Local‑first
Owned infrastructure, local services and reduced data flows. Cloud services are not required for the concept described on this site.
Technology
A local AI architecture for traceable work.
The technical idea behind Nova/NQIS combines local services, structured knowledge stores, API boundaries, role logic, safety gates and evidence files. The result is an AI environment that can be operated and reviewed, not just queried.
Local-firstEvidence-firstSafety-first
Evidence‑first
Progress remains traceable through status files, checks, logs, checksums, test output and release artifacts.
Safety‑first
No direct shell execution from LLM output, clear STOP_ALL boundaries, role model and controlled approvals.
System model
From knowledge to answer to approval.
NQIS/Nova is designed for controlled workflows. A request should not simply trigger text. Context, sources and system state are reviewed first. Then an answer or plan is created. Risky steps are limited, logged and only become executable through suitable approvals.
1Ingest source or local data
2Structure as memory, chunks, claims and concepts
3Create answer or plan with source grounding
4Apply risk gates and governance rules
5Write audit event and evidence
6Require approval or rollback path where needed
Architecture layers
The platform consists of clearly separated layers.
Interface
Dashboard, chat interface, status pages and API documentation.
API & orchestration
Routes, jobs, ops endpoints and transitions between request, processing and result.
Memory & Knowledge
Knowledge base, chunks, sources, claims, search functions and data-shape checks.
Models & Router
Local LLM usage, routing decisions, parameters, fallback ideas and output quality checks.
Reflection & Decision
Evaluation of completeness, source grounding, uncertainty, risk and next steps.
Safety Layer
STOP_ALL, blocked tool execution, risk gates, approvals and protection against uncontrolled changes.
Ops & Evidence
Health, readiness, logs, metrics, test output, freeze packages, checksums and restore probes.
Governance
Roles, permissions, auditability, documentation and separation between stable baseline and development.
Why separation matters
Many AI demos look convincing as long as only a chat window is visible. A local system needs more than that: knowledge, rights, boundaries, review paths and a traceable operating state. NQIS therefore separates interface, knowledge, decision, execution and audit.
This does not prevent every error, but it makes errors more visible and supports tests, rollbacks, security review and later extensions.
Release and review model
New functions should not move into the stable state uncontrolled. A release state is frozen, checked, supplied with evidence and archived. Development states remain separate until they are reliable enough.
Long term, every model or system change should be measured against a fixed evaluation baseline. Regressions should be detectable and reversible.
Roadmap
Coherent development direction
Today
Grounded, local, reviewable
Memory, knowledge, API, ops, dashboard, Auth/RBAC foundation and evidence history.
Next
Code and agent review
NQIS reviews Nova agents before execution for safety, quality and governance.
Later
Evaluation-based improvement
Measure model and system changes against fixed baselines, detect regressions and keep rollback paths.