Pilardin now runs on a multi-model AI infrastructure, meaning the platform can work with several language models at the same time instead of depending on a single provider. Teams can keep their existing model while adding others alongside it, and Niravin handles the routing so users do not have to think about which model sits behind each task.
The practical benefit is choice. A quick summarization job can go to a smaller, faster model, while a complex analysis or a sensitive document can be sent to a larger or on-premise model. Administrators set the rules once: which models are available, which tasks each one handles, and what data may leave the environment. Fallback is built in, so if one model is unavailable or returns an error, Pilardin retries with an approved alternative instead of failing the request.
Cost and performance are easier to manage as well. The infrastructure tracks usage per model, so organizations can see where spending goes and shift workloads when a model becomes slower or more expensive. Mixed environments are supported too: cloud models and locally hosted models can run together, which matters for teams that already invested in on-premise hardware.
- Route tasks to different models by rule or by default
- Automatic fallback when a model is unavailable
- Per-model usage and cost visibility
- Cloud and on-premise models in the same setup
Existing Pilardin workflows do not need to be rebuilt. The multi-model layer sits under the current agents and automations, and administrators can enable additional models from the settings area. Niravin will keep expanding the list of supported models, and organizations can start small, test a second model on a few tasks, and widen its use once results look right.