Data sovereignty is the constraint, not the afterthought. We build encrypted data infrastructure designed so your AI workloads sit inside the controls your auditors already expect.
The enterprise AI adoption paradox is straightforward: the organisations that stand to gain the most from AI — financial institutions, healthcare providers, legal firms, government agencies — are precisely the ones whose regulatory obligations make adoption most perilous. Data-protection fines under the GDPR can reach 4% of global annual turnover or €20 million, whichever is higher, so the standard approach of piping sensitive data to third-party cloud APIs is rarely defensible. Secure data pipelines are the engineered foundation that resolves this tension.
On-premise AI deployment represents the most sovereign tier of our security architecture. We deploy high-performance open-weight foundation models, or domain-specific fine-tuned variants of them, entirely within your data centre or private cloud enclave. No inference data traverses public networks and no inference requests reach external API endpoints. The models run on your hardware, governed by your network policies, monitored by your security operations centre. Where a data-protection obligation, a health-data restriction or a classification rule means the data cannot leave your perimeter at all, this stops being optional hardening and becomes the starting point.
For hybrid deployments where cloud APIs are utilized for non-sensitive workloads, our encrypted data pipeline architecture implements defense-in-depth. Intelligent PII detection layers — powered by named entity recognition models trained on your specific data taxonomy — automatically identify and mask personally identifiable information before any request exits your perimeter. National ID numbers, health record identifiers, financial account details and custom-defined sensitive fields are scrubbed, replaced with synthetic tokens, and reconstructed only after the response returns to your secured environment. Named entity recognition is probabilistic, so the cloud model sees masked data and a residual-risk review is part of the design, not an optional extra.
Control design is not a checkbox applied after deployment — it governs every architectural decision from day one. Complete audit trails log every AI interaction: which model processed the request, what data was accessed, which user initiated the query, and what response was generated. Role-Based Access Control restricts AI agents to the data the requesting user is authorised to access. Data retention policies are enforced programmatically. Encryption at rest and in transit uses AES-256 and TLS 1.3 as non-negotiable minimums. These are the artefacts your auditors ask for; the architecture is built so you can produce them.
On training: in an on-premise deployment no inference data leaves your perimeter, so the question does not arise. In a hybrid deployment we use provider tiers whose terms exclude training on customer inputs, and mask PII before egress — confirm those terms against the provider's current contract, because they are the provider's to change, not ours.
One thing we will not blur: QQuantum.ai holds no security certifications or attestations of any kind. Certification is yours to hold. What we do is design and build so that your auditors can evidence the controls they are testing — access control, audit logging, data residency, retention and encryption — and so that the AI layer does not become the exception in an otherwise well-governed estate.
Yes. We deploy high-performance open-weight models entirely within your data centre or private cloud enclave. No data leaves your network perimeter and no inference requests reach external APIs. The models run on your infrastructure, governed by your security policies. This is the usual deployment model where data sovereignty is a hard requirement — under the GDPR, under health or financial-sector rules, or under government classification mandates.
Our intelligent PII detection layers use named entity recognition models trained on your specific data taxonomy to automatically identify and mask personally identifiable information before any request exits your security perimeter. Sensitive fields are replaced with synthetic tokens during processing and reconstructed only after responses return to your secured environment. The external model never sees real PII data.
Controls rather than badges: least-privilege access, audit logging on every read and every action, data residency you choose, retention that actually expires, and encryption in transit and at rest. Where the law applies we design to it directly — the GDPR, the EU AI Act, NIS2, and DORA in financial services — because those are obligations rather than certifications. Where your sector requires a certification or attestation, we work to whatever control set your compliance team gives us. To be explicit: QQuantum.ai holds no security certifications, so nothing we build substitutes for your own audit; it is designed to make that audit evidenceable.
In an on-premise deployment, no inference data leaves your perimeter, so there is nothing for anyone to train on. In a hybrid deployment we use commercial provider tiers whose terms exclude training on customer inputs, and mask PII before egress. Those terms belong to the provider and can change, so we name the tier in the architecture document and recommend you confirm it against the provider's current terms at the time of writing.
Yes. Hybrid architectures route sensitive workloads to on-premise or private-cloud models while non-sensitive tasks use commercial cloud APIs. A PII masking proxy sits at the boundary, scrubbing and tokenizing sensitive fields before any request leaves your perimeter, so you get cloud economics without compromising compliance.
Data is encrypted at rest with AES-256 and in transit with TLS 1.3 as non-negotiable minimums. Vector stores are siloed with Role-Based Access Control, keys are managed through your existing KMS or HSM infrastructure, and every decryption event is captured in the audit trail.
Add AI capability without weakening your security posture. Bring your control framework and we will design the pipeline around it.
30-minute technical call · no deck
What we built, what stage it is at, and a link to the live thing wherever there is one.
yourOS is an operating system with an AI at its heart. You talk to it like a person, out loud or typed. It makes your calls, plans your days and works on your projects with a crew of agents, on your own private server. Our own product, built from the first commit; the waitlist is open.
Website, backend and app assets for Sonora, a free AI sound-therapy app on iOS and Android published by Cognitive AI.
Brand, website, prototype, apps on five platforms, the AI backend and the algorithm-intelligence pipeline for Podswap, a social growth engine that runs without a human in the loop.
In closed beta: Roxy, a private AI assistant with permanent memory running on infrastructure the user owns. Brand, site, product, backend and conversational design.
Live at coherence.com: brand identity generated with AI agents we directed, the motion system and website for the group behind Sonora, AboutAI, WakeUp and MudShadows. Agent work remains in build.
Live at domainbooth.com for DomainBooth, a premium domain brokerage: a new animated mark, every page redesigned and rebuilt with designed mobile layouts, and a custom CMS for its sales record, featured names, press and enquiries.
Apps, platforms and campaigns for these teams, long before the AI work. Every one is written up on the work page — what we did, and where our part ended.
