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1347 lines (1252 loc) Β· 65.4 KB
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<!DOCTYPE html>
<html lang="en" class="no-js">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<script>document.documentElement.classList.remove('no-js');</script>
<title>Data Concierge β AI-Powered Data Enrichment with High-Value Datasets</title>
<meta name="description" content="Ask questions in plain English about your data, enriched with authoritative high-value datasets β and get citation-backed answers with reproducible Jupyter notebooks." />
<!-- Canonical / social -->
<link rel="canonical" href="https://dataconcierge.dathere.com/" />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://dataconcierge.dathere.com/" />
<meta property="og:title" content="Data Concierge β AI-Powered Data Enrichment with High-Value Datasets" />
<meta property="og:description" content="Ask questions in plain English about your data, enriched with authoritative high-value datasets β and get citation-backed answers with reproducible Jupyter notebooks." />
<meta name="twitter:card" content="summary" />
<meta name="twitter:title" content="Data Concierge β AI-Powered Data Enrichment with High-Value Datasets" />
<meta name="twitter:description" content="Ask questions in plain English about your data, enriched with authoritative high-value datasets β and get citation-backed answers with reproducible Jupyter notebooks." />
<!-- Favicon (inline SVG: brand-blue dot) -->
<link rel="icon" type="image/svg+xml" href="data:image/svg+xml;utf8,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 32 32'><circle cx='16' cy='16' r='12' fill='%233b82f6'/></svg>" />
<!-- Fonts -->
<link rel="preconnect" href="https://fonts.googleapis.com" />
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet" />
<style>
/* ββ Reset & Base βββββββββββββββββββββββββββββββββββββββββββ */
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
:root {
--bg: #0a0e17;
--bg-card: #111827;
--bg-card2: #141c2b;
--border: rgba(255,255,255,.08);
--border-glow: rgba(99,179,237,.25);
--text: #e2e8f0;
--muted: #8b98b1;
--accent: #3b82f6;
--accent-2: #8b5cf6;
--accent-3: #06b6d4;
--accent-4: #10b981;
--red: #ef4444;
--yellow: #f59e0b;
--grad-hero: linear-gradient(135deg, #0a0e17 0%, #0d1a2e 50%, #0f1629 100%);
--grad-accent: linear-gradient(135deg, #3b82f6 0%, #8b5cf6 100%);
--radius: 12px;
--radius-lg: 20px;
--shadow: 0 4px 24px rgba(0,0,0,.4);
--shadow-lg: 0 12px 48px rgba(0,0,0,.6);
}
html { scroll-behavior: smooth; }
body {
font-family: 'Inter', sans-serif;
background: var(--bg);
color: var(--text);
line-height: 1.6;
overflow-x: hidden;
}
a { color: inherit; text-decoration: none; }
img { max-width: 100%; display: block; }
/* ββ Utilities ββββββββββββββββββββββββββββββββββββββββββββββ */
.container { max-width: 1200px; margin: 0 auto; padding: 0 24px; }
.container-sm { max-width: 800px; margin: 0 auto; padding: 0 24px; }
.badge {
display: inline-flex; align-items: center; gap: 6px;
padding: 4px 12px; border-radius: 999px; font-size: 12px; font-weight: 600;
letter-spacing: .03em; text-transform: uppercase;
}
.badge-blue { background: rgba(59,130,246,.15); color: #60a5fa; border: 1px solid rgba(59,130,246,.3); }
.badge-purple { background: rgba(139,92,246,.15); color: #a78bfa; border: 1px solid rgba(139,92,246,.3); }
.badge-green { background: rgba(16,185,129,.15); color: #34d399; border: 1px solid rgba(16,185,129,.3); }
.badge-cyan { background: rgba(6,182,212,.15); color: #22d3ee; border: 1px solid rgba(6,182,212,.3); }
.badge-yellow { background: rgba(245,158,11,.15); color: #fbbf24; border: 1px solid rgba(245,158,11,.3); }
.gradient-text {
background: var(--grad-accent);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
background-clip: text;
}
/* ββ Nav ββββββββββββββββββββββββββββββββββββββββββββββββββββ */
nav {
position: fixed; top: 0; left: 0; right: 0; z-index: 100;
background: rgba(10,14,23,.85);
backdrop-filter: blur(16px);
border-bottom: 1px solid var(--border);
}
.nav-inner {
display: flex; align-items: center; justify-content: space-between;
height: 64px;
}
.nav-logo {
display: flex; align-items: center; gap: 10px;
font-weight: 700; font-size: 18px;
}
.nav-logo .dot { width: 8px; height: 8px; border-radius: 50%; background: var(--accent); }
