From Engineering Queues to Instant Insight
Empower analysts and decision-makers with natural-language access to trusted, ready data — no engineering bottlenecks.
Natural Language Access
Describe what you need in plain language — Belvedere builds the pipeline.
80% Less Prep Time
Automates up to 80% of data preparation and validation work.
Empowered Analysts
Frees engineering resources for higher-value work while giving analysts self-service access.
Audit-Ready Results
Every output is deterministic, traceable, and compliant — no black boxes.
Every analytics request requires engineering involvement — creating queues, delays, and frustrated stakeholders. Belvedere gives analysts direct access to trusted, governed data.
Engineering Bottlenecks
Every new analytics request requires engineering involvement — creating queues, delays, and frustrated stakeholders waiting for data access.
Data Trust Gaps
When analysts build their own queries without governance, results are inconsistent, unauditable, and unreliable for critical decisions.
Manual Data Prep
Analysts spend 60–80% of their time finding, cleaning, and preparing data instead of analyzing it. Repetitive prep work kills productivity.
Great questions shouldn’t wait for engineers. Analysts and mission specialists need to explore and act at the speed of curiosity — but traditional analytics workflows create bottlenecks that delay insight by days or weeks.
Belvedere™ turns self-service analytics into reality through natural-language and no-code orchestration. Users describe what they need; Belvedere builds deterministic, validated pipelines that deliver it. Every dataset behind each dashboard is traceable, explainable, and current — so insight is never left to chance.
By automating up to 80% of data preparation and validation, Belvedere frees engineering resources for higher-value work while giving analysts the independence they need to drive decisions faster.
Self-Service Analytics in Action
See how Belvedere turns natural-language requests into governed, validated analytics pipelines.
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.
How It Works
From engineering queues to instant, trusted insight
Your analysts shouldn’t wait weeks for data. Here’s how Belvedere puts trusted, governed data directly in their hands.
Step 01
Describe what you need in plain language
Analysts and decision-makers describe the data they need using natural language — no SQL, no pipeline configuration, no engineering tickets. Belvedere understands the intent and maps it to the right sources.
Natural language • no tickets • immediate response
Step 02
Belvedere finds and validates the right data
The Knowledge Arm identifies relevant sources, resolves definitions, and confirms data quality — so analysts know exactly what they’re working with before a single query runs.
Sources identified • definitions resolved • quality confirmed
Step 03
Generate a governed, deterministic pipeline
Belvedere generates pipeline code that ingests, transforms, and delivers the requested data — with full lineage, compliance rules, and validation built in. Every output is traceable and explainable.
Deterministic • governed • fully traceable
Step 04
Keep data fresh as sources evolve
Pipelines stay current automatically. When schemas change, new sources become available, or definitions shift, Belvedere adapts the pipeline — so dashboards and reports always reflect the latest reality.
Auto-adapting • always current • zero maintenance
Step 05
Deliver trusted data to any analytics tool
Clean, structured, queryable data lands in your team’s tools of choice — dashboards, notebooks, BI platforms, or ML pipelines. Every dataset is audit-ready and traceable back to its source.
Tool-agnostic • audit-ready • analyst-owned
Free Your Analysts from the Queue
See how teams eliminate the engineering bottleneck — analysts get trusted data on demand, no tickets required.
Explore Other Use Cases
Identity Intelligence
Unify fragmented digital footprints with self-healing pipelines.
Learn moreCompliance Automation
Ensure every analytics pipeline meets governance standards.
Learn moreOperational Data Fusion
Combine mission, sensor, and logistics data for real-time awareness.
Learn morePlatform Migration
Port analytics pipelines across platforms without lock-in.
Learn more