Dualithic provides an AI integration platform that turns multimodal and agentic models into composable, production‑grade services for enterprise workflow automation. Its SDK and runtime expose standardized APIs, handling orchestration, scaling, monitoring, and security so engineering teams can embed AI capabilities into existing systems without building custom infrastructure.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises seeking to automate complex business workflows struggle to integrate cutting‑edge multimodal and agentic AI models into reliable, production‑grade systems. Existing solutions often lack composability, robustness, and seamless compatibility with legacy technology stacks, leading to brittle implementations and delayed time‑to‑value.
Solution
Dualithic delivers an AI integration layer that converts advanced multimodal and agentic models into dependable, composable components for automation platforms. By coupling battle‑tested infrastructure foundations with custom AI pipelines, the service abstracts model orchestration, error handling, and scaling concerns. Clients receive a turnkey SDK and runtime that expose standardized APIs, enabling rapid embedding of AI capabilities into existing products and workflows. The platform also provides monitoring, version control, and security controls to ensure continuous, reliable operation in production environments. This allows engineering teams to focus on business outcomes while Dualithic manages the underlying intelligence stack.
Target Audience
Primary customers are product engineering teams and automation architects in mid‑size to large enterprises that need to embed advanced AI functions into their business process platforms. The solution also serves SaaS providers building AI‑enhanced features for their customers.
Features
- Unified framework for integrating vision, language, and multimodal models into a single execution graph
- Agentic orchestration engine that manages autonomous decision loops and tool usage across heterogeneous services
- Composable pipeline architecture with plug‑and‑play modules for preprocessing, inference, and post‑processing
- Production‑grade runtime built on proven cloud‑native and on‑premise foundations (container orchestration, CI/CD hooks)
- SDKs and REST/GraphQL APIs that expose model capabilities as reusable services for any tech stack
- Built‑in observability suite (metrics, tracing, automated anomaly detection) to maintain reliability at scale
- Security and compliance layer with role‑based access, data encryption, and audit logging for enterprise deployments
- Reference integration with products such as Docoo, demonstrating end‑to‑end workflow automation use cases