.nav-links { display: flex; align-items: center; gap: 32px; }
.nav-links a { font-size: 14px; color: var(--muted); transition: color .2s; }
.nav-links a:hover { color: var(--text); }
.nav-cta {
padding: 8px 20px; border-radius: var(--radius);
background: var(--grad-accent); color: #fff !important;
font-weight: 600; font-size: 14px;
transition: opacity .2s, transform .2s;
}
.nav-cta:hover { opacity: .9; transform: translateY(-1px); }
/* Mobile nav toggle */
.nav-toggle {
display: none;
background: transparent;
border: 1px solid var(--border);
color: var(--text);
width: 40px; height: 40px;
border-radius: 8px;
cursor: pointer;
align-items: center; justify-content: center;
padding: 0;
}
.nav-toggle:hover { border-color: rgba(255,255,255,.2); background: rgba(255,255,255,.04); }
.nav-toggle svg { width: 20px; height: 20px; }
@media (max-width: 768px) {
.nav-toggle { display: inline-flex; }
.nav-links {
display: none;
position: absolute;
top: 64px; left: 0; right: 0;
flex-direction: column;
align-items: stretch;
gap: 0;
background: rgba(10,14,23,.97);
backdrop-filter: blur(16px);
border-bottom: 1px solid var(--border);
padding: 12px 24px 20px;
}
.nav-links.open { display: flex; }
.nav-links a {
padding: 14px 0;
font-size: 16px;
border-bottom: 1px solid var(--border);
}
.nav-links a:last-child { border-bottom: none; }
.nav-cta { margin-top: 12px; text-align: center; }
}
/* Skip link */
.skip-link {
position: absolute;
top: -48px; left: 8px;
background: var(--accent);
color: #fff;
padding: 8px 16px;
border-radius: 6px;
z-index: 200;
font-weight: 600;
font-size: 14px;
transition: top .2s;
}
.skip-link:focus { top: 8px; outline: 2px solid #fff; outline-offset: 2px; }
/* ββ Hero βββββββββββββββββββββββββββββββββββββββββββββββββββ */
.hero {
padding: 140px 0 100px;
background: var(--grad-hero);
position: relative;
overflow: hidden;
}
.hero::before {
content: '';
position: absolute; inset: 0;
background:
radial-gradient(ellipse 80% 60% at 20% 20%, rgba(59,130,246,.08) 0%, transparent 60%),
radial-gradient(ellipse 60% 80% at 80% 80%, rgba(139,92,246,.07) 0%, transparent 60%);
pointer-events: none;
}
.hero-grid {
display: grid; grid-template-columns: 1fr 1fr;
gap: 64px; align-items: center;
}
.hero-eyebrow { margin-bottom: 20px; }
.hero h1 {
font-size: clamp(36px, 5vw, 56px);
font-weight: 800;
line-height: 1.15;
letter-spacing: -.02em;
margin-bottom: 24px;
}
.hero h1 .hero-connector {
display: block;
font-weight: 500;
font-size: .32em;
letter-spacing: .22em;
color: var(--muted);
text-transform: uppercase;
text-align: center;
margin: .35em 0 .2em;
}
.hero h1 .hero-connector::before,
.hero h1 .hero-connector::after {
content: "β";
display: inline-block;
margin: 0 .7em;
opacity: .55;
}
@media (min-width: 480px) {
.hero h1 .gradient-text { white-space: nowrap; }
}
.hero p {
font-size: 18px; color: var(--muted); line-height: 1.7;
margin-bottom: 36px; max-width: 520px;
}
.hero-actions { display: flex; gap: 16px; flex-wrap: wrap; }
.btn-primary {
display: inline-flex; align-items: center; gap: 8px;
padding: 14px 28px; border-radius: var(--radius);
background: var(--grad-accent); color: #fff;
font-weight: 600; font-size: 15px;
transition: opacity .2s, transform .2s, box-shadow .2s;
box-shadow: 0 4px 20px rgba(59,130,246,.35);
}
.btn-primary:hover { opacity: .9; transform: translateY(-2px); box-shadow: 0 8px 28px rgba(59,130,246,.4); }
.btn-secondary {
display: inline-flex; align-items: center; gap: 8px;
padding: 14px 28px; border-radius: var(--radius);
background: transparent; color: var(--text);
border: 1px solid var(--border);
font-weight: 600; font-size: 15px;
transition: border-color .2s, background .2s, transform .2s;
}
.btn-secondary:hover { border-color: rgba(255,255,255,.2); background: rgba(255,255,255,.04); transform: translateY(-2px); }
/* Terminal Card */
.hero-terminal {
background: var(--bg-card);
border: 1px solid var(--border);
border-radius: var(--radius-lg);
overflow: hidden;
box-shadow: var(--shadow-lg);
position: relative;
}
.terminal-header {
display: flex; align-items: center; gap: 8px;
padding: 14px 16px;
background: rgba(255,255,255,.03);
border-bottom: 1px solid var(--border);
}
.terminal-dot {
width: 12px; height: 12px; border-radius: 50%;
}
.terminal-dot.red { background: #ff5f56; }
