Building reliable AI systems is complex. Managing state, handling failures, and implementing proper observability takes months of engineering time. Synchronizing agent state across services creates distributed systems challenges that slow down development.
CHALLENGE
Inferable provides deterministic controls that wrap your existing functions and APIs. Build customizable agents without modifying your codebase, while maintaining full control over execution and state management.
Inferable helps the entire engineering team be productive on day one.
AI projects that work with distributed systems, using your existing services often become infrastructure challenges rather than business solutions. Teams get bogged down learning new tools and frameworks instead of delivering features, while business demands for AI capabilities continue to grow.
Inferable offers flexibility without lock-in. Deploy to our Actuonics or self-host for complete control. Our language-agnostic control plane lets your team use existing tools and infrastructure while moving quickly on AI features.
AI systems often require exposing sensitive data to external services, creating compliance risks and complex security boundaries. Existing auth and RBAC systems need modifications, while cloud-based frameworks are difficult to audit.
CHALLENGE
Your tools run entirely within your infrastructure and security boundaries. Optionally, self-host for zero egress of data. Integrate with your existing observability stack and security policies. Maintain complete control over compute resources while supporting custom auth and RBAC systems.
Deploying AI systems that work with your existing infrastructure requires complex networking setups and infrastructure modifications. Opening private subnets for cloud agent access creates security risks, while new components increase operational complexity and on-call burden.
CHALLENGE
Inferable integrates seamlessly with your existing infrastructure stack. Run agent actions within your current environment, whether k8s, bare metal, or cloud. Long-polling architecture eliminates the need for new load balancers or ingress points.
At the core of Inferable is a distributed message queue with at-least-once delivery guarantees. It ensures your AI automations are scalable and reliable
Model human in the loop with a simple API that allows you to pause a function execution for an indeterminate amount of time. Whether the human responds in a few minutes or a few months.
Your functions run on your own infrastructure, LLMs can't do anything your functions don't allow. Since the SDK long-polls for instructions, no need to allow any incoming connections or provision load balancers.
Inferable comes with a built-in ReAct agent that can be used to solve complex problems by reasoning step-by-step, and calling your functions to solve sub-problems.