From job boards to LLM corpora: structured data for talent analytics and AI

Talent data and AI training data sit at opposite ends of the web-data spectrum — one is small, structured and high-precision; the other is massive, messy and provenance-sensitive. We build both. Here's how they differ.

Jobs & talent data

Aggregating hiring data across many boards — Indeed, LinkedIn Jobs, Glassdoor, StepStone and regional players — sounds like a scraping job. The real work is afterward:

• Deduplication — the same role is posted on five boards.
• Normalization — titles, locations, seniority and salary into one schema.
• Comp benchmarking — percentile bands by role and region, refreshed continuously.

Clients use it for wage analytics, skill-demand forecasting, and sourcing tools.

AI training corpora

Training data is a volume-and-cleanliness problem, plus a provenance one. Building a usable corpus means:

• Crawl & clean — boilerplate stripping, language detection, dedup (minhash / simhash).
• Provenance — per-document source URL and fetch timestamp, kept with the data.
• Licensing awareness — license flags, robots/ToS respect, and takedown handling.
• Delivery — Parquet on S3 with a documented schema, not a pile of HTML.

Different shapes, same discipline: clean inputs, validated outputs, and honesty about what the data is and isn't.