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Ertas AI helps builders train, own, and ship custom AI models without becoming ML infrastructure experts. Indie developers, startups, and agencies use Ertas to turn their own examples into task-specific models, evaluate them against real product prompts, export portable model artifacts, and deploy them on-device, locally, or to managed GPU infrastructure. The result is lower token dependence, faster responses, better privacy, and AI that is shaped around the product instead of generic prompts. For enterprise teams, Ertas brings the same model-building loop into managed engagements: dataset curation, evaluation design, right-sized model training, safety and failure-mode testing, and deployment-ready custom models that fit into existing AI tool stacks. We work across specialised task models, routed systems of domain experts, and frontier-level quality targets where a POC defines the model size, optimisation path, latency, and cost crossover. Our goal is practical AI ownership: improve quality where generic APIs fall short, reduce cost exposure per API call as internal adoption grows, and keep more of the intelligence inside your own infrastructure.
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United States
- ertas.ai
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