.terminal-dot.yellow { background: #ffbd2e; }
.terminal-dot.green { background: #27c93f; }
.terminal-title { font-size: 12px; color: var(--muted); margin-left: 8px; font-family: 'JetBrains Mono', monospace; }
.terminal-body { padding: 24px; font-family: 'JetBrains Mono', monospace; font-size: 13px; line-height: 1.8; }
.t-prompt { color: var(--accent-4); }
.t-query { color: var(--text); }
.t-comment { color: var(--muted); }
.t-key { color: #fbbf24; }
.t-val { color: #34d399; }
.t-string { color: #a78bfa; }
.t-num { color: #60a5fa; }
.t-bar {
height: 4px; border-radius: 2px;
background: linear-gradient(90deg, var(--accent-4), var(--accent));
margin: 4px 0; animation: progress 2s ease-in-out infinite;
}
@keyframes progress {
0%,100% { opacity: 1; }
50% { opacity: .5; }
}
@media (max-width: 900px) {
.hero-grid { grid-template-columns: 1fr; }
.hero-terminal { display: none; }
}
/* ββ Stats Bar ββββββββββββββββββββββββββββββββββββββββββββββ */
.stats-bar {
padding: 48px 0;
border-bottom: 1px solid var(--border);
}
.stats-grid { display: grid; grid-template-columns: repeat(4, 1fr); gap: 24px; }
.stat-item { text-align: center; }
.stat-number { font-size: 36px; font-weight: 800; letter-spacing: -.02em; }
.stat-label { font-size: 14px; color: var(--muted); margin-top: 4px; }
@media (max-width: 640px) {
.stats-grid { grid-template-columns: repeat(2, 1fr); }
}
/* ββ Sections βββββββββββββββββββββββββββββββββββββββββββββββ */
section { padding: 96px 0; }
.section-header { text-align: center; margin-bottom: 64px; }
.section-header h2 {
font-size: clamp(28px, 4vw, 42px); font-weight: 800;
letter-spacing: -.02em; margin-bottom: 16px;
}
.section-header p { font-size: 18px; color: var(--muted); max-width: 560px; margin: 0 auto; }
/* ββ How It Works βββββββββββββββββββββββββββββββββββββββββββ */
.how-it-works { background: var(--bg-card2); }
.steps { display: flex; flex-direction: column; gap: 0; }
.step {
display: grid; grid-template-columns: 80px 1fr;
gap: 32px; align-items: start;
padding: 40px 0;
border-bottom: 1px solid var(--border);
position: relative;
}
.step:last-child { border-bottom: none; }
.step-num {
width: 56px; height: 56px; border-radius: 50%;
display: flex; align-items: center; justify-content: center;
font-size: 20px; font-weight: 800;
background: var(--bg-card);
border: 1px solid var(--border);
flex-shrink: 0;
position: relative; z-index: 1;
}
.step:not(:last-child) .step-num::after {
content: '';
position: absolute; top: 100%; left: 50%; transform: translateX(-50%);
width: 2px; height: calc(100% + 80px);
background: linear-gradient(to bottom, var(--border), transparent);
}
.step-content h3 { font-size: 20px; font-weight: 700; margin-bottom: 10px; }
.step-content p { color: var(--muted); font-size: 15px; }
.step-tags { display: flex; gap: 8px; flex-wrap: wrap; margin-top: 12px; }
.step-tag {
font-size: 11px; font-weight: 600; padding: 3px 10px;
border-radius: 6px; font-family: 'JetBrains Mono', monospace;
background: rgba(59,130,246,.1); color: #60a5fa;
border: 1px solid rgba(59,130,246,.2);
}
/* ββ Features βββββββββββββββββββββββββββββββββββββββββββββββ */
.features-grid {
display: grid;
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Data Concierge
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<h1>Ask Any Question <span class="gradient-text">About Your Data</span> <span class="hero-connector">enriched with</span> <span class="gradient-text">High Value Datasets</span></h1>
<p>Data Concierge turns plain-English questions into citation-backed answers with reproducible Jupyter notebooks β deployable in any jurisdiction, powered by an ever-expanding library of authoritative datasets.</p>
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<div><span class="t-prompt">user ></span> <span class="t-query">How do Pittsburgh 311 complaints per capita correlate with neighborhood income and unemployment?</span></div>
<br>
<div><span class="t-comment">βΈ Intent: </span><span class="t-string">DATA_LINKING Β· Tier 2</span></div>
<div><span class="t-comment">βΈ Entities: </span><span class="t-val">Pittsburgh</span> <span class="t-comment">/ </span><span class="t-val">311 Requests, MedianIncome, Unemployment</span></div>
