Thomas uses Databricks RAG and Vector Search to personalize psychometric assessments at scale
Thomas' paper-based psychometric assessment model could not scale as their customer base grew, and their legacy platform's enormous content library — built to cover every possible personalization iteration — made it extremely difficult to surface the right insights for each individual client.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User profile submission
A user profile is completed with Thomas every 90 seconds.
Tools used
Databricks Data Intelligence PlatformMosaic AIDatabricks Vector Searchretrieval augmented generation (RAG)natural language processing (NLP)LLMs
Outcome
Thomas integrated GenAI into three platforms in three months, went from proof of concept to MVP in weeks, and now delivers dynamic personalized insights through Vector Search rather than lengthy static reports, resulting in increased user satisfaction and deeper engagement.
What failed first
Thomas' previous approach relied on a labor-intensive model of manually training HR directors and hiring managers to interpret assessments, and a legacy content platform with billions of words covering every possible iteration that could not be efficiently personalized or connected to modern workplace applications.