--- title: "Clear Fracture | Trusted Data Operations For The AI Era" description: "Clear Fracture builds AI-native systems that help complex organizations discover, govern, engineer, and operate all the data their missions depend on." canonical: "https://www.clearfracture.ai/" --- # 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. [Explore the Belvedere Platform](/platform) [Request a Demo](/demo) Data Sources S3 / Azure APIs Oracle SAP SQL Server Platforms & Tools Snowflake Databricks Airflow dbt LLM Models Claude OpenAI Llama Bedrock Consumers BI / Dashboards Internal Apps Analysts Notebooks ![Belvedere](/images/explainer/belvedere.svg?dpl=dpl_7rz6tTTZPW9iSpEQv9XhrtFj5Gvn) BelvedereAgentic Data Manager Knowledge Discover all assets Semantic graph Resolve definitions Preserve context Workflow Declare, not script Auto-gen pipelines Test before deploy Runs on your infra Observability Quality scoring Drift detection Auto-heal pipelines Lineage & audit Technical Stewards Data EngineersBuild & maintain Data StewardsDefine & govern Business Consumers Analytics TeamsExplore & report Business UsersSelf-serve insights ExecutivesStrategic decisions Data ScientistsModel & predict Data Engineers & Stewards ![Belvedere](/images/explainer/belvedere.svg?dpl=dpl_7rz6tTTZPW9iSpEQv9XhrtFj5Gvn) 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](/images/partners/dow-seal.png) ![Intelligence Community](/images/partners/ic-seal.png) ![Carahsoft](/images/partners/carahsoft.svg) ![Unfiltered Media Group](/images/partners/unfiltered-white.png) ![TapHere! Technology](/images/partners/taphere.png) ![Amazon Web Services](/images/partners/aws.svg) 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](/_next/image?url=%2Fimages%2Fweb-images%2Fbelvedere-white-logo-.png&w=828&q=75&dpl=dpl_7rz6tTTZPW9iSpEQv9XhrtFj5Gvn) 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 PipelinesGlobal Logistics MonitoringUnsaved BuildSave as Template Lineage Source Carrier Tracking Systems Real-time GPS and status updates from 12 carrier platforms across road, rail, air, and ocean freight networks. Source Warehouse Management Suite Inventory levels, dock schedules, and shipment staging data from 8 regional distribution centers worldwide. Source Customs & Compliance Feeds Import/export declarations, tariff codes, and regulatory hold notices from customs authorities across 14 ports of entry. Transform Normalize carrier schemas Reconcile tracking formats across all carrier platforms into a unified shipment event model with standardized status codes. Read Store Transform Correlate shipment lifecycle Link tracking events to warehouse records, building end-to-end shipment timelines with handoff traceability. Read Lineage Transform Validate compliance holds Cross-reference customs declarations against regulatory rules, flagging holds and tariff exceptions in real time. Read Govern Action Publish to operations layer Merge correlated and validated streams into a single governed dataset for the global operations dashboard. Read Publish Enrich Score delivery risk Apply ML-driven risk scoring on the published dataset using carrier history, weather, and route congestion signals. Read Score ACTION Load Warehouse Management Suite Ingest inventory and shipment staging data from all 8 regional distribution centers. DescriptionInputsOutputsHistory 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. Read Sources Warehouse Mgmt Suite Write Targets Add target Saved 8 nodesDataUnsaved changes Zoom: 100%Last saved: 8:34 AM 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. 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. Ask about this pipeline 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. Ask about this pipeline Every Source DiscoveredEvery Pipeline GovernedEvery Change MonitoredEvery Output Auditable [Explore the Platform](/platform) ## 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 [View all](/articles) [![Agent Skill: Idempotent Backfill for Late-Arriving Data](/_next/image?url=https%3A%2F%2Frgzgbulrzrbknuiprxti.supabase.co%2Fstorage%2Fv1%2Fobject%2Fpublic%2Fmedia%2Fuploads%2F1787677997000-idempotent-backfill-animation_2x.gif&w=1920&q=75)](/articles/an-idempotent-backfill-skill-for-agentic-data-pipelines) [ ## Agent Skill: Idempotent Backfill for Late-Arriving Data ](/articles/an-idempotent-backfill-skill-for-agentic-data-pipelines) ![Haydn Strauss](/_next/image?url=https%3A%2F%2Frgzgbulrzrbknuiprxti.supabase.co%2Fstorage%2Fv1%2Fobject%2Fpublic%2Fmedia%2Fuploads%2F1776107930464-E8BC84B9-E3E5-4B8B-B138-F2D6989C734A.png&w=48&q=75) [Haydn