This blog covers how Visier builds, operates, and evolves the technology behind its analytics and AI platform.
Visit the blog at engineering.visier.com.
Subscribe to the RSS feed at engineering.visier.com/feed.xml.
How Visier's semantic model turns time, relationships, concepts, and metrics into shared analytical context for human analysts and AI agents.
Semantic modeling | Temporal analytics | AI context
Part 3 and conclusion of the series on how Visier's cache-copy architecture preserves source-of-truth semantics and how test automation, code quality, and dependency discipline help keep Visier DB sustainable.
Cache-copy architecture | Security and governance | Engineering discipline
Part 2 of the series on how Visier ingests data as states and events, executes temporal analytics in Visier DB, and models metrics and cohorts as reusable time-aware concepts.
Event streams | Temporal queries | Metrics and cohorts
Part 1 of the series on why row-centric BI pipelines become fragile under schema drift, source churn, and temporal complexity, and how Visier's subject model addresses that problem.
Data modeling | Temporal analytics | Platform architecture
Visier builds AI-powered workforce intelligence. Its platform brings people and work data together so organizations can make better decisions across hiring, mobility, compensation, productivity, and organizational change.
The blog covers:
- Architecture and platform engineering
- Data modeling and analytics at scale
- AI and ML in people insights
- Security, privacy, and compliance
- Engineering culture and career growth
For product information or company news, visit Visier's public website.
Install the site dependencies into the repository so the local preview does not modify your global Ruby environment:
bundle config set --local path vendor/bundle
bundle installServe the GitHub Pages site locally from the docs/ directory:
"$(brew --prefix ruby@3.3)/bin/bundle" exec jekyll serve --source docsPreview the site at http://127.0.0.1:4000.