HRPulsar measures AI fluency from real work, not self-reported confidence — inside an open-source talent platform you can audit and self-host.
Most companies still can't answer four questions about their own workforce:
Which skills does your team already have?
Titles and org charts don't say what people can actually do.
Which capabilities are missing?
Gaps surface as missed deadlines and failed hires — not as data.
Who is ready to work with AI?
Adoption is uneven, and self-reported confidence is not a signal.
How do you develop talent for what's next?
Without a skills baseline, development budgets are spent on guesses.
Without this foundation, scaling AI across the organization stalls.
The measurement sits inside a talent platform for AI-first companies: people, skills, hiring and development in one system. All of it ships today — open the tour or the demo and see for yourself.
Define & measure
Model the skills your company needs, then check them with assessments and exams.
Hire & mobilize
Bring demonstrated skills in from outside — or find them on teams you already have.
Develop & promote
Close the measured gaps and connect growth to a career ladder people can see.
The AI layer
Not a separate module — it works inside every module above.
Framework generation, semantic search and suggestions across every module.
AI Fluency is an observable skill, not self-reported confidence. HRPulsar measures it with a methodology based on Reid Hoffman's three levels.
Find capability gaps, compare teams, and build development programs that close them.
A measurement that can be verified and that travels with the person becomes a standard over time. A standard belongs to whoever holds the network behind it — and a network is the one thing that cannot be bought with a funding round. Three of the four below do not exist yet, and the cards say which.
An HR score dies with the HR system that issued it.
A level earned at one employer will travel with the person to the next — signed, and owned by the person it describes.
A claim on a CV is a claim.
A third party will check a stated level against our signed API, so the level can be verified rather than trusted.
A single company only sees itself.
The Hoffman 60 / 30 / 5 distribution across companies, published per industry once it clears the 20-tenant k-anonymity threshold.
AGPL gives the product away.
The core is live and self-hostable, and that is the distribution channel. What no single instance can build alone is the network above it — which is where the business will sit.
The network has no cohort today. It starts with the first design partners.
Budget without ROI. Hiring without a baseline. Software you cannot defend in review. Same Hoffman scale — different door in.
For CEO / Founders
Renewals run on anecdotes. Invoices do not say who compounds value — and neither do self-reviews.
For HRD
Self-reported confidence is not a hiring signal. Without a skills baseline, development budgets chase whoever asked loudest.
For CTO
If you cannot audit what the vendor measures, you cannot ship it. AGPL core you can self-host — same product as Cloud.
There will no longer be solo specialists working alone. Each of us will operate together with our own set of AI agents.REID HOFFMAN · LINKEDIN CO-FOUNDER · FEBRUARY 2026
Today HRPulsar manages the human side of an AI-first company. V2 extends the same skills-first core to AI agents — one workforce graph, a shared capability model, governed work routing.
Competencies, assessments, development, recruiting, grading, exams, talent market and analytics — open source, live in the product and the demo today.
Agent profiles next to people, work orchestration, quality controls and outcome analytics for hybrid teams — the roadmap we are building toward.
Spin up a sandbox preloaded with a sample team and its usage signals. No installs, no spreadsheets — see how the fluency map reads, which gaps it surfaces, and what a manager does and does not get to see. Your own data comes later, on your terms.
Before that: the signals we use, and the ones we refuse to collect →
Eight companies, six months, measuring the same thing the same way. They get the first numbers and a say in what gets built; we get a dataset nobody else has. That is the whole trade.
Free, self-hosted
Credits
