Trusted Data
Operations For
The AI Era

Clear Fracture builds AI-native systems that help complex organizations discover, govern, engineer, and operate all the data their missions depend on. Our flagship platform, Belvedere, turns data needs into deterministic, auditable workflows across the stack you already run.

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Data Engineers & Stewards
Belvedere
BelvedereAgentic Data Manager
Knowledge
Workflow
Observability
Data SourcesS3, APIs, Oracle, SAP
PlatformsSnowflake, Airflow, dbt
LLM ModelsClaude, OpenAI, Llama
ConsumersDashboards, Apps, Analysts
Analytics, Executives, Data Scientists
The Mission

Complex Organizations Need Trusted Data Operations That Can Keep Pace With AI

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.

Source systems multiply. Definitions drift. Tribal knowledge disappears. Pipelines break quietly. Every new AI initiative raises the stakes because bad context now moves faster than ever.

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. Clear Fracture harnesses agentic AI to automate the engineering while preserving the meaning that makes the output trustworthy.

Belvedere
Your Distinguished Steward of Data

Meet Belvedere™, Your Agentic Data Manager

Belvedere is Clear Fracture's flagship platform for trusted data operations. Declare what data you need. Belvedere handles everything behind it: discovery, governance, pipeline generation, observability, and repair across your existing stack.

app.clearfracture.ai/pipelines/logistics-monitoring
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Global Logistics MonitoringUnsaved
Source

Carrier Tracking Systems

Source

Warehouse Management Suite

Source

Customs & Compliance Feeds

Transform

Normalize carrier schemas

Reconcile tracking formats across all carrier platforms into a unified shipment event model with standardized status codes.

Transform

Correlate shipment lifecycle

Link tracking events to warehouse records, building end-to-end shipment timelines with handoff traceability.

Transform

Validate compliance holds

Cross-reference customs declarations against regulatory rules, flagging holds and tariff exceptions in real time.

Transform

Publish to operations layer

Merge correlated and validated streams into a single governed dataset for the global operations dashboard.

Transform

Score delivery risk

Apply ML-driven risk scoring on the published dataset using carrier history, weather, and route congestion signals.

8 nodesDataUnsaved changes
Belvedere AIOnline

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.

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Every Source DiscoveredEvery Pipeline GovernedEvery Change MonitoredEvery Output Auditable

Define The Outcome. Belvedere Handles The Data Operations Behind It.

Belvedere turns intent into governed, production-ready data operations: discovery, contracts, pipelines, observability, and repair. No scripting, no manual plumbing, no vendor-specific lock-in.

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

Latest Articles

Insights from ClearFracture

Belvedere: Your Agentic Data Manager for Mission Operations

Brian FrutcheyBrian Frutchey1 min readProductPublished June 25, 2026

Belvedere uses AI agents to build the data pipeline, not to be the pipeline. The agents handle the design work: profiling sources, drafting transforms, and wiring governance. What they produce is a transparent, repeatable pipeline your team can read, audit, and run cheaply.

This walkthrough shows how that plays out for mission operations, starting from raw, fragmented sources, scoping a data contract, and landing a governed data product with lineage and access controls intact.

Want to see it on your own data? Book a demo and we'll tailor it to your environment.

Read whole article
The Foundation Behind Reliable AI Agent Analytics

The Foundation Behind Reliable AI Agent Analytics

Haydn StraussHaydn Strauss4 min readAnalyticsPublished June 16, 2026

Anthropic recently published how it runs self-service analytics on Claude. One result caught my eye: context + skills took its analytics agent from 21% accuracy to consistently above 95%.

Highlighting that generating SQL is the easy part, the hard part is everything underneath it: canonical datasets, a semantic layer, lineage, maintained skills, and provenance on every answer.

That jump came from the foundation, not a bigger model. With the context right, the agent on top matters much less.

Why Agents Alone Fail

In addition to cost, three context problems keep coming up.

  • Entity ambiguity. "Active users" or "revenue" has several definitions in the warehouse. The agent picks one and writes correct SQL against the wrong data.

  • Staleness. The definition was right when written. Then the pipeline changed and the skill was never updated.

  • Retrieval failure. The right definition exists somewhere, but the agent can't find it, or grabs the wrong version.

Two of these, staleness and retrieval, can't be fixed easily by prompting alone. They need the context to be a versioned, owned asset wired to the pipeline it describes.

Anthropic tried the shortcut of handing the agent the raw query corpus, and accuracy barely moved. As they put it: "The information was there, the agent saw it, and it still didn't use it."

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From Belvedere Pipeline to Flue Agents: A Skeptical Pick of the 2026 World Cup Winner

From Belvedere Pipeline to Flue Agents: A Skeptical Pick of the 2026 World Cup Winner

Haydn StraussHaydn Strauss9 min readAnalysisPublished June 9, 2026

We build AI systems for a living. In production today, that means LangGraph wired into Belvedere: governed pipelines, human approvals, audit trails, provenance, etc.

But for this project, I wanted to kick the tires on something new: Flue, the agent framework from the Astro team. I’ve long been a fan of Astro for web development, so when they released Flue, I wanted to take it for a spin.

Initially I went down the path of adding Flue to background tasks (bug ticket sync, feedback triage, opportunity discovery, etc), but then decided to build something a little more fun, an 'agentic analyst org' powered by Flue.

It's completely free to use if you want to head to https://www.belvederelabs.ai/ and try it out with your data.

Flue Analyst Org | Belvedere Labs: drop a CSV and get an analyst's answer in a couple of minutes.

The data: 3,759 international matches assembled by Belvedere

We did not hand-roll the dataset. We built it in Belvedere, the same governed pipeline system we use in production.

The pipeline ("World Cup International Match Dataset") is eight nodes and seven edges in the canvas that was assembled by connecting our source API and using the following prompt:

"Pull senior men's international football fixtures and per-match statistics from the API-Football REST API, then computes leak-free pre-match features with all available statistics"

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