Evatt AI provides a cloud‑native platform that lets enterprises create, train, and deploy custom predictive models without needing extensive data‑science expertise. The service includes a library of pre‑validated algorithms, automated feature engineering, scalable compute resources, and APIs for integration, plus monitoring tools for drift detection and automated retraining within an enterprise‑grade security framework.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often possess large, heterogeneous datasets but lack the expertise, tooling, and scalable infrastructure required to develop, train, and maintain custom machine‑learning models. This results in delayed insight generation, sub‑optimal decision making, and higher operational costs. Additionally, deploying AI solutions across multiple business units can be fragmented and difficult to govern.
Solution
Evatt AI delivers a cloud‑native platform that abstracts the end‑to‑end machine‑learning workflow, allowing organizations to build, train, and deploy custom predictive models without extensive in‑house data‑science resources. The service provides a library of pre‑validated algorithms optimized for complex data structures, combined with automated feature engineering and hyper‑parameter tuning. Users can provision scalable compute resources on demand, ensuring models run efficiently across varied workloads. Integrated APIs and role‑based access controls enable seamless embedding of AI outputs into existing business applications and dashboards. Continuous monitoring tools detect data drift and performance degradation, prompting automatic retraining or alerts to maintain model reliability. The platform’s compliance‑focused architecture supports secure handling of sensitive data while meeting industry regulations.
Target Audience
The primary customers are data‑driven enterprises—such as manufacturers, financial services firms, and logistics providers—that require scalable predictive analytics across multiple operational domains. It also serves internal data‑science teams and business analysts seeking to operationalize AI without managing underlying infrastructure.
Features
- Pre‑built algorithm repository covering time‑series forecasting, anomaly detection, classification, and regression for high‑dimensional data
- Automated data ingestion pipeline with schema inference, cleansing, and feature extraction
- Distributed training engine that scales horizontally across GPU/CPU clusters with auto‑scaling policies
- RESTful and gRPC APIs for real‑time inference integration into ERP, CRM, and BI systems
- Model versioning, A/B testing framework, and performance dashboards for transparent governance
- Built‑in drift detection and automated retraining scheduler to sustain model accuracy over time
- Enterprise‑grade security: encryption at rest and in transit, IAM integration, and audit logging compliant with GDPR, HIPAA, and SOC 2
- No‑code UI for model configuration, monitoring, and role‑based collaboration among data scientists and business analysts