<div><span class="t-comment">βΈ Sources: </span><span class="t-key">Pittsburgh 311</span><span class="t-comment">, </span><span class="t-key">Census ACS</span><span class="t-comment">, </span><span class="t-key">BLS LAUS</span></div>
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<div><span class="t-comment">βΈ Top neighborhood: </span><span class="t-val">Carrick</span> <span class="t-comment">(</span><span class="t-num">8.4</span><span class="t-comment"> reqs / 100 res)</span></div>
<div><span class="t-comment">βΈ Median income: </span><span class="t-num">$42,300</span> <span class="t-comment">vs. </span><span class="t-num">$58,000</span><span class="t-comment"> citywide</span></div>
<div><span class="t-comment">βΈ Unemployment: </span><span class="t-num">4.0%</span> <span class="t-comment">(Allegheny Co.)</span></div>
<div><span class="t-comment">βΈ Correlation r: </span><span class="t-num">-0.71</span> <span class="t-comment">(income Γ complaints)</span></div>
<div><span class="t-comment">βΈ Confidence: </span><span class="t-val">HIGH (0.88)</span></div>
<br>
<div><span class="t-comment">βΈ Notebook: </span><span class="t-string">pgh_311_demographics.ipynb</span> <span class="t-val">β</span></div>
<div><span class="t-comment">βΈ Citations: </span><span class="t-num">4</span><span class="t-comment"> official sources cited</span></div>
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<!-- ββ STATS ββββββββββββββββββββββββββββββββββββββββββββββββββββ -->
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<div class="stat-number gradient-text">7+</div>
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<div class="stat-number gradient-text">60+</div>
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<div class="stat-number gradient-text">6</div>
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<div class="stat-number gradient-text">100%</div>
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<span class="badge badge-cyan">Process</span>
<h2 style="margin-top:16px">From Question to<br>Reproducible Research</h2>
<p>Eight intelligent agents work in sequence to classify your intent, retrieve authoritative data, FAIRify it against your metadata schemas, compute statistics, and deliver a citable notebook.</p>
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<div class="step-num gradient-text">1</div>
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<h3>Intent Classification</h3>
<p>Your natural-language query is classified into one of six intent types β factual lookup, comparison, trend analysis, dataset discovery, methodology question, or data linking β and routed to the appropriate processing tier (1, 2, or 3).</p>
<div class="step-tags">
<span class="step-tag">FACTUAL_LOOKUP</span>
<span class="step-tag">COMPARISON</span>
<span class="step-tag">TREND_ANALYSIS</span>
<span class="step-tag">DATASET_DISCOVERY</span>
<span class="step-tag">METHODOLOGY_QUESTION</span>
<span class="step-tag">DATA_LINKING</span>
</div>
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<div class="step fade-in">
<div class="step-num gradient-text">2</div>
<div class="step-content">
<h3>Entity Extraction</h3>
<p>Places are resolved to canonical identifiers, variables are mapped to authoritative codes from the active connector pack (e.g., BLS series, Census variables, FRED codes), and time periods are normalized. Geographic resolution scales from country down to state/province, county, and city β wherever the connectors reach.</p>
<div class="step-tags">
<span class="step-tag">geoId/48 β Texas</span>
<span class="step-tag">NUTS:FR10 β Γle-de-France</span>
<span class="step-tag">UnemploymentRate</span>
<span class="step-tag">2019β2024</span>
</div>
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<div class="step-num gradient-text">3</div>
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<h3>Multi-Source Data Retrieval</h3>
<p>A prioritized retrieval strategy first queries Data Commons, then falls back to vector similarity search, then directly calls the configured connector APIs β BLS, Census, BEA, FRED, or any custom source plugged into the connector library. Results are deduplicated and cached with per-source TTLs.</p>
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<span class="step-tag">Data Commons v2</span>
<span class="step-tag">Qdrant / Pinecone</span>
