Connect and discover
- Source
- QueryExecution
- SavedQuery
Project-scoped Sources and immutable files enter bounded metadata, preview and read-only exploration paths.
Explore data operationsGoverned data operations & Assurance
From raw data to a decision backed by evidence.
advanexus connects heterogeneous sources, files, quality controls, versioned data assets, governed analytics, and isolated Python work for United States organizations in one tenant/project-scoped operating model. Assurance links supported steps into a permission-aware operational story without replacing the data stack already in use.
- Heterogeneous sources
- Versioned data assets
- Permission-aware execution
- Evidence-linked operations
Not another warehouse, scheduler, catalog, BI tool or notebook. A governed operating model across them.
A controlled path from source to an evidence-backed outcome
Assurance scope
Known outcome
Evidence strength stays source-aware.
Assurance scope
- Actor
- Tenant / Project
- Version
- Policy
- Permission / RLS
- Run
- Outcome
- Integrity
Synthetic scenario · public capability boundaries are detailed below.
Source / File
Synthetic scenario · public capability boundaries are detailed below.
Platform in one view
See the whole path. Then go as deep as the decision requires.
Follow how Advanexus connects heterogeneous sources, controlled processing, quality, analytics, Python work, delivery, and Assurance from source to evidence. Use the overview to choose a stage, then explore the exact capabilities, controls, and boundaries documented below. The current capability contract remains authoritative for implemented support.
Review current capability status
Your stack can execute. Can it explain and prove the result?
A critical number may cross databases, files, SQL, transformations, quality rules, dashboards, notebooks and tickets before anyone acts on it. At each hand-off, ownership, versions, permission context, retries and supporting evidence can split into separate systems.
Ownership gets lost between teams.
Quality becomes detached from the run it evaluated.
Permission and RLS context disappears from delivery.
Retry and partial success collapse into one final label.
Connected operating model
A connected platform, not a collection of disconnected features.
Six capability planes move work from approved inputs to controlled outcomes. Each plane has an explicit input, canonical owner and output; the next plane consumes a governed reference rather than an implied hand-off.
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Build and validate
- FileVersion
- TableVersion
- QualityRun
- PipelineRun
Inspected inputs become immutable managed-table versions; QualityRuns and PipelineRuns retain their own outcomes.
Explore quality controls -
Govern and analyze
- DatasetVersion
- ReportVersion
- AnalyticsRun
DatasetVersion supplies an accepted data contract; ReportVersion and AnalyticsRun preserve analytical intent and execution context.
Explore controlled analytics -
Extend with Python
- NotebookVersion
- Environment
- CellRun
A known notebook revision and immutable environment bind controlled CellRuns to a project-scoped runtime.
Explore ANPy -
Understand and orchestrate
- Session
- Turn
- ModelRun
- Action
Intelligence uses server-owned context to explain, propose and invoke only registered, policy-checked actions.
Explore Intelligence -
Operate and prove
- Finding
- Case
- IntegrityRun
- EvidencePackage
Assurance projects supported canonical evidence into scoped investigation, integrity and package workflows.
Explore Assurance
Advanexus Assurance
From any supported outcome to the evidence behind it.
Advanexus Assurance connects permitted actors, scopes, versions, runs, relationships, findings and artifacts into a permission-aware tenant-wide operational story. It distinguishes verified, unverified, pending, legacy and unavailable evidence without inventing history.
Investigate
Evidence Explorer, Entity 360, User 360, Execution Story and a bounded Evidence Graph move from signal to supporting record.
Evaluate
Explicit reproducibility criteria and source-aware integrity expose both proof strength and evidence gaps.
Act and package
Findings, Cases and permission-checked Evidence Packages preserve controlled human and export workflows.
AI operates inside the same permissions, policies and evidence model.
Advanexus Intelligence maintains owner-private goals and turns while rebuilding actor, tenant, project, permission, RLS and object context on the server. A model may explain or propose; deterministic services validate; policy and people authorize registered state-changing actions; canonical results and evidence show what actually happened.
Goal → Grounded context → Proposal → Validation → Confirmation or approval → Registered action → Evidence
Synthetic scenario
One critical reporting flow. One connected operational story.
The synthetic walkthrough below is an operator journey, not one automatic workflow. Each illustrated hand-off is a separate governed decision and keeps the canonical run or version reference produced by the previous module.
A project-scoped Source or immutable FileVersion establishes identity, scope and the exact input reference.
- Register the input
A project-scoped Source or immutable FileVersion establishes identity, scope and the exact input reference.
- Inspect and publish
Bounded preflight validates content; a successful transform promotes a new immutable table version behind a stable Sandbox alias.
