Enterprise GenAI is a fundamentally different problem from building a foundational model. You are not training from scratch. You are fine-tuning a pre-trained model on your organisation's documents, processes, and knowledge base — and then deploying it to users who will trust it implicitly because it exists within a system they already trust.
The failure mode is not dramatic. It is gradual. The model answers 95% of queries correctly, which gives the deployment team confidence. The 5% that fail are in domain-specific, high-stakes contexts — the exact contexts where your users most need accurate answers. A bank's internal compliance copilot that misquotes RBI regulations. A hospital's clinical documentation AI that hallucinates drug names. A law firm's research assistant that cites an overruled precedent.
The enterprises that deploy GenAI successfully are the ones that treat human evaluation as a continuous operational function — not a one-time pre-launch check. They have a standing process for evaluating live model outputs, measuring quality metrics week by week, catching drift before users experience it, and producing curated retraining data based on what they observe in production.
This is exactly what Concave AI's Enterprise GenAI retainer provides. A permanently assigned team of expert evaluators — calibrated to your domain, familiar with your product, aware of your regulatory environment — producing weekly quality reports and monthly retraining batches from your live production data.
