Drive Clear Decisions
with AI Agents
Understand and operate your data through simple, intelligent interfaces that turn new ideas into production-ready data products and pipelines in minutes, on top of your existing stack. AI speed and intelligence, with deterministic, repeatable results you can trust.
Enterprise decisions get harder when definitions drift and systems grow more complex. AI can fix that, if the results are trustworthy.
Unify the Stack You Already Have
Agents operate across the systems you already run, so complexity drops without a rip-and-replace program.
Preserve Meaning Across Every Layer
Definitions, context, and business rules stay intact through every transformation instead of getting lost in pipeline code.
Make Every Output Provable
Deterministic, auditable, repeatable outputs make AI-generated data products something your teams can actually trust.
Hundreds of source systems. Contradictory definitions, where “revenue” means one thing to Finance and another to Sales. Tribal knowledge locked in the heads of people who already left. Enterprise data ecosystems were built by generations of engineers with different priorities, and every new pipeline is another place where meaning can silently diverge. The result: your team spends more time reconciling what data means than on the decisions it was supposed to enable.
AI agents change the equation, but only when they produce deterministic, auditable, repeatable output that carries context through every transformation layer. No hallucinations. No black boxes. ClearFracture harnesses agentic AI to automate the engineering while preserving the meaning that makes the output trustworthy.
Meet Belvedere™, Your Agentic Data Manager
Belvedere is a data control plane that makes sense of and automates traditional data curation and engineering. Instead of specifying how to build pipelines, you declare what data products you need,and Belvedere's agents handle the rest by operating your existing tools on your behalf. You get the benefits of intelligent agents without the costs or risks of an agent-only architecture or single-vendor selection.
Carrier Tracking Systems
Real-time GPS and status updates from 12 carrier platforms across road, rail, air, and ocean freight networks.
Warehouse Management Suite
Inventory levels, dock schedules, and shipment staging data from 8 regional distribution centers worldwide.
Customs & Compliance Feeds
Import/export declarations, tariff codes, and regulatory hold notices from customs authorities across 14 ports of entry.
Normalize carrier schemas
Reconcile tracking formats across all carrier platforms into a unified shipment event model with standardized status codes.
Correlate shipment lifecycle
Link tracking events to warehouse records, building end-to-end shipment timelines with handoff traceability.
Validate compliance holds
Cross-reference customs declarations against regulatory rules, flagging holds and tariff exceptions in real time.
Publish to operations layer
Merge correlated and validated streams into a single governed dataset for the global operations dashboard.
Score delivery risk
Apply ML-driven risk scoring on the published dataset using carrier history, weather, and route congestion signals.
Load Warehouse Management Suite
Ingest inventory and shipment staging data from all 8 regional distribution centers.
Load data from the Warehouse Management Suite across all 8 regional distribution centers. This step ingests current inventory levels, dock appointment schedules, and shipment staging records, preserving each record’s facility context so downstream correlation can match inbound shipments to their destination warehouse and dock assignment. The output is a standardized warehouse-event stream that will be joined against carrier tracking data to build complete shipment timelines. This is essential because delivery risk scoring requires visibility into warehouse capacity and staging delays — not just carrier GPS positions.
Markdown supported. Type @ to link data sources. Ctrl+B bold. Ctrl+I italic.
How does the risk scoring work?
The pipeline analyzes historical delivery patterns, current weather, and real-time route congestion across all carriers. Each shipment gets a risk score from 0–100, with alerts triggered above 75.
What if a carrier changes their tracking format?
The Schema Agent auto-detects format changes, maps new fields to the canonical model, and updates the normalization step — no manual intervention required. All changes are logged for audit.
Carrier Tracking Systems
Warehouse Management Suite
Customs & Compliance Feeds
Normalize carrier schemas
Reconcile tracking formats across all carrier platforms into a unified shipment event model with standardized status codes.
Correlate shipment lifecycle
Link tracking events to warehouse records, building end-to-end shipment timelines with handoff traceability.
Validate compliance holds
Cross-reference customs declarations against regulatory rules, flagging holds and tariff exceptions in real time.
Publish to operations layer
Merge correlated and validated streams into a single governed dataset for the global operations dashboard.
Score delivery risk
Apply ML-driven risk scoring on the published dataset using carrier history, weather, and route congestion signals.
How does the risk scoring work?
The pipeline analyzes historical delivery patterns, current weather, and real-time route congestion across all carriers. Each shipment gets a risk score from 0–100, with alerts triggered above 75.
Your Experts Should Define the Data Product, Not Hand-Build the Pipeline
Belvedere lets you describe the data products you need in goal-oriented terms. Agents reason through system models to build, test, and deploy them. No scripting, no manual plumbing, no vendor-specific syntax.
Knowledge Arm: Learns Your Landscape
Know where every piece of data lives, what it means, and how different teams define it automatically. Business context persists even when people leave.
Workflow Arm: Acts with Precision
Go from data need to production pipeline in minutes, fully tested, auditable, and running on your existing infrastructure.
Observability Arm: Monitors and Self-Heals
Real-time monitoring catches schema drift, definition divergence, and quality anomalies before they compound downstream. Belvedere diagnoses and repairs before you notice.
How It Works
From scattered data to confident decisions
Your data is everywhere. Your team needs it in one place, clean and ready. Here's how Belvedere makes that happen.
Step 01
Discover and connect everything you have
Scattered data across dozens of systems? Belvedere’s Knowledge Arm discovers where your data exists across CRMs, ERPs, file shares, and APIs, then catalogs the full landscape automatically. It knows what you have before you do.
Sources mapped • systems connected • landscape visible
Step 02
Understand what you’re working with
Before anything moves, Belvedere builds a living knowledge base that captures what every field means, who owns the definition, and how it relates to the rest of your data. When “revenue” means different things to different teams, both definitions are captured and made explicit, so context persists even as people rotate.
Living knowledge base • definitions captured • context preserved
Step 03
Turn messy into trustworthy
Inconsistent formats, duplicate records, missing values: the stuff that makes analysts distrust their own reports. Belvedere’s Workflow Arm configures deterministic, auditable transformation rules that enforce contracts between data producers and consumers with transparent, repeatable results every time, deployed to whatever platform you choose.
Deterministic • auditable • ready to analyze
Step 04
Deploy anywhere without lock-in
Belvedere sits above your execution platforms as the configuration plane. Pipeline logic is portable, transparent code that deploys to Snowflake, Databricks, Airflow, or anywhere else. Switch platforms without recoding.
Consume from any source • deploy to any platform • zero lock-in
Step 05
Ready for decisions and ready to scale
Your pipelines deliver clean, structured, queryable data with the context that makes it trustworthy for your analysts, dashboards, ML models, and AI agents. As your data grows, Belvedere’s configuration plane scales with compute, not manpower.
Structured • queryable • ready to scale
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