The bank’s AI team had real momentum: fraud detection flagged anomalies as they happened, the copilot answered policy questions in seconds, and new risk models beat the legacy scorecards. Then came production. Data could not legally leave the bank’s boundary. Cloud bills swung month to month. Latency spiked without warning. And nobody could answer the CFO’s simple question: what does one million tokens actually cost us, and what happens to that number at scale?
Four barriers blocked the path to enterprise AI: prompts, embeddings, and training data sitting on shared hyperscaler infrastructure was unacceptable to legal and risk for customer PII and transaction records; GPU hours, egress, and API charges made every invoice a surprise with no way to model TCO; noisy neighbours on shared GPUs made real-time inference inconsistent; and separate teams had stitched together Kubernetes, GPU operators, vector databases, and model serving with no single view of cost or utilisation. The bank did not need more GPUs — it needed a private AI platform where data, models, and inference stayed inside the trust boundary, with predictable cost and enterprise-grade operations.
