Padox.AI provides a scalable orchestration platform for AI agent workflows, enabling teams to monitor inputs, outputs, cost, latency, hallucinations, and retries in real time. The platform supports independent CPU and GPU scaling, policy enforcement, and model-agnostic deployments, all controllable with a single line of code. It is built for mission-critical use cases like ticket triage, clinical scribes, and customer support copilots.
Funding
Funding not disclosed
Founders
Product
Problem
Teams running AI agent workflows at scale struggle to ensure reliability, control costs, and manage infrastructure complexity. Existing orchestration tools, such as Temporal.io or AWS Step Functions, were not designed for AI-specific needs, while home-rolled solutions quickly become fragile under production load. This creates operational overhead and makes it difficult to handle failures, tune resources, and enforce policies across diverse agentic pipelines.
Solution
Padox.AI provides a platform for orchestrating and scaling AI agent workflows with full observability into every input, output, cost, latency, hallucination, and retry. The platform offers deterministic and AI-agentic workflow execution, with built-in resilience to gracefully handle infrastructure failures such as data center outages. It enables independent CPU and GPU auto-scaling in real time, so users can optimize both performance and cost without manual intervention. Teams orchestrate their pipelines with a single line of code, and the platform fits into existing tech stacks with model-agnostic support, meaning users bring their own keys and infrastructure.
Target Audience
Primary customers are engineering and operations teams running mission-critical, AI-agent-based workflows in production, including those building customer support copilots, clinical scribes, ticket triage, revenue automation, and compliance workflows.
Features
- Real-time monitoring of agent inputs, outputs, cost, latency, hallucinations, and retries
- Automatic CPU and GPU scaling independent of each other for cost and performance tuning
- Fault-tolerant execution that handles infrastructure failures gracefully, ensuring every job succeeds
- Model-agnostic design that supports any LLM or model provider using the user's own API keys
- Policy enforcement and cost-control reporting across all workflows
- One-line orchestration code for pipelines, enabling rapid integration into existing systems
- Built-in scheduled tasks and event-driven processing for high-throughput automation