plug‑and‑play SDKs for popular workflow orchestration tools (e.g., Airflow, Camunda) and CRM platforms (e.g., Salesforce, HubSpot)