Operations intelligence workspace

See the whole operation.
Govern every move.

NAXZOR brings tenant-scoped operations, KPIs, risk, teams, projects, AI-assisted insights and reports into one operating picture, so teams can take governed action.

Illustrative interface below. No customer data shown.

  • Tenant-scoped workspace
  • Validated CSV imports
  • Tenant-scoped REST API
  • Role-controlled access
  • Audit logging
  • AI-assisted insights
  • Reviewed Adaptive ML
From fragments to one picture

Operations live in pieces. Decisions need one picture.

Bring supported operational data into a single customer workspace, scoped to your organization. Set up the tenant, bring records in through validated CSV imports or the REST API, read them together, then interpret and act through role-controlled workflows.

OPERATIONSKPISRISKSTEAMSPROJECTSREPORTSNOTESOnePICTURE
  • Fragmented data silos

    One operational picture

    Pull supported operational data into a single customer workspace.

  • Delayed risk visibility

    Structured risk intelligence

    Search, filter, score, and prioritize risks with status and severity context.

  • Reactive decision-making

    Documented mitigation

    Turn risk plans into owned action items with target dates and progress.

  • Unclear issue ownership

    Clear accountability

    Owners, notes, and timeline events stay visible in one record.

  • Manual status reporting

    Reports and activity history

    Create reports, schedule reporting workflows, and review operational activity.

  • Scattered operational context

    Connected executive visibility

    Dashboards, KPIs, projects, departments, and notifications in one workspace.

Product intelligence

Six surfaces. One set of records.

Every interface below is an illustrative local mock. It shows layout only, with dashes in place of values.

01 / Dashboard

Your organization's operating position

KPIs, open work, risks, and activity sit over the same records people work in.

Adaptive Intelligence

A system that measures itself before anyone trusts it.

Adaptive ML works only from each tenant's eligible history. Insufficient evidence remains insufficient. It is separate from the LLM that explains findings, and from the deterministic, role-controlled actions people take.

ObserveSTAGE 01 / 07ObserveBaselineDetectPredictRecommendMeasureLearn

  • Tenant-specific baselines

    Built from an organization's own eligible operating windows, only when the evidence is sufficient.

  • Deviations and anomalies

    Unusual movement against a validated baseline is surfaced for review rather than silently averaged away.

  • Forecast candidates

    Daily event forecasts, next-day delay-risk and time-to-completion candidates, evaluated against later holdouts.

  • Prospective measured outcomes

    Issued estimates are compared with what was later observed.

  • Drift and recalibration

    Drift is a review signal. Recalibration is a proposal that needs human review and separate approval.

  • Human review

    People review signals and approve any change. Domain declaration is context, not proof, and never selects a model.

What it is not

No training across customers. Histories are not pooled.

No autonomous model promotion.

No automatic retraining.

No ML-triggered workflows.

No guaranteed outcomes. Forecasts are candidates under evaluation.

No unrestricted automation of any kind.

Domain context

Declare a domain. Same method underneath.

Declared as logistics, results are read in terms of delivery and movement records. Domain is context, not proof, and never selects a model.

Delivery eventsDwell windowsRoute completionsTenant baselineSAME METHOD
Risk Intelligence

The early signal, then the owned action.

Risk and anomaly signals go to review. People log, score, plan and resolve through a supported workflow.

VALIDATED TENANT BASELINEREVIEWDEVIATION SURFACEDSENT TO PEOPLE
  1. 01

    Logged

    A risk is recorded with a category, description, owner, and workflow status.

  2. 02

    Scored

    The detail view presents score, level, probability, impact, and priority.

  3. 03

    Contextualized

    Linked department, project, operation, and report context appears when those relationships exist.

  4. 04

    Planned

    Document a mitigation plan and create action items with an owner and target date.

  5. 05

    Worked

    Complete mitigation tasks, add notes, and review the risk timeline as work progresses.

  6. 06

    Resolved

    Move the risk through the supported status workflow with resolution history kept visible.

Governed action

Three layers. Never blurred.

ADAPTIVE ML

Predicts

Candidates and signals from tenant history, evaluated against later outcomes. Shadow mode. Not guarantees.

LLM

Explains

A qualitative reading on request. Evidence stays server-authoritative. Read-only; it triggers nothing.

DETERMINISTIC + HUMAN

Acts

  • Role-controlled access. Organization-scoped permissions decide who sees and changes what.
  • Defined workflows. Risk status, mitigation tasks, and approvals follow explicit states that people move forward.
  • Controlled internal tasks. Automation is limited to defined internal task actions under role control.
  • Audit history. Activity and administrative changes are logged, keeping who did what visible.
NO DIRECT ML TRIGGERML signalEvidence shownRole checkHuman approvalInternal task
Reports and decisions

From the picture to the record.

  • 01

    What is the position?

    Dashboard and KPIs read against the same records.

  • 02

    What needs attention?

    Risks, anomalies and signals go to review with their evidence.

  • 03

    Who owns the next step?

    Mitigation tasks carry an owner and a target date.

  • 04

    What was decided?

    Reports and activity history keep the record.

FAQ

Straight answers.

Set up or select your organization, configure your company profile, add departments and teams, and define KPIs. Operational data can then be added through validated CSV imports or the tenant-scoped REST API.

Yes. NAXZOR supports validated CSV imports with mapping, relationship checks, duplicate handling, and review states, plus a tenant-scoped REST API for programmatic data access. Native ERP, CRM, project-management, and cloud-provider connectors are not currently part of the product.

Adaptive ML is designed around eligible operational history from each organization. Baselines require sufficient evidence, and forecast candidates are evaluated against later measured outcomes. Customer histories are not pooled; sparse or incomplete evidence is treated as insufficient.

No. Forecasts are evaluated in shadow mode, drift is a review signal, and recalibration requires human review and separate approval. Models do not automatically promote themselves or trigger workflow actions.

NAXZOR uses Clerk authentication, organization-scoped access controls, tenant isolation, role-controlled administration, protected APIs, and audit logging. Infrastructure and deployment security requirements should be reviewed for the target environment.

The guided setup walks through organization access, company details, departments, teams, and KPIs. After setup, administrators can manage members and invitations and bring in operational data through the supported import and API paths.

See the operating picture.

Request a 14-day trial, or talk to our team about security, deployment and how NAXZOR fits your workflows.