Updated: May 11, 2026

Sovereign AI.
Local. Auditable. Human-governed.

pumm.group documents Nova and NQIS as a private project for local AI systems. The focus is controlled knowledge intake, traceable decisions, technological sovereignty and a clear separation between vision, prototype and evidenced development state.

Overall concept

Nova is the idea. NQIS is the auditable foundation.

The project combines a long-term assistant vision with a technical platform that can be reviewed, limited and improved. Nova describes the desired interaction: understandable, personal, local and source-aware. NQIS describes the technical operating model: knowledge base, API, roles, safety boundaries, audit logs, evidence files and release checks.

The website deliberately avoids inflated claims. It does not present an all-knowing AI, a conscious system or unlimited autonomy. The focus is a local and traceable AI environment where sources, decisions, changes and risks remain visible.

The three layers

Nova
Vision and interaction model for a local assistant that can explain, organize knowledge and support long-term work.
NQIS
Technical platform with grounded chat, memory/knowledge, Auth/RBAC, ops checks, evidence and safety gates.
pumm.group
Private project frame for documentation, book context, development state and public positioning.
Current state

What is already described and technically prepared

NQIS is now significantly more than an idea. The current state covers local runtime, source-grounded answers, protection mechanisms, roles, operational data and evidence files. The stable line remains separated from the development line so changes can be tested in a controlled way.

Local runtime

Operation on owned infrastructure with local API, dashboard/ops views and separated release states.

Knowledge base

Memory, knowledge, sources, chunks and shape checks form the basis for traceable answers.

Grounded chat

Answers are derived from available context. Uncertain current questions are not invented without reliable sources.

Auth/RBAC

Roles such as admin, operator, reader and auditor separate visibility and mutating actions.

Safety layer

Direct tool or shell execution from model output remains blocked. Mutating actions require rules and approvals.

Audit & evidence

Check results, status files, logs and evidence artifacts make development and operation more traceable.

Restore & rollback

Archived states, checksums and restore probes reduce the risk of uncontrolled changes.

Next stage

NQIS is intended to review, score and mark Nova agents, scripts and tools before productive execution.

Why local?

More control, less dependency, better traceability.

Local AI is not an end in itself. The key value is stronger control over data flows, models, sources, logs and system changes. This creates a different operating model than pure cloud chatbots: less black box, more responsibility and more technical accountability.

For Nova, personal assistance is therefore not only conversation, but a controlled environment with source grounding, safety logic and visible limits. For NQIS, every new capability has to fit architecture, tests, roles, evidence and rollback paths.

Communicated as verifiable: local operation, grounded chat, knowledge base, source grounding, Auth/RBAC foundation, audit and evidence files, separated release states.
Deliberately limited: no public AGI claims, no claim of real consciousness, no uncontrolled autonomy, no shell actions from LLM output.

Development principle

New functions are not simply activated; they are checked against existing baselines, safety rules and traceability.

Roadmap

Controlled self-improvement instead of blind automation.

Phase 1

Stable local base

Runtime, dashboard, memory, grounded chat, evidence and separated release states form the foundation.

Phase 2

Agent and script review

NQIS reviews Nova agents before execution for safety, quality, compatibility, testability and governance conformity.

Phase 3

Evaluation-based improvement

Model and system changes are measured against fixed baselines. Regressions should become detectable and reversible.