BelvedereMeet Belvedere:

Your AI-Native Data Control Plane

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.

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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
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
BelvedereData Catalog
SIGINT Feed AlphaDatabase Connection
14 fields
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
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.

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Latest Articles

From the ClearFracture Team

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"

Read whole article
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