<span class="step-tag">Redis TTL Cache</span>
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<div class="step-num gradient-text">4</div>
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<h3>FAIRification & Metadata Enrichment</h3>
<p>Retrieved data is enriched in-process against FAIR principles β Findable, Accessible, Interoperable, Reusable. Customizable metadata schemas (DCAT-AP, Schema.org/Dataset, DataCite, or your organization's own) describe provenance, licensing, controlled vocabularies, and quality flags before any downstream computation.</p>
<div class="step-tags">
<span class="step-tag">DCAT-AP</span>
<span class="step-tag">Schema.org/Dataset</span>
<span class="step-tag">DataCite</span>
<span class="step-tag">Custom Schemas</span>
</div>
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<div class="step fade-in">
<div class="step-num gradient-text">5</div>
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<h3>Statistical Computation</h3>
<p>The Stats Computer runs the appropriate calculation for each intent type: direct lookups, time-series trend analysis, cross-sectional comparisons, or statistical inference β each with its own reliability score.</p>
<div class="step-tags">
<span class="step-tag">direct_lookup: 1.0</span>
<span class="step-tag">trend_analysis: 0.85</span>
<span class="step-tag">statistical_inference: 0.70</span>
</div>
</div>
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<div class="step fade-in">
<div class="step-num gradient-text">6</div>
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<h3>Visualization Generation</h3>
<p>Vega-Lite chart specifications are generated automatically β time-series line charts, bar comparisons, geographic choropleth maps β and embedded directly in the output notebook.</p>
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<span class="step-tag">Vega-Lite</span>
<span class="step-tag">Time Series</span>
<span class="step-tag">Choropleth</span>
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<div class="step-num gradient-text">7</div>
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<h3>Citation Building</h3>
<p>Every data point is traced back to its authoritative source. Citations are formatted in both government and academic styles with publication dates, dataset names, API endpoints, and access timestamps.</p>
<div class="step-tags">
<span class="step-tag">Government Format</span>
<span class="step-tag">Academic Format</span>
<span class="step-tag">API Attribution</span>
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<div class="step-num gradient-text">8</div>
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<h3>Reproducible Notebook</h3>
<p>Every query generates a self-contained, Colab-ready Jupyter notebook. The full execution trace β every API call, every computation β is recorded so your analysis can be reproduced, modified, and peer-reviewed.</p>
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<h2 style="margin-top:16px">Built for Rigorous<br>Data Research</h2>
<p>Every feature is designed around a core principle: authoritative data, traceable methods, and reproducible results.</p>
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<div class="feature-icon" aria-hidden="true" style="background:rgba(59,130,246,.12)">π§ </div>
<h3>Multi-Agent AI Orchestration</h3>
<p>LangGraph-powered supervisor coordinates eight specialized agents β each expert in a different phase of the data pipeline. The supervisor delegates entity extraction, function calling, and reasoning to your configured LLM.</p>
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<p>Data Concierge is LLM-agnostic by design. Run with frontier models from your preferred provider, open-weights models, or fully local LLMs deployed on-premises β keeping sensitive data and queries inside your security perimeter.</p>
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<p>Domain ontologies are derived automatically by combining statistical analysis β distributions, cardinality, type inference, correlations β with FAIRified metadata, in the context of the LLM's general world knowledge. Outputs map cleanly to well-known schemas and vocabularies (Schema.org, SKOS, DCAT, Dublin Core, PROV-O) and can be easily customized and tailored to your domain.</p>
