--- title: "Belvedere | Your Agentic Data Manager" description: "Declare what data you need. Belvedere handles discovery, governance, pipeline generation, observability, and repair across your existing stack." canonical: "https://www.clearfracture.ai/platform" --- ![Belvedere](https://rgzgbulrzrbknuiprxti.supabase.co/storage/v1/object/public/media/uploads/1778699597981-belvedere-png-medium.webp)Meet Belvedere: # Your Agentic Data Manager Declare what data you need. Belvedere handles everything behind it: discovery, governance, pipeline generation, observability, and repair across your existing stack, with deterministic results your teams can verify and trust. [Request a Demo](/demo) [Contact Us](/contact) app.clearfracture.ai Live Analyst & Decision-Maker Interface Ask BelvedereNatural Language Show me all identity pipelines that failed validation this week Belvedere Found 3 pipelines with validation errors. I've auto-generated a fix for the schema drift in HUMINT Source Merge. Deploy? Yes, deploy and notify the compliance team Belvedere Deployed. Compliance bot notified. Pipeline restored — 100% validation. Pipeline Dashboard Processed Validated 47Active 99.2%Healthy Data Explorer Reports Belvedere Configuration Plane 47Pipelines 23Sources 1.2M/minThroughput KnowledgeDiscovers & maps WorkflowBuilds & deploys SchemaDetects & heals ComplianceGoverns & audits PublishRoutes & delivers Your Data Enterprise SourcesEnginesDestinations & Consumers PostgreSQL Airflow Snowflake S3 / Azure Spark BigQuery Kafka Streams dbt Data Lake Salesforce Databricks Tableau / BI MongoDB Bots active Analyst Notebooks SFTP / Files Palantir API Consumers REST APIs AWS Glue Live Dashboards ✓47 pipelines active✓1,847 tables synced✓5 agents running✓99.9% uptime Declarative data ops ## You define the goal. Belvedere engineers the solution. Describe the data products you need in plain, goal-oriented terms. Belvedere's agents harness AI speed and intelligence to derive contracts, reason through system models, and generate deterministic, repeatable implementations you can verify and trust — delivering in minutes, not the weeks or months you're used to waiting. - Plain-language in, deterministic code out — no hallucinations - Every output is verifiable, repeatable, and auditable - AI speed without sacrificing trust or control - Minutes to production, not weeks 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 Store Action Score delivery risk Apply ML-driven risk scoring based on historical carrier performance, weather, and route congestion signals. Read Store 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. Markdown supported. Type @ to link data sources. Ctrl+B bold. Ctrl+I italic. Read Sources Warehouse Mgmt Suite Write Targets Add target Saved 6 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. 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 Score delivery risk Apply ML-driven risk scoring based on historical carrier performance, weather, and route congestion signals. 6 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 app.clearfracture.ai/catalog Live BelvederePipelinesData CatalogLearning Hive Add Source All Datasources6 Cloud Storage2 Database Conn.3 Streaming1 Filter... Source SIGINT Feed AlphaDatabase Connection GEOINT ArchiveCloud Storage HUMINT Case MgmtDatabase Connection SIGINT Feed AlphaDatabase Connection 14 fields Sample DataStructureEnrichments Classification & Security MarkingTS/SCI — Top Secret / Sensitive CompartmentedTS/SCI DisseminationNOFORN — Not Releasable to Foreign NationalsNOFORN Handling CaveatSpecial handling procedures apply Access & Releasability ReleasabilityREL TO USA ONLYUSA Only Data OwnerSIGINT Operations Division Access LevelAuthorized personnel — need-to-know Topic Taxonomy DomainSignals Intelligence / CommunicationsInferred Content Fieldsintercept\_body, metadata\_header ClassificationLLM topic classification availableAI Source Metadata CategoryDatabase Connection Update Freq.Real-time / Event-driven Quality Score92 / 100 — High confidence Data Governance RetentionMission-defined retention policy PII DetectionScanned — 3 fields flaggedScanned LineageCollector → Catalog → EnrichmentTracked Inferred Schema 14 fields Tintercept\_idUUID Ttimestamp\_utcTIMESTAMP Tsource\_platformVARCHAR Tintercept\_bodyTEXT app.clearfracture.ai/catalog Live BelvedereData Catalog SIGINT Feed AlphaDatabase Connection 14 fields Sample DataStructureEnrichments Classification & Security MarkingTS/SCI — Top Secret / Sensitive CompartmentedTS/SCI DisseminationNOFORN — Not Releasable to Foreign NationalsNOFORN Handling CaveatSpecial handling procedures apply Access & Releasability ReleasabilityREL TO USA ONLYUSA Only Data OwnerSIGINT Operations Division Access LevelAuthorized personnel — need-to-know Topic Taxonomy DomainSignals Intelligence / CommunicationsInferred Content Fieldsintercept\_body, metadata\_header ClassificationLLM topic classification availableAI Source Metadata CategoryDatabase Connection Update Freq.Real-time / Event-driven Quality Score92 / 100 — High confidence Data Governance RetentionMission-defined retention policy PII DetectionScanned — 3 fields flaggedScanned LineageCollector → Catalog → EnrichmentTracked Inferred Schema 14 fields Tintercept\_idUUID Ttimestamp\_utcTIMESTAMP Tsource\_platformVARCHAR Tintercept\_bodyTEXT Data Catalog ## Agentic discovery across your entire data landscape. Belvedere's Knowledge Arm automatically discovers, classifies, and catalogs every data source across your environment — from cloud storage and databases to streaming feeds and legacy systems. Each source is enriched with classification markings, access controls, topic taxonomy, governance policies, and inferred schema — capturing not just structure but what the data means, all without manual intervention. - Finds every source — cloud, on-prem, or air-gapped - Schema, classification, and governance inferred automatically - End-to-end lineage from ingestion to delivery - PII detection and releasability controls built in Observability & Self-Healing ## Drift happens. Belvedere handles it. Belvedere's Observability Arm monitors every pipeline and integration point in real time. When something drifts — a schema change, a quality anomaly, a broken contract — it detects the issue and diagnoses root cause automatically. High-confidence fixes are applied instantly. Lower-confidence changes are surfaced to your team for review — or run Belvedere in proposal mode, where every change requires human approval before it ships. You choose the level of autonomy. - High-confidence fixes applied automatically — no 3 a.m. pages - Low-confidence changes routed to your team for review - Proposal mode available — human approves every change before deploy - Full audit trail on every action, automatic or approved app.clearfracture.ai/observability Live ObservabilityPipeline health & self-healing All systems nominal Pipeline Health 99.9% Avg Latency 2.1s Auto-heals (24h) 7 Recent Events Last 30 min Schema drift detected — auto-healed HUMINT Merge2 min ago Migration applied Latency spike above threshold Identity Graph8 min ago Investigating New source column discovered GEOINT Ingest15 min ago Cataloged Data quality anomaly corrected SIGINT Fusion23 min ago Quarantined & repaired 7 issues auto-resolved · 0 manual actions needed Avg resolution: 12s app.clearfracture.ai/observability Live ObservabilityAll nominal 99.9%Health 2.1sLatency 7Auto-heals Schema drift detected — auto-healedHUMINT Merge · 2 min ago Latency spike above thresholdIdentity Graph · 8 min ago New source column discoveredGEOINT Ingest · 15 min ago 7 auto-resolved 0 manual actions Architecture ## Three Intelligent Arms. One Unified Platform. Belvedere operates as a multi-agent system with three intelligent “arms” — knowing, doing, and watching. Processing logic is maintained separate from its implementation, making understanding accessible to non-developers and platform migrations painless. 01 — Knowing ### Knowledge Arm Continuously explores your systems, tools, and data sources to understand where data lives, what it means to different teams, how it flows, and what governs it. Definitions, relationships, and context are stored — so knowledge persists even when people leave. - Auto-discovers sources and schema - Maps lineage, context, and governance - Living knowledge graph of your environment 02 — Doing ### Workflow Arm Designs, tests, and deploys deterministic, auditable pipelines with enforced contracts between data producers and consumers. Context carries through every transformation layer — traceable, affordable at scale, and already trusted by the enterprise. - Generates deterministic, auditable code - Deploys on your existing infrastructure - Goal-oriented — declare what, not how 03 — Watching ### Observability Arm Monitors every pipeline, data product, and integration point in real time. When something drifts — a schema change, a definition that no longer matches its contract, a data quality anomaly — Belvedere detects it, diagnoses the root cause, and self-heals before it impacts downstream consumers. - Real-time pipeline health monitoring - Automatic schema-drift detection and repair - Self-healing with full audit trail Agent-Native Operations ## Give Your Existing Agent Swarm a Chief Data Officer. ### Connect to the stack you already run Belvedere works inside your existing enterprise and agent architecture, so value increases without a rip-and-replace program. ### Give every agent shared, governed context Clean data products, contracts, and lineage-aware context stay intact across transformations instead of being lost in prompts and pipeline code. ### Make every agent output easier to trust Auditable, verifiable outputs give your teams the confidence to use agent-driven decisions in real operational workflows. Belvedere does not ask you to replace the agents, models, or orchestration layers you've already deployed. It operates inside that environment as the trusted data and governance layer, giving every agent access to current, structured, lineage-aware context instead of brittle prompts, stale retrieval results, or disconnected source systems. Belvedere can act as the Chief Data Officer for your agent swarm, giving every agent the equivalent of a team of data engineers and data stewards. It publishes clean data products, contracts, and governed context that agents can use directly, so their decisions are informed by context that is data-driven, auditable, and verifiable. Let Belvedere operate autonomously where confidence is high, and require human review where the stakes are higher. Security Model ## Verifiable by design. AI speed and intelligence, with deterministic results you can trust. ### No hallucinations Deterministic, verifiable code output — not probabilistic guesses ### No vendor lock-in Portable pipeline logic that runs anywhere your infrastructure lives ### Automation you can trust Every action is logged, explainable, and fully auditable ### Operates your tools Maximizes your existing IT investments instead of replacing them Belvedere operates your tools on your behalf — its agents never touch mission data directly. They write verifiable code that runs inside your environment, using data contracts and system models to know what the data means before they act. No hallucinations. No black boxes. AI that produces deterministic, auditable, repeatable output you can verify before it ever reaches production. Get Started ## Ready to See Belvedere in Action? We'll show you Belvedere operating on a live data environment — not slides. See how declarative data ops delivers trusted results in minutes. [Request a Demo](/demo) [Contact Us](/contact) Latest Articles ## From the Clear Fracture Team [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/platform](https://www.clearfracture.ai/platform)