Detecting Tech Stacks at Scale for B2B Prospecting

If you sell developer tools, SaaS infrastructure, or B2B services, knowing what technology a prospect already uses tells you whether they’re a fit before you ever send a cold email.

A company running Shopify, Klaviyo, and Facebook Pixel is a different buyer than one running Magento with Salesforce and Marketo. Their budget, maturity, and needs are totally different.

Tech stack detection — scanning websites to infer the technologies they use — turns cold outreach into informed, personalized sales.

How tech stack detection works

Most web technologies leave detectable fingerprints:

• Script tags referencing CDN URLs (e.g., googletagmanager.com, klaviyo.com/media/js)
• Meta tags (e.g., generator: WordPress)
• HTTP response headers (e.g., X-Powered-By: Express)
• Cookie names (Shopify uses _shopify_y)
• URL patterns (/wp-content/ = WordPress)
• DNS records (MX records reveal the email provider)
• robots.txt and sitemap.xml structure

Combining these signals gives a high-confidence detection of 500+ technologies.

The API call

curl -X POST https://api.seek-api.com/v1/workers/tech-stack-detector/jobs \
  -H "X-Api-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example-ecommerce-store.com"}'

Response:

{
  "url": "https://example-ecommerce-store.com",
  "technologies": [
    { "name": "Shopify", "category": "eCommerce", "confidence": 0.97 },
    { "name": "Klaviyo", "category": "Email Marketing", "confidence": 0.91 },
    { "name": "Meta Pixel", "category": "Advertising Analytics", "confidence": 0.95 },
    { "name": "Google Analytics 4", "category": "Analytics", "confidence": 0.93 },
    { "name": "Gorgias", "category": "Customer Support", "confidence": 0.88 },
    { "name": "Cloudflare", "category": "CDN", "confidence": 0.99 }
  ],
  "emailProvider": "Google Workspace",
  "cms": "Shopify",
  "adPlatforms": ["Meta", "Google Ads"]
}

Scale this across a prospect list

import httpx, csv, time

with open("prospects.csv") as f:
    prospects = [{"company": row[0], "url": row[1]} for row in csv.reader(f)]

# Submit all jobs concurrently
job_ids = []
for p in prospects:
    resp = httpx.post(
        "https://api.seek-api.com/v1/workers/tech-stack-detector/jobs",
        headers={"X-Api-Key": API_KEY},
        json={"url": p["url"]}
    )
    job_ids.append({"company": p["company"], "job_uuid": resp.json()["job_uuid"]})

# Poll until all complete
results = []
for item in job_ids:
    while True:
        status = httpx.get(
            f"https://api.seek-api.com/v1/jobs/{item['job_uuid']}",
            headers={"X-Api-Key": API_KEY}
        ).json()
        if status["status"] == "completed":
            results.append({**item, "stack": status["result"]["technologies"]})
            break
        time.sleep(3)

In a few minutes, you’ve enriched your entire prospect list with technology data.

Filtering and scoring prospects

Once you have tech stacks, filter by ICP criteria:

def is_ideal_prospect(result):
    techs = [t["name"] for t in result["stack"]]
    return (
        "Shopify" in techs and          # Ecommerce platform we integrate with
        "Klaviyo" not in techs and      # Doesn't use competitor
        "Meta Pixel" in techs           # Runs ads (has budget)
    )

qualified = [r for r in results if is_ideal_prospect(r)]
print(f"{len(qualified)} qualified prospects out of {len(results)} total")

Technology signals and what they mean

Building a trigger-based outreach system

Combine tech stack detection with regular re-scans to catch when a prospect migrates platforms:

1. Scan target companies weekly
2. Detect when a company moves from Shopify → Magento, or adds Salesforce
3. Trigger outreach immediately: “We noticed you just adopted X — we integrate with it seamlessly”

Platform migration moments are perfect sales windows. The company is already in change mode.

Cost efficiency

Tech stack scanning costs ~$0.005 per domain. Enriching 10,000 prospects = $50 in API costs. No BuiltWith subscription needed.

For most sales teams, a $50 spend replacing a $299/month tool is a clear win — especially when the data feeds directly into your pipeline automation.