See Belvedere, your Agentic Data Manager, in action.
Belvedere's AI agents catalog your data estate, learn what it means, and keep it governed and current. Ask for what you need in plain English and Belvedere delivers it: discovered, connected, validated, and ready to act on. Bring one data problem your team handles by hand and we'll show Belvedere take it end to end.
One of your workflows, mapped
Describe a data flow you run today and we'll sketch how Belvedere would build, govern, and monitor it
A scenario close to your stack
We run the demo on the problem shape nearest yours: migration, cataloging, fusion, or analytics build-out
A concrete next test
You leave with a specific first pipeline to try and what it would need from your side
Get a demo of Belvedere
A 30-minute working session on a live environment. We'll reach out within one business day to schedule.
A live environment. Not slides.
Every demo runs on a working Belvedere environment: a governed estate with cataloged sources, data contracts, and end-to-end lineage over demo data. We hand it a requirement in plain English, the way your analysts would, and you watch it find governed context, propose the pipeline, and generate deterministic code you can read.
Nothing deploys until a reviewer approves it. That review moment is the heart of the demo: the proposed graph, the evidence behind each node, and the validation state.
- A plain-English request turned into a working pipeline, live
- The proposed graph inspected node by node before anything runs
- Contracts, lineage, and validation state behind every step
- Drift detection and self-healing with a full audit trail
HR Records
Employee records including department, hire date, role, and demographic attributes.
Department Hierarchy
Organizational structure and department metadata — reporting lines, cost centers, and division mappings.
Termination Logs
Historical termination records with exit dates, reasons, and department context.
Discover sources
Belvedere's catalog is searched for matching datasets — ranking by freshness, quality score, and access permissions.
Generate transforms
Pipeline code is auto-generated to join, filter, and aggregate the discovered sources into the requested output shape.
Validate & publish
Generated pipeline runs validation checks — schema compatibility, row-count assertions, and governance controls verified.
Generate Transforms
Auto-generate pipeline code to join, filter, and aggregate discovered sources.
Pipeline code is auto-generated based on the analyst's natural-language request. The transform generator identifies the optimal join paths between discovered sources, applies appropriate filters (in this case the Q4 date range), and builds aggregation logic for the requested metrics. The generated code is idiomatic, tested, and includes inline documentation explaining each transformation step. A year-over-year comparison is added via a self-join on prior-year data with a calculated YoY delta column. The complete pipeline is ready for validation before publishing to the analyst dashboard.
Markdown supported. Type @ to link data sources. Ctrl+B bold. Ctrl+I italic.
Show me attrition rates by department for Q4
I found 3 matching datasets: HR records, department hierarchy, and termination logs. Building a pipeline to join and aggregate by department with Q4 date filter.
Can you add a year-over-year comparison?
Done — I've added a self-join on prior year data with a calculated YoY delta column. The pipeline now includes both current and historical comparisons.
HR Records
Department Hierarchy
Termination Logs
Discover sources
Belvedere's catalog is searched for matching datasets — ranking by freshness, quality score, and access permissions.
Generate transforms
Pipeline code is auto-generated to join, filter, and aggregate the discovered sources into the requested output shape.
Validate & publish
Generated pipeline runs validation checks — schema compatibility, row-count assertions, and governance controls verified.
Show me attrition rates by department for Q4
I found 3 matching datasets: HR records, department hierarchy, and termination logs. Building a pipeline to join and aggregate by department with Q4 date filter.
Thirty minutes. All three arms, working.
Belvedere operates as three intelligent arms: knowing, doing, and watching. The demo puts each one in front of you on a live environment, tailored to the systems and problems you bring.
Step 01
Your estate, mapped
You describe the systems you run and where pipeline work piles up. We pull up the closest scenario, whether that's migration, cataloging, fusion, or analytics build-out, so the rest of the demo happens on a problem shaped like yours.
Step 02
The Knowledge Arm: a catalog that built itself
We tour a governed estate Belvedere discovered on its own: sources, inferred schema, classification markings, data contracts, and end-to-end lineage. This is the context every pipeline starts from, and it's the first thing most teams want for their own environment.
Step 03
The Workflow Arm: request to running pipeline
We hand Belvedere a requirement in plain English and watch it derive contracts, reason through the system model, and propose a pipeline graph backed by deterministic code. You inspect every node and the evidence behind it. Nothing deploys until a reviewer approves.
Step 04
The Observability Arm: break it on purpose
We change a schema upstream and watch Belvedere catch the drift, diagnose the root cause, and propose the repair, with the audit trail that follows every action, automatic or approved.
Step 05
Your questions
Architecture, security model, and deployment options, from cloud to on-prem to air-gapped. Plus how Belvedere runs on the stack you already own.
Not ready for a call? Read the platform overview or try the Data Contract Builder.