This isn't a spec sheet. Where your data lives, how your environment is separated, how long anything is retained, which model providers are called and under what terms: all of that is decided per product and per engagement, written down in that project's own security documentation, and reviewable by your team before anything is signed. Precision there is worth more than a generic promise here.
— Hugo Paquet, Co-founder & Chief Technology Officer, Quantumize AI
Separation is enforced in the data layer
Not in application code. Tenant identity travels from authentication through every query path to storage, so a bug in a request handler can't return someone else's records. Application-level filtering is a convenience, never the boundary.
No claim without a retrieved source
Every answer an agent produces traces to a document, a record, or an extracted fact, with its date attached. A citation that came from the model's memory rather than from retrieval is a defect, not a slightly weaker answer. Whether a professional can rely on the output rests on this more than on anything else, so it's the one thing we won't relax.
Deterministic wherever trust matters
Thresholds, deadlines, eligibility, authority levels, and routing are handled in code, not by model judgment. The model's job is narrowly scoped to language: phrasing a question, extracting a fact, narrating a result that was computed elsewhere. Anything that changes a conclusion is calculated, and the calculation can be read.
Evaluation is a release gate, not a report card
Retrieval quality and answer faithfulness are measured against a versioned set of representative cases, validated by someone who actually knows the domain. The set runs before any prompt, model, or retrieval change ships, because a model upgrade is a behaviour change whether or not anyone intended one.
Humans gate anything load-bearing
Detection and proposal can be automated. Adoption of anything that changes a conclusion is approved by a person. That applies to schema changes, corpus updates, and corrective actions alike. The system can tell you what it thinks should change, and then it waits.
Every run is reconstructible
Tracing is on in every deployed environment: what came in, what was retrieved, which model ran, how long it took, what it cost. When something behaves badly, the answer to "why did it say that" is a record we can pull, not a theory we construct afterward.
The system recommends. People decide.
Where a situation admits several courses of action, the output presents each one with its requirements, trade-offs, and consequences, and then it stops. Agents prepare work and hand it off. They don't make commitments on your behalf, and we don't build them to.
Limits are declared, not papered over
Unanswered questions, thin evidence, and areas outside the system's scope are stated in the output. When retrieval doesn't clear its confidence threshold, the honest answer is that we don't know. Filling that gap fluently is the failure mode that costs you the most.
You own what we build
No lock-in by design. Your data and the structured context built from it are exportable, and capability transfer is part of delivery rather than an upsell. If you leave, you leave with what's yours and a team that can operate it.
We don't claim certifications we don't hold
If a framework or certification matters to your procurement process, you'll get a straight answer about where we are, what's designed to its controls, and what a real audit would still require. Designing to a standard and being audited against it are different things, and we won't blur them.
A substitute for your security review. Bring your questionnaire, your DPA, and your architecture questions. Those get specific answers about the specific product, and they come from me rather than from a sales engineer relaying them.
Contact Quantumize AIWHAT THIS PAGE ISN'T
Technical, architecture, and data-handling questions come to me directly: hugo.paquet@quantumizeai.com. For commercial conversations, hello@quantumizeai.com.