Interzoid

@Interzoid check if these company names match: 'International Business Machines', 'IBM Corp', 'I.B.M. Corporation'

All three names resolve to the same entity with a match confidence of 94%. The algorithm normalized variations in punctuation, legal suffixes, and abbreviations to identify them as the same organization. This matching uses Interzoid's similarity key generation, which clusters semantically identical records even when string formatting differs significantly.

This example shows fuzzy matching for deduplication workflows. Requires an API key with access to the company name matching endpoint. Match confidence thresholds are configurable but not shown here — production use should define acceptable confidence floors based on your data quality needs.

@Interzoid generate a similarity key for the address '123 Main Street, Suite 4B, New York NY 10001' so I can find duplicates in our CRM

Generated similarity key: `ADR-8F3E2A1C9B7D`. This key normalizes the address by stripping suite numbers, standardizing abbreviations (Street → St), and geocoding the ZIP. Any address that resolves to the same physical location will produce an identical key, enabling you to GROUP BY this value in SQL to surface duplicate customer records.

Similarity keys are the core primitive for matching workflows — they turn messy text into deterministic identifiers. This example assumes the address normalization API is enabled on your plan. Keys are stable across API calls but may change if Interzoid updates its normalization rules during major version releases.

@Interzoid I have 500 email addresses in a CSV — can you identify which domains are from disposable email providers so I can flag them for review?

This would require iterating through each email's domain and checking it against Interzoid's disposable email detection database. For a 500-row batch, expect ~30 seconds of processing time due to per-request API latency. The MCP would return a list of flagged emails with disposable provider names (e.g., 'tempmail.com', '10minutemail.net'). Consider caching results by domain to avoid redundant lookups.

Batch operations like this hit rate limits quickly — Interzoid's free tier allows 100 requests/day. This example demonstrates validation use cases but requires either a paid plan or chunked processing with delays. The MCP does not currently support native batch endpoints, so each email is a separate API call.

When Interzoid makes sense for cleaning messy CRM data

A 6-person sales ops team inherits 40,000 customer records after a merger. Half the entries are duplicates with slight variations in company names, addresses, or contact info. Interzoid's data-matching algorithms can identify fuzzy duplicates that simple string comparison misses—'IBM Corp' vs 'International Business Machines' vs 'I.B.M.'—without writing custom normalization logic. The API_KEY auth means any team member can run deduplication jobs from Switchy without waiting on engineering. This works best when you're cleaning a dataset once or twice a quarter. If you need real-time deduplication on every form submission, you'll want a native CRM integration instead. For batch cleanup jobs under 100k records, Interzoid saves 10-15 hours of manual review per run.

How Interzoid fits into route optimization workflows

A 3-person logistics coordinator team at a regional delivery service plans routes every Monday morning. They paste 200-300 delivery addresses into a spreadsheet, but 15-20% have typos, missing zip codes, or ambiguous street names. Interzoid can standardize and validate those addresses in bulk before the routes get locked in. The team runs the validation from Switchy, flags the problem rows, and fixes them in 20 minutes instead of discovering bad addresses mid-route. This scenario works when address quality directly impacts your schedule and you have a human in the loop to review flags. If you're validating addresses at checkout in a live app, you need a lower-latency solution. For weekly batch validation under 500 addresses, Interzoid cuts route planning errors by half.

When Interzoid helps e-commerce teams merge supplier feeds

A 4-person e-commerce merchandising team aggregates product data from 12 different suppliers. Each supplier uses different naming conventions—'TV 55in 4K' vs '55-inch Ultra HD Television' vs '55" UHD TV'—making it impossible to detect duplicate listings automatically. Interzoid's matching algorithms can cluster similar product names and flag likely duplicates for manual review. The team runs a weekly sync in Switchy, reviews the flagged clusters, and merges duplicates before publishing to the storefront. This works when your catalog changes weekly and you have 500-5000 SKUs. If you're syncing 50,000+ products daily, the API rate limits and manual review step become bottlenecks. For mid-size catalogs with weekly updates, Interzoid prevents 30-40 duplicate listings per month.