Ertas

Fine-tuning a model on your product documentation should not require provisioning GPUs or writing training scripts. Ertas AI addresses that gap with a canvas-based workflow: you bring your domain content, configure a training run through a visual interface, and export the resulting model for deployment. The intended output is a model you can embed in a mobile app for offline inference or serve per-customer inside a SaaS product — without touching cloud inference APIs after the model is exported.

The core differentiator Ertas targets is cost reduction through local model execution. Once you export a fine-tuned model, inference runs on-device or on your own infrastructure, eliminating per-token API fees from providers like OpenAI. For solopreneurs or agencies shipping multiple client projects, that cost ceiling is the practical reason to go through fine-tuning at all rather than wrapping a hosted API.

Where Ertas fits: a single-tenant chatbot trained on a product knowledge base, an offline mobile feature that needs to work without a network connection, or an agency delivering a white-labeled AI feature to a client who wants something that answers their domain correctly out of the box. Where it breaks: the absence of an API means you cannot automate model delivery or trigger retraining from an external system — every update is a manual operation. No self-hosted option means training data leaves your environment, which disqualifies the platform for healthcare, legal, or financial data before the conversation starts. Multi-tenant SaaS use cases are listed as a target, but managing per-customer model versions without programmatic tooling creates operational overhead that grows with each new tenant.