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replaces.nianav OS is a procedure-native operational OS for regulated startups. It replaces the fragmented workflow stack around quality, operations, product, people, evidence, and AI-assisted execution.
In regulated work, an AI agent is not a chatbot. It is an executor inside a controlled operating model. It needs a named version, a validated capability, a permitted scope, a human principal, a custodian, an input contract, a reasoning or execution trace, and a sealed record of what it read and wrote.
That is the difference between “we used AI” and “we can defend how AI was used.” nianav OS lets startups use agentic workflows without losing control of evidence, review, or accountability.
If the value is who does what, in which order, against which rule, with which evidence, it belongs in nianav OS. If the value is a specialist engine doing specialist work, nianav OS connects to it and governs the workflow around it.
The company stops being a patchwork of tools and becomes one operating fabric: procedures, records, role-based views, triggers, connectors, capabilities, and step execution logs all working from the same substrate.
Quality work is not a document library. It is procedures, responsibilities, evidence, reviews, approvals, deviations, records, and recurring proof. That shape is native to nianav OS.
The purchasing manager can feel like they are using an ERP surface, but the underlying step still lives once in the canonical execution log with the records it touches.
nianav OS does not draw the CAD model or run the simulation. It owns the review, sign-off, change record, release evidence, and downstream carry-through.
People records, roles, capabilities, assignments, training debt, and handover are part of the operating system because they determine who can perform which step.
A ticket is a procedure instance with owners, deadlines, evidence, comments, escalations, and closure conditions. nianav OS turns that into structured work instead of another isolated queue.
The workflow comes inside. The specialist calculation or filing engine stays connected. The system knows what was approved, by whom, against which requirement, and where the external output lives.
A QA officer can feel like they are using a QMS. A purchaser can feel like they are using an ERP. A production worker can feel like they are using an MES. An HR manager can feel like they are using an HRIS.
The point is that the step lives once. The same canonical record, same owner, same evidence, same due date, same execution log, and same audit trail can render differently by persona without becoming different data in different systems.
That is how nianav OS makes the company queryable. You can ask what is blocked, who owns it, which rule applies, which evidence exists, what changed, and what needs to happen next without reconstructing the truth from a fragmented stack.
Clinical trials, real-world evidence, design validation, and post-market evidence are how a regulated company shows that its product does what it claims, for the people it is intended to serve, under the conditions it will actually face.
nianav OS makes that evidence visible. What requirement is being tested, what evidence supports it, what record was created, who approved it, what changed, and what still needs follow-up.
nianav OS can serve regulated and non-regulated process-driven companies. For regulated tenants, the compliance machinery activates through the tenant's declared regulatory scope. For non-regulated tenants, the same workflow rigour remains, without claiming framework coverage.
Controlled processes, role-based responsibilities, training awareness, deviations, CAPA, change control, approval trails, and evidence capture.
Design controls, traceability, electronic records, review gates, sign-off logic, and the link between work performed and evidence retained.
Access control, ownership, auditability, data boundaries, policy-to-work mapping, and operational proof for governance reviews.
Project Zero turns one tenant's regulatory scope, org chart, selected archetypes, and customised templates into the first working operating system. The regulatory intelligence layer keeps it current; the AI governance model keeps agentic work inside evidence boundaries.