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

Trusted By

Department of War
Intelligence Community
Carahsoft
Unfiltered Media Group
TapHere! Technology
Amazon Web Services
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
Live
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 Clear Fracture

Introducing Investigate, Belvedere's New Data Analysis Agent

Introducing Investigate, Belvedere's New Data Analysis Agent

Scott FincherScott Fincher4 min readProductPublished September 15, 2026

A Common Engineering Problem

Maybe you’ve been there: you find yourself given access to a giant, half-documented store of data, and you’re not entirely sure what you even have. Even worse, maybe you’ve never seen most of it before! Your unfortunate task is to find the needle in the haystack on a tight deadline. Connections are fragile, and you can’t risk altering any of the data, but you need answers fast. Where do you begin, and what can you realistically hope to achieve?

A tough situation to be sure, but one that is fairly common. Here at Clear Fracture we found ourselves running into this too. That’s why we developed Investigate, a new feature available in Belvedere. Investigate is a read-only research agent that answers questions about your data, grounded in your catalog.

How Investigate Can Help

To start digging into a task like this one, normally you at least need to have an idea of what databases and tables might be available, along with their schemas. Lineage is likely to play a role too, so that you can understand both upstream and downstream data flows around a particular asset.

With Investigate, you have the luxury of letting an agent figure those things out and document them for you. Instead of starting with SQL queries, you start with plain English: 

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Agent Skill: Idempotent Backfill for Late-Arriving Data

Agent Skill: Idempotent Backfill for Late-Arriving Data

Haydn StraussHaydn Strauss4 min readData EngineeringPublished August 25, 2026

Uber built Apache Hudi to solve a familiar data problem: Records change after they land.

  • A trip gets corrected after it ends.

  • A chargeback arrives weeks later.

Rebuilding a large table to fix a handful of rows is wasteful.

Our free idempotent backfill skill applies some of Hudi's design ideas to late-arriving corrections in data you've already published. Give the current URL to your coding agent and simply tell it to install the skill to try it out.

What we borrowed from Hudi

Uber's lakehouse write-up describes a backfill that reads a fixed snapshot and overwrites only the affected partitions. It does not move the incremental writer's checkpoint. That separation prevents an old repair from disrupting the live pipeline.

Our skill turns a few of its core practices into a six-step, database-independent checklist:

  1. Fix the source snapshot.

  2. Name the partitions, business key, and newest-wins order.

  3. Build each partition twice.

  4. Compare the logical rows.

  5. Stop for approval.

  6. Replace and verify one partition at a time.

The six gates of the idempotent backfill skill

The skill is short on purpose so the agent can use the database's own atomic replacement operation.

Why build it twice

Maxime Beauchemin's essay on functional data engineering gives us a clear rule. Treat a partition as the complete output of a function. The same source snapshot and transform should return the same rows every time.

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From Here to There: How Belvedere™ Maps Your Current State and Builds the Path to Your Target

From Here to There: How Belvedere™ Maps Your Current State and Builds the Path to Your Target

Brian FrutcheyBrian Frutchey6 min readSystem ModelingPublished August 18, 2026

Every capable agent (human or artificial) needs two things before it can act with confidence: a clear picture of where we are, and a robust definition of where we need to go.

That sounds obvious. It is also where most agentic systems quietly fail.

We have poured enormous energy into making models smarter, tools more composable, and orchestration layers more sophisticated. Yet the hard problem is not reasoning in the abstract. It is grounding that reasoning in a faithful account of the present (here) and an unambiguous specification of the intended future (there). Without both, an agent is improvising. With both, it can plan, execute, verify, and explain.

Agents Don't Need Magic. They Need Context with Edges.

An AI agent assisting a mission, a business process, or a data pipeline is only as good as the situation it can see and the outcome it is asked to produce. "Current state" is not a chat transcript. "Goal" is not a vague aspiration. Both must be detailed enough that another competent actor (software or human) could inspect them, challenge them, and act on them.

That means capturing:

  • Here: what exists now (systems, sources, constraints, policies, dependencies, quality, ownership, and known gaps). Not a slide. Not a tribal memory. Ground truth.

  • There: what "done" looks like (required outcomes, acceptance criteria, interfaces, governance rules, and the boundaries the agent must not cross).

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