Strauss](/articles/author/haydn-strauss)4 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](https://www.clearfracture.ai/skills/idempotent-backfill.md) 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](https://www.uber.com/us/en/blog/ubers-lakehouse-architecture/) 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](https://rgzgbulrzrbknuiprxti.supabase.co/storage/v1/object/public/media/uploads/1787679315617-published-skill.png?v=3) *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](https://maximebeauchemin.medium.com/functional-data-engineering-a-modern-paradigm-for-batch-data-processing-2327ec32c42a) 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. [Read whole article](/articles/an-idempotent-backfill-skill-for-agentic-data-pipelines) [![From Here to There: How Belvedere™ Maps Your Current State and Builds the Path to Your Target](/_next/image?url=https%3A%2F%2Frgzgbulrzrbknuiprxti.supabase.co%2Fstorage%2Fv1%2Fobject%2Fpublic%2Fmedia%2Fuploads%2F1786592161000-here-to-there-map-animation-v2_2x.gif&w=1920&q=75)](/articles/from-here-to-there) [ ## From Here to There: How Belvedere™ Maps Your Current State and Builds the Path to Your Target ](/articles/from-here-to-there) ![Brian Frutchey](/_next/image?url=https%3A%2F%2Frgzgbulrzrbknuiprxti.supabase.co%2Fstorage%2Fv1%2Fobject%2Fpublic%2Fmedia%2Fuploads%2F1775054766834-1613771332003.jpg&w=48&q=75) [Brian Frutchey](/articles/author/brian-frutchey)6 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). [Read whole article](/articles/from-here-to-there) [![Turn GitHub Repositories Into Explorable SysML v2 System Models](/_next/image?url=https%3A%2F%2Frgzgbulrzrbknuiprxti.supabase.co%2Fstorage%2Fv1%2Fobject%2Fpublic%2Fmedia%2Fuploads%2F1784667213851-automated-system-modeling-animation_2x.gif&w=1920&q=75)](/articles/sysml-repo-modeler-github-repositories) [ ## Turn GitHub Repositories Into Explorable SysML v2 System Models ](/articles/sysml-repo-modeler-github-repositories) [Jeremy Fields](/articles/author/jeremy-fields) and [John Sutton](/articles/author/john-sutton)6 min readSystem ModelingPublished August 5, 2026 SysML Repo Modeler is now open source and free to use. It turns one GitHub repository—or many—into an explorable SysML v2 system model. [Get the code on GitHub](https://github.com/ClearFracture/sysml-repo-modeler). It gives us a coherent way to visualize our own repositories, services, APIs, and layers of institutional knowledge in an exploratory systems view. And now it’s open source for anyone to freely use. ## Explore a Live Model Want to see the result before getting into the details? Start with the [live Supabase Platform model](https://www.belvederelabs.ai/project-analyzer/supabase-platform), which maps five repositories and four languages as one system. You can also explore the [OpenClaw model](https://www.belvederelabs.ai/project-analyzer/openclaw), the [n8n model](https://www.belvederelabs.ai/project-analyzer/n8n) and the [Ollama model](https://www.belvederelabs.ai/project-analyzer/ollama). All four sit side by side in the [SysML Repo Modeler gallery](https://www.belvederelabs.ai/project-analyzer), with the repository, part and connection counts for each. [![The SysML Repo Modeler model of Supabase Platform showing services and dependencies across five repositories](https://www.belvederelabs.ai/screenshots/supabase-platform.webp)](https://www.belvederelabs.ai/project-analyzer/supabase-platform) [*Open the live Supabase Platform model*](https://www.belvederelabs.ai/project-analyzer/supabase-platform) *— click the image to search, filter, and explore the system.* ## The Problem: Architecture Lives in Too Many Places The reality is that modern systems are scattered across multiple repositories, services, APIs, and layers of institutional knowledge. The challenge is not that teams lack documentation; it is that documentation struggles to keep up with what the code does. Traditional Model-Based Systems Engineering (MBSE) documentation can be useful, but it is often manually maintained across disparate software platforms that require significant user knowledge and training, with limited ability to transfer data between tools. As a system evolves, the diagram becomes a snapshot of what people thought a system looked like but not necessarily what exists now. [Read whole article](/articles/sysml-repo-modeler-github-repositories) [View all articles](/articles) --- Source: [https://www.clearfracture.ai/](https://www.clearfracture.ai/)