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<p>Every answer ships as a Colab-ready .ipynb file. The full execution trace β API calls, computations, chart specs β is baked in so anyone can reproduce, verify, or extend your analysis.</p>
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<p>Can't find the right dataset? The CKAN agent combines LLM function calling with Pinecone semantic vector search to find datasets across thousands of open-data catalogs β federal, state, municipal, and international.</p>
</div>
<div class="feature-card fade-in">
<div class="feature-icon" aria-hidden="true" style="background:rgba(6,182,212,.12)">π</div>
<h3>Automatic Visualizations</h3>
<p>Vega-Lite chart specs are generated automatically for every result β time-series trends, geographic comparisons, distribution plots β and rendered directly in the output notebook.</p>
</div>
<div class="feature-card fade-in">
<div class="feature-icon" aria-hidden="true" style="background:rgba(245,158,11,.12)">π</div>
<h3>Government-Grade Citations</h3>
<p>Every data point is traceable. Citations include the source agency, dataset name, API endpoint, access date, and series identifier β formatted in both government and academic styles.</p>
</div>
<div class="feature-card fade-in">
<div class="feature-icon" aria-hidden="true" style="background:rgba(239,68,68,.12)">β‘</div>
<h3>Intelligent Caching</h3>
<p>Redis-backed cache with per-source TTLs (BLS: 30 days, Census: 90 days, NCSES: 365 days). Version-aware keys ensure cached data is never stale after API updates.</p>
</div>
<div class="feature-card fade-in">
<div class="feature-icon" aria-hidden="true" style="background:rgba(59,130,246,.12)">π―</div>
<h3>Confidence Scoring</h3>
<p>A five-factor weighted confidence model grades every answer: query interpretation, source authority, retrieval match quality, data recency, and computation reliability. Transparent, auditable scores.</p>
</div>
<div class="feature-card fade-in">
<div class="feature-icon" aria-hidden="true" style="background:rgba(16,185,129,.12)">π</div>
<h3>Tiered Processing</h3>
<p>Simple factual lookups are fully automated (Tier 1). Comparisons and trend analyses run a multi-step pipeline (Tier 2). Complex or low-confidence queries escalate to human review (Tier 3).</p>
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<div class="feature-card fade-in">
<div class="feature-icon" aria-hidden="true" style="background:rgba(139,92,246,.12)">ποΈ</div>
<h3>Session Memory</h3>
<p>Conversational sessions keep recent queries in context, enabling follow-up questions and iterative refinement without repeating yourself. Context depth and expiry policy are fully configurable per deployment.</p>
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<div class="feature-card fade-in">
<div class="feature-icon" aria-hidden="true" style="background:rgba(16,185,129,.12)">π₯</div>
<h3>Expert-in-the-Loop Curation</h3>
<p>Generated notebooks can be submitted to a GitHub-based review workflow where domain experts vet methodology and verify citations. Approved notebooks join an ever-expanding library of <strong>vetted, reproducible, hallucination-free deterministic answers</strong> β searchable, citable, and reusable across your organization.</p>
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</div>
</div>
</section>
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<section class="sources" id="data-sources">
<div class="container">
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<span class="badge badge-green">Data Sources</span>
<h2 style="margin-top:16px">Authoritative Datasets<br>at Your Fingertips</h2>
<p>Direct API integrations with the most trusted sources in official statistical data β and a pluggable connector architecture so any jurisdiction can extend the library with their own high-value datasets.</p>
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<div class="sources-grid">
<div class="source-card fade-in">
<div class="source-header">
<div class="source-logo" aria-hidden="true" style="background:rgba(6,182,212,.12)">π‘</div>
<div>
<div class="source-name">Data Commons</div>
<span class="badge badge-cyan" style="font-size:10px">Google</span>
</div>
</div>
