Cybic's Drava platform provides a governed AI automation stack that integrates data pipelines, custom ML models, and autonomous agents for regulated enterprises. It offers workflow orchestration, role‑based access, audit‑ready logging, and supports on‑prem, hybrid, or multi‑cloud deployments to enable secure predictive‑maintenance, asset monitoring, and decision‑support across sectors such as oil & gas, healthcare, and manufacturing.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises in regulated sectors often operate siloed AI initiatives that lack integration with legacy systems, resulting in manual, error‑prone workflows and limited visibility. Compliance requirements further restrict the deployment of generative AI and automation without robust governance, security, and audit capabilities. Consequently, organizations struggle to achieve scalable, data‑driven decision support across complex operational environments.
Solution
Cybic’s Drava platform delivers an end‑to‑end, governed AI automation stack that unifies enterprise data pipelines, custom machine‑learning models, and autonomous AI agents. By embedding AI workflow orchestration, role‑based access controls, and audit‑ready logging directly into the architecture, Drava enables secure execution of predictive‑maintenance, asset‑monitoring, and decision‑support use cases across oil & gas, healthcare, manufacturing, public sector, and retail. The platform supports on‑prem, hybrid, or multi‑cloud deployments, allowing organizations to scale generative‑AI copilots and LLM‑powered knowledge systems while remaining compliant with SOC 2, HIPAA, ISO, GDPR, and industry‑specific regulations. Clients access real‑time analytics and AI‑generated insights through a web dashboard or API, turning previously isolated models into actionable business outcomes.
Target Audience
Primary customers are large enterprises in regulated industries—oil & gas, healthcare, manufacturing, government, and retail—that require secure, governed AI automation to modernize legacy operations and improve decision‑making.
Features
- Integrated data‑ingestion layer that normalizes structured and unstructured sources into AI‑ready pipelines for LLM fine‑tuning and ML model training.
- AI workflow orchestration engine that coordinates rule‑based RPA steps with LLM‑driven decision logic and autonomous agents.
- Agentic automation framework enabling goal‑based AI agents to execute end‑to‑end processes, negotiate tasks, and adapt in real time.
- Built‑in governance stack: RBAC, end‑to‑end encryption, immutable audit trails, and compliance templates for SOC 2, HIPAA, ISO, GDPR.
- Scalable deployment model supporting on‑prem, hybrid, and multi‑cloud environments with containerized microservices and Kubernetes orchestration.
- Real‑time monitoring and MLOps pipelines for model versioning, automated retraining, and performance dashboards.
- Extensible API layer (REST/GraphQL) and FHIR‑compatible connectors for seamless integration with ERP, EHR, SCADA, and other legacy systems.