Synnada provides an AI infrastructure platform that enables developers to build, deploy, and run persistent intelligent agents at production scale. Its stack includes the Agentia runtime for continuous agent execution, the Mithril compiler for optimized model binaries, and the Tenet multi‑cloud layer for reliable, long‑running decision systems, all integrated with Apache DataFusion for high‑performance data access.
Funding
$2.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
SSFounders
Product
Problem
Existing AI infrastructure is optimized for batch data pipelines and short-lived services, making it difficult to deploy and maintain large-scale, continuously operating intelligent agents. This limits the reliability and scalability of production decision systems that require persistent, coordinated agent behavior.
Solution
Synnada offers an AI infrastructure platform designed for the “agentic economy,” enabling developers to build, deploy, and run persistent intelligent agents at production scale. The platform combines a runtime that treats agents as first‑class code entities, a production‑grade ML compiler, and multi‑cloud deployment tools to ensure agents can reason, act, and coordinate continuously across datasets and environments. By focusing on correctness, efficiency, and long‑term operability, Synnada turns prototype agent logic into reliable services that can run for years without degradation. The stack integrates with open‑source projects such as Apache DataFusion, providing a familiar data‑processing foundation while extending it for agent‑native workloads.
Target Audience
Primary customers are large digital enterprises and data‑science teams that need production‑grade, continuously operating AI agents for automated decision systems, as well as AI platform providers seeking agent‑native infrastructure.
Features
- Agentia runtime that executes persistent agents with built‑in code isolation and coordination primitives
- Mithril ML compiler that transforms model code into optimized, production‑ready binaries
- Tenet multi‑cloud deployment layer for seamless scaling of agent workloads across providers
- Integration with Apache DataFusion for high‑performance, SQL‑based data access within agent pipelines
- Built‑in correctness and efficiency checks to prevent runtime failures in long‑running decision loops
- Support for continuous data ingestion and real‑time decision making without manual intervention