Prem is building private superintelligence: sovereign AI infrastructure that lets regulated institutions run frontier AI without their data ever leaving their perimeter. The ecosystem spans Fluso, a private AI workspace with compounding memory and 50+ connectors, and the Confidential API: multi-modal private inference (LLMs, vision-language models, voice transcription) secured by verifiable end-to-end encryption.
The engineering bar here is categorically different from typical SaaS: inference runs in volatile memory and vanishes when done, every interaction generates a hardware-signed proof, and deployments must work entirely inside a client's perimeter, including air-gapped environments for government and healthcare.
We worked alongside Prem's team as an engineering partner across their platform ecosystem, building product surfaces and infrastructure where privacy isn't a feature but a hard constraint. Every design decision had to survive the question: does this still work when no data can leave the environment, nothing can persist to disk, and every access must be auditable?
Working at this level of the AI stack (confidential computing, sovereign deployments, verifiable inference) shaped how we now build every AI system: assume the strictest constraint first, and the architecture stays honest.
Prem's client base is exactly the audience most agencies can't serve: an EU RegTech leader processing 2,000+ regulatory documents per quarter that couldn't touch public cloud AI, a North American healthcare provider with 10M+ patient records locked in unstructured PDFs under HIPAA, a Fortune 50 retailer that couldn't send proprietary sales data to a third-party API.
This is where enterprise AI is heading, and it's the environment we've learned to build for.