From AI scraping to AI matching — building the data pipeline for competitive analysis
AI scraping collects cleaner data than rule-based crawlers. AI matching processes it beyond what string comparisons allow. Here is how the full stack works.
AI talent matching as a service: the infrastructure gap holding consultants back
Talent matching consultants spend more time building pipelines than matching candidates. Configurable matching infrastructure changes the math.
Building a candidate-to-job matching workflow that actually scales
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.
Scaling your AI recruiting practice beyond spreadsheets and scripts
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.
AI extraction vs AI enrichment: how structured data gets pulled from files
Extraction produces one column from a file. Enrichment produces many. Understanding the difference — and when to use each — determines whether your matching pipeline gets the right signals.
Extracting structured data from home inspection reports and insurance documents
Inspection reports and insurance documents are PDFs full of structured data — room-by-room condition ratings, damage photos, repair estimates, coverage details. AI extraction turns them into matchable records.
Matching real estate listings with photos: how AI reads property images across platforms
Addresses differ, MLS numbers don't transfer, and square footage disagrees. Listing photos show the same kitchen in both datasets. AI extraction turns property images into matchable attributes.
From string comparisons to contextual reasoning: how AI transformed data matching
Data matching evolved from rigid rules to machine learning to neural embeddings to LLMs. Each generation solved problems the previous one couldn't. Here's how the technology progressed, what each approach actually does, and why modern systems layer all of them.
Can ChatGPT do fuzzy matching? Yes — but here's where it breaks down
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 vs probabilistic matching: what they are, when to use each, and why the best systems use both
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.
Can Gemini read PDFs? Can ChatGPT understand documents? Yes — here's how AI classifies PDFs
LLMs like Gemini, ChatGPT, and Claude can read PDFs, understand tables, extract text from images, and interpret graphs. Here's how multimodal AI enables granular PDF document classification — and where it still needs help.
Why SQL and pandas can't accurately match retail products — and what can
SQL JOINs and pandas merges fail on color variants, promotional naming, translated descriptions, and spec formatting differences. AI embeddings and LLMs understand that 'Midnight' means black and 'Violet' means purple. Here's why traditional tools hit a ceiling and how hybrid pipelines break through it.
Extracting matchable attributes from product images: beyond basic categorization
Product images contain brand names, model numbers, colors, and condition details that aren't in your spreadsheet. AI attribute extraction turns visual information into structured fields ready for matching.
Image categorization at scale: from folders of photos to structured data
Thousands of images sitting in folders with meaningless filenames. AI image categorization extracts structured labels, categories, and descriptions — turning visual assets into matchable data.
Matching with images and attributes: the complete file-based matching workflow
Text matching misses products that look identical but are described differently. File-based matching adds images, PDFs, and documents to the comparison — combining visual and textual signals for accurate results.
Extracting structured data from PDFs: categorization, attributes, and matching
PDFs contain structured information trapped in unstructured format. AI extraction turns invoices, contracts, reports, and spec sheets into matchable data rows — no manual data entry required.
AI embeddings vs rule-based matching: when to use each
A comparison of rule-based and AI embedding approaches to record matching — strengths, weaknesses, costs, and why the best systems use both.