<div class="source-desc">Google's unified knowledge graph aggregating statistical data from 100+ government and academic sources. Primary source for geo-resolved queries via Data Commons DCID system.</div>
<div class="source-meta">
<span class="badge badge-cyan">v2 API</span>
<span class="badge badge-cyan">7-day TTL</span>
</div>
</div>
<div class="source-card fade-in">
<div class="source-header">
<div class="source-logo" aria-hidden="true" style="background:rgba(59,130,246,.12)">πΌ</div>
<div>
<div class="source-name">Bureau of Labor Statistics</div>
<span class="badge badge-blue" style="font-size:10px">BLS</span>
</div>
</div>
<div class="source-desc">Official US employment, wages, inflation (CPI), and productivity statistics. Timeseries API with 500 requests/day with API key. Primary source for labor market queries.</div>
<div class="source-meta">
<span class="badge badge-blue">Timeseries</span>
<span class="badge badge-blue">30-day TTL</span>
</div>
</div>
<div class="source-card fade-in">
<div class="source-header">
<div class="source-logo" aria-hidden="true" style="background:rgba(16,185,129,.12)">ποΈ</div>
<div>
<div class="source-name">Census Bureau</div>
<span class="badge badge-green" style="font-size:10px">ACS Β· Decennial</span>
</div>
</div>
<div class="source-desc">Population, demographics, housing, and economic data from the American Community Survey and Decennial Census. Sub-county geographic granularity available.</div>
<div class="source-meta">
<span class="badge badge-green">ACS</span>
<span class="badge badge-green">90-day TTL</span>
</div>
</div>
<div class="source-card fade-in">
<div class="source-header">
<div class="source-logo" aria-hidden="true" style="background:rgba(245,158,11,.12)">π</div>
<div>
<div class="source-name">FRED</div>
<span class="badge badge-yellow" style="font-size:10px">St. Louis Fed</span>
</div>
</div>
<div class="source-desc">Federal Reserve Economic Data β 800,000+ economic time series. 60+ pre-mapped series IDs covering GDP, interest rates, money supply, financial conditions, and more.</div>
<div class="source-meta">
<span class="badge badge-yellow">60+ Series</span>
<span class="badge badge-yellow">Real-time</span>
</div>
</div>
<div class="source-card fade-in">
<div class="source-header">
<div class="source-logo" aria-hidden="true" style="background:rgba(139,92,246,.12)">π</div>
<div>
<div class="source-name">Bureau of Economic Analysis</div>
<span class="badge badge-purple" style="font-size:10px">BEA</span>
</div>
</div>
<div class="source-desc">GDP, national income, industry accounts, and international trade statistics from the BEA. Provides the authoritative measurement of the US economy.</div>
<div class="source-meta">
<span class="badge badge-purple">GDP Β· NIPA</span>
<span class="badge badge-purple">90-day TTL</span>
</div>
</div>
<div class="source-card fade-in">
<div class="source-header">
<div class="source-logo" aria-hidden="true" style="background:rgba(249,115,22,.12)">π‘οΈ</div>
<div>
<div class="source-name">FBI Crime Data</div>
<span class="badge" style="background:rgba(249,115,22,.15);color:#fb923c;border:1px solid rgba(249,115,22,.3);font-size:10px">CDE Β· UCR</span>
</div>
</div>
<div class="source-desc">Crime statistics, arrest data, hate crimes, NIBRS incidents, law-enforcement employment, LEOKA, and use-of-force from the FBI's Crime Data Explorer β covering both the Summary Reporting System (SRS) and the National Incident-Based Reporting System (NIBRS). Powered by our <a href="https://github.com/dathere/fbi-crime-data-mcp" style="color:#fb923c;text-decoration:underline">fbi-crime-data-mcp</a> server.</div>
<div class="source-meta">
<span class="badge" style="background:rgba(249,115,22,.15);color:#fb923c;border:1px solid rgba(249,115,22,.3)">NIBRS + SRS</span>
<span class="badge" style="background:rgba(249,115,22,.15);color:#fb923c;border:1px solid rgba(249,115,22,.3)">17 Tools</span>
</div>
</div>
<div class="source-card fade-in">
<div class="source-header">
<div class="source-logo" aria-hidden="true" style="background:rgba(239,68,68,.12)">ποΈ</div>
<div>
<div class="source-name">CKAN Open Data</div>
<span class="badge" style="background:rgba(239,68,68,.15);color:#f87171;border:1px solid rgba(239,68,68,.3);font-size:10px">data.gov</span>
</div>
</div>