- Apply the quality gate
A supported assertion produces its own QualityRun and bounded failure finding; preview alone is not persisted evidence.
- Accept a DatasetVersion
SQL, source lineage, columns and Dataset-owned metadata become an immutable data-contract version after explicit promotion.
- Save and execute a ReportVersion
An exact DatasetVersion binding, server-owned RLS and runtime filters produce a separate AnalyticsRun with diagnostics and artifacts.
- Continue in ANPy when needed
A saved notebook revision and ready immutable environment bind controlled Python cells; this step is optional, not an implicit report action.
- Investigate and package
Assurance follows projected canonical references, exposes missing links and lets an authorized user request a bounded technical Evidence Package.
Success remains specific
Each completed module retains its own terminal state, identity and output; completion is never inferred from the next screen.
Partial remains partial
A report binding, dashboard widget, transfer or master step can succeed while another fails; the platform preserves both outcomes.
Failure remains actionable
Diagnostics, retries, correlation and evidence gaps remain visible without claiming that every attempted operation can be rolled back.
Works with the existing stack
Use the tools you already trust. Add the control they do not share.
Advanexus complements established systems by connecting responsibility and evidence across their boundaries. It does not relabel every neighboring category as a feature of the platform.
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Warehouse or lakehouse
- What it does well
- Storage and compute
- What advanexus adds across the stack
- Cross-system scope, control and evidence around supported use.
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Orchestrator
- What it does well
- Schedules and dependencies
- What advanexus adds across the stack
- Business context, versions, quality, approvals and evidence.
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Catalog or governance tool
- What it does well
- Metadata and ownership
- What advanexus adds across the stack
- Runtime enforcement and execution-linked outcomes.
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Observability platform
- What it does well
- Detection and diagnosis
- What advanexus adds across the stack
- Preventive control, formal findings, cases and evidence delivery.
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BI or notebook
- What it does well
- Analysis and presentation
- What advanexus adds across the stack
- Governed input, version, permission, result and evidence context.
Heterogeneous systems. Explicit capabilities.
Seventeen connector contracts across eight capability families expose only the operations a source can safely support. Discovery, preview, query, transfer, write, delete, quality and direct Analytics availability vary by source, driver and deployment.
- Relational — PostgreSQL, MySQL, MariaDB, SQL Server, Oracle and Db2
- Warehouse — SAP HANA, Snowflake and Databricks SQL
- Key-value — DynamoDB and Redis
- Document and wide-column — MongoDB and Cassandra
- Search — Elasticsearch and OpenSearch
- Graph and enterprise SaaS — Neo4j and Salesforce
Paths through the platform
Choose the path by responsibility or operating context.
The same evidence contract answers different questions. Roles begin with the decision they own; industries begin with the flow and control boundary they must operate.
Executive and risk leadership
See control coverage, outcome trends and unresolved evidence gaps without flattening operational detail into a confidence score.
Data engineering and platform teams
Connect, inspect, transform, validate and operate runs while preserving exact versions and failure state.
Analytics and data science
Accept a DatasetVersion, pin ReportVersions, inspect AnalyticsRuns and continue with controlled Python when appropriate.
Audit, compliance and investigation
Move from an outcome or signal to scoped events, entities, relations, integrity and authorized evidence packages.
Regulated and public services
Start with reporting, exchange and review obligations where permission, quality, version and evidence context cannot be separated.
Data modernization programs
Introduce explicit hand-offs and acceptance around existing systems instead of making platform replacement the first requirement.
Trust is a product behavior, not a marketing badge.
Server-owned scope, tenant and project boundaries, explicit versions, source-aware integrity, bounded operations and visible capability status make trust inspectable instead of implied. Identity sessions, SAML policy, RLS, sensitive-data handling and deployment acceptance retain their own exact boundaries.
Scope
Tenant and Project contexts are resolved server-side and revalidated at protected boundaries.
Version and execution
Immutable contracts and persisted runs make known state inspectable; mutable definitions and live rows remain explicitly identified.
Evidence and integrity
Canonical source strength determines integrity; projection presence never upgrades unsupported evidence to verified.
Deployment
Production sign-off belongs to an exact revision, immutable images, target infrastructure and recorded acceptance—not a website badge.
Explore advanexus
One connected map of the public Advanexus system.
Move from platform capability to operating outcome, trust boundary and practical guidance without losing your place.
Application Open platform Opens the Advanexus application
Next step
Bring one critical data flow. Leave with a controlled operating model.
Start with the systems, rules, owners, delivery obligations and evidence requirements that matter most. The first conversation is about operational reality, not a generic feature tour.
Discuss a critical flow