CIYA

CIYA is a deterministic storage and logic engine that sits between your applications and cloud language models. The core value proposition is reliability. When OpenAI, Anthropic, or any other LLM provider goes down, the tools and datasets you have built remain accessible. The platform runs in front of mixed cloud providers and maintains functionality even when connections to those providers fail. For organizations operating in air-gapped, on-premises, or robotic environments, it provides a layer that keeps working regardless of external connectivity status.

The problem it addresses is a fundamental fragility in LLM-dependent applications. When your entire workflow relies on third-party APIs, any outage breaks everything. A provider going down, rate limits kicking in, or network issues on your end can all cause your application to fail completely. CIYA separates what requires live model inference from what can be stored and retrieved deterministically. Your prompts, tools, workflows, and reference data persist locally. When you need novel content generation, that still requires an active connection to a language model. But for anything you have already built, stored, or configured, the system maintains access without depending on external services being online.

The architecture routes requests across multiple backend providers. If one provider fails, others can pick up the load. This multi-provider approach provides redundancy beyond what you get from depending on a single API. Users maintain access to built-in tools and public datasets even during complete LLM disconnections. Content creation capabilities may be restricted when no provider is reachable, but core functionality persists. This makes it viable for secure environments, edge deployments, and any situation where network connectivity is intermittent or restricted. The routing logic handles failover automatically so your application code does not need to manage provider health checks or switching logic.

Interaction happens through a command-driven portal interface. You access documentation with commands like R: guide, file error reports with R: error, and reach support with R: contact. This is clearly built for technical users who are comfortable with that style of interaction rather than drag-and-drop visual builders. The interface assumes you know what you are doing and provides tools accordingly. If you need a polished consumer-friendly experience, this is not that. If you need reliable infrastructure that does what it says, this fits.

The platform supports multiple backend LLM providers simultaneously. You configure which providers to use, and CIYA handles routing, failover, and status monitoring. When one provider experiences issues, traffic can shift to alternatives without your application needing to handle that logic directly. This abstraction layer means your code talks to CIYA rather than to individual provider APIs, which simplifies the integration and adds resilience in one step. You are not locked into any single provider, and adding or removing providers from your configuration does not require changes to your application code.

CIYA is currently in public beta with free account access available. There is no published pricing for post-beta tiers, which means the business model is still being defined. For users exploring the platform now, the free access provides a way to evaluate whether the reliability layer solves a real problem in their workflow without financial commitment. The documentation was updated recently, indicating active maintenance despite the beta status. Because the pricing model is not finalized, it is worth testing now while access is free to understand whether it fits your needs before any costs apply.

Target users include developers and organizations who have experienced the pain of LLM provider outages breaking their applications, enterprise environments that need high availability guarantees, teams operating in restricted connectivity scenarios, and anyone building systems where language model access cannot be a single point of failure. The trade-off is learning a new system and accepting that some functionality, specifically novel content generation, still requires live model access. CIYA does not replace LLMs. It makes your dependence on them more resilient by adding a buffer layer between your application and the providers you rely on.

For air-gapped deployments, on-premises installations, and environments where external network access is prohibited or unreliable, CIYA provides a path to using LLM capabilities without constant connectivity requirements. The deterministic storage means that once you have built something, it stays available regardless of what happens to the external services you originally used to build it. Robotic and edge computing environments where connectivity cannot be guaranteed are specifically mentioned as use cases.