AURA, SAGE, and NOVA are trained exclusively on one industry each. They understand your terminology, regulations, and operational realities - not generalists pretending to be experts. Models do not share your data - only anonymised learnings.
28 agents that take a task instruction and complete the entire job - from accessing your private data, through calling the right domain model, to using specialist tools, to delivering finished output. They do not suggest - they complete.
20 purpose-built tools - document reading, signal detection, standards checking, report generation. Six work independently for any business. Fourteen are agent-bound, included automatically with the agent that uses them.
This is not a policy - it is an architectural constraint. Six independent barriers prevent any customer's data from touching another's.
Your records live in your own isolated section. No shared tables, no cross-customer data access at any level.
Your uploaded documents live in your own private storage area. Invisible to every other customer by architectural design.
Your data is encrypted with keys only your account can use - automatically rotated on a regular schedule.
Every request is verified against your organisation's identity. Any mismatch is rejected immediately with no data exposed.
When AI processes your data, it operates in a context that can only see your data - contexts never bleed into each other.
Every action is permanently recorded and cannot be altered. Show a regulator exactly what happened, at any time, for any period.
In regulated industries a single breach can destroy patient trust, fail a regulatory inspection, or expose student records. Six independent layers means a catastrophic failure would require all six to fail simultaneously - a structural impossibility, not a policy promise.
All data is encrypted in every state. No data travels or is stored unencrypted.
All customer data stored and processed within India by default, supporting DPDP Act compliance.
Fine-grained permissions control which users within your organisation can access which agents, tools, and data.
The Knowledge Communication Layer is how VelorQ's domain models improve over time without compromising customer data.
Imagine a safety signal is detected in pharma data - a pattern suggesting a potential drug interaction. NOVA sees it. Through the KCL Protocol, SAGE learns to watch for the same pattern in clinical hospital settings. No patient names move. No records move. No organisation's data touches another's. Only the anonymised, stripped learning - the pattern itself - travels through the secure layer.
A recurring shape in anonymised data - shared as a detection template, not as data.
An anonymous industry reference that helps models contextualise performance without identifying anyone.
New or amended rules propagated to all relevant models so every customer's outputs remain compliant automatically.
A time-sensitive safety or compliance signal shared within its domain so all relevant models are watching for it.
A validated best practice shared as a template that improves agent outputs across the domain.
An unusual deviation flagged as a signal for other models to watch for, without revealing the source.
A synthesised finding from multiple signals that helps models improve their domain understanding.
Every VelorQ output is compliant with the regulatory frameworks that govern your industry — built in, not bolted on.