Matching resumes to job descriptions requires more than keyword overlap. Here's how to build a multi-signal matching workflow that handles thousands of candidates and hundreds of roles.
Most AI recruiting consultants match candidates with a mix of spreadsheets, Python scripts, and API calls. Here's how to move from fragile one-off workflows to repeatable matching operations.
ChatGPT, Gemini, Claude, and other LLMs can absolutely do fuzzy matching. They're just not built for it. Here's what works, what doesn't, and when you need a dedicated matching tool.
Deterministic matching compares exact values. Probabilistic matching uses statistics, embeddings, and LLMs to find likely matches. Here's how each works, where each fails, and how combining them produces faster, cheaper, more accurate results.
How to handle the N x M explosion in record matching — blocking strategies, pre-filter cascades, batch processing, and fault tolerance for large datasets.
Number of matches is not a quality metric. Learn how to measure precision, recall, and F1 score for data matching — including practical sampling methods when you don't have labeled data.
Matching slows down fast at scale. Learn how blocking keys reduce comparisons by orders of magnitude, how to choose effective keys, and how multi-pass blocking recovers missed pairs.
Duplicate mailers waste money and annoy recipients. Learn how to match across mailing lists, standardize addresses for deliverability, and build a clean send file.
Scraped data from multiple platforms contains the same entities represented differently. AI-powered record matching connects job postings, product listings, reviews, and properties across sources to build a unified competitive intelligence picture.
Catch migration errors before go-live. Learn how to match source and target records, check for data loss, detect transformation errors, and build a migration validation report.
Transaction reconciliation involves matching records across systems with amount differences, date offsets, and description mismatches. A step-by-step guide for finance teams.
Duplicate vendor records cause overpayments, missed discounts, and audit findings. Learn how to match vendor records across ERPs and maintain a clean vendor master.
A systematic approach to comparing two datasets — finding overlapping records, identifying gaps, detecting conflicting values, and building a comparison report.
From pandas merge to fuzzywuzzy to recordlinkage — a practical guide to CSV matching in Python, plus a framework for deciding when custom code stops making sense.
Excel's Remove Duplicates misses most real-world duplicates. Learn field-by-field fuzzy deduplication, threshold tuning, and strategies for CSV files with 100K+ rows.
VLOOKUP and INDEX-MATCH break when data is messy. Learn how to merge two spreadsheets with fuzzy matching, AI-powered joins, and practical validation steps.
A comparison of rule-based and AI embedding approaches to record matching — strengths, weaknesses, costs, and why the best systems use both.
A practical decision guide for selecting matching algorithms based on data type, quality, and scale — from simple name matching to multi-field entity resolution.
Addresses are the hardest field to match. Abbreviations, unit numbers, directionals, and international formats make exact matching useless. Here's how to handle them.
These five common record matching errors don't throw exceptions or show warnings. They just quietly produce bad results. Here's how to identify and fix each one.
80% of matching improvement comes from basic data cleaning. Here are the specific steps — with before and after examples — that transform garbage-in matches into reliable results.