Building a Headless CMS for Numismatic Research: Auction Histories & Provenance Tracking

Let me tell you about building a headless CMS for numismatic research. It’s been one of my most fascinating projects – like giving a digital backbone to centuries of coin history. When I started creating a platform for tracking coin provenance, I quickly realized traditional CMS platforms just weren’t built for this kind of complex, interconnected data.

Why Headless CMS for Provenance Research?

Picture this: You’re trying to trace a 1916-D Mercury dime’s journey through time. It’s appeared in auctions at Heritage, Stack’s, and Goldberg. It’s been graded by PCGS, then re-graded by NGC. There are catalog scans, high-res images, and notes from dealers who handled it decades ago.

Traditional CMS systems? They choke on this complexity. A headless CMS with an API-first approach acts like a central hub for all this data. It keeps everything connected but separate from how we display it – whether that’s on a website, mobile app, or research tool.

Picking Your Headless CMS Platform

For numismatic work, I tested three platforms that each have their sweet spots:

Contentful: When You Need Serious Media Handling

Perfect if you have the budget and deal with lots of images. Here’s why it shines:

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• Handles high-resolution coin images like a champ

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• Lets you create custom content types that mirror real-world relationships (like coin → auction → certification)
• Keeps version history when provenance details change
• Global CDN makes those big image files load fast worldwide

Here’s how I modeled coin data in Contentful:

// Contentful content model for coin provenance
{
  "coin": {
    "fields": {
      "designation": "1905-O Dime (Blay)",
      "certifications": ["PCGS 35 6933.35/5732952"],
      "auctionHistory": ["relationship to auction collection"],
      "images": ["relationship to assets"],
      "provenanceNotes": "Long text field"
    }
  },
  "auction": {
    "fields": {
      "house": "Heritage/Stack's/Goldberg",
      "date": "date",
      "lotNumber": "string",
      "description": "text",
      "images": ["relationship to assets"]
    }
  }
}

Strapi: The Customization King

When I needed to build something tailored specifically for numismatics with limited funds, Strapi was my go-to. It let me:

• Create relationship fields that connect coins to their auction appearances and grading history
• Add validation rules to catch certification number typos
• Build a custom search that finds coins across multiple data points

My Strapi setup for coin data:

// Strapi coin collection type module.exports = {
  kind: 'collectionType',
  attributes: {
    designation: {
      type: 'string',
      required: true,
      unique: true
    },
    grade: {
      type: 'string',
      regex: /^(PCGS|NGC|ANACS)\s+\d{1,2}(\+?)$/
    },
    auctionReferences: {
      collection: 'auction',
      via: 'mintedCoins'
    },
    certNumber: {
      type: 'string',
      regex: /^[A-Z0-9\/]+$/
    }
  }
};

Sanity.io: For Real-Time Collaboration

When working with other researchers or AI tools, Sanity’s real-time features are golden. I use it to:

• Store AI-processed data from old auction catalogs
• See updates as soon as new auction records come in
• Create specialized input fields for things like pedigree notation

Getting Data In: From Paper to Pixels

Here’s where the real work happens. Turning scattered auction records into structured data took some clever solutions:

1. Scraping Digital Archives

For modern auction sites, I built scrapers to pull:

• Lot details and descriptions
• Certification numbers
• Grading information
• High-quality images

A quick look at my Heritage scraper:

const scrapeHeritageLot = async (lotUrl) => {
  const browser = await puppeteer.launch();
  const page = await browser.newPage();
  await page.goto(lotUrl);
  
  const lotData = await page.evaluate(() => {
    return {
      lot: document.querySelector('.lot-number').innerText,
      description: document.querySelector('.lot-description').innerText,
      grade: document.querySelector('.grade').innerText,
      certNumber: document.querySelector('.cert-number').innerText,
      imageUrl: document.querySelector('.lot-image img').src
    };
  });
  
  await browser.close();
  return lotData;
};
// Then send to CMS
await fetch('https://your-cms.com/api/auctions', {
  method: 'POST',
  body: JSON.stringify(lotData)
});

2. Processing Paper Records with AI

For those musty old catalogs? That’s where AI shines:

• Tesseract.js extracts text from scanned images
• Custom models recognize coin designations and grades
• GPT-style prompts turn messy descriptions into clean JSON

Example prompt I use:

Extract structured data from this auction description: “Lot 1234: 1905-O Dime, PCGS MS-65, Ex: Blay Collection, Previously Auctioned in 1999 Heritage Sale”. Return JSON with fields: designation, grade, cert, provenance, previousSale.

3. Capturing Human Expertise

No algorithm knows everything. I built a custom Strapi field where researchers can:

• Add their provenance insights
• Link to physical catalogs (shoutout to NNP integration)
• Keep private notes for their collections

Frontend That Doesn’t Slow You Down

With all that data in place, I built a frontend that loads fast and works well:

Next.js vs Gatsby: Why I Chose Next

Next.js won for handling large image collections. My setup:

• Next.js powers the main research interface
• Incremental Static Regeneration keeps auction data fresh
• Dynamic routes create individual pages for each coin (by certification number)
• Custom API routes handle complex searches

How I generate coin pages:

// pages/coins/[certNumber].js
export async function getStaticPaths() {
  // Get all certification numbers
  const certNumbers = await fetch('https://your-cms.com/api/coins?fields=certNumber');
  return {
    paths: certNumbers.map(cert => ({ params: { certNumber: cert } })),
    fallback: 'blocking'
  };
}

export async function getStaticProps({ params }) {
  const coin = await fetch(`https://your-cms.com/api/coins?certNumber=${params.certNumber}`);
  return { props: { coin }, revalidate: 3600 }; // Update hourly
}

Search That Actually Works

Finding specific coins means smart search:

• Elasticsearch indexes every auction description
• Custom filters for certification numbers, dates, grading services
• Image similarity search finds matching photos across sites

What I Learned the Hard Way

After months of building, these lessons stood out:

• Design your data model first: Map out how coins connect to auctions, certifications, and owners before importing anything.
• Automate the boring stuff: Scraping and AI extraction saved me weeks of manual work.
• Experts still matter most: No system replaces decades of numismatic knowledge. Make their insights easy to share.
• Speed is king: When you’re showing thousands of high-res images, Jamstack with proper optimization is non-negotiable.
• Stay flexible: The headless approach means I can add new sources and interfaces as needs change.

The Big Picture

This CMS isn’t just about organizing data – it’s about making coin history accessible. Some key takeaways:

• Contentful if you need robust media handling and have budget
• Strapi when you need to customize everything
• Sanity.io for real-time collaboration
• Jamstack + Next.js for speed with large image collections
• AI tools that cut through the manual data entry

The result? A living research tool that connects auction records, certification details, expert knowledge, and collector insights in one place. And while I built this for numismatics, the same approach works for any specialized research area with scattered historical data.

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