About Performix
Performance is a system.We make the system measurable.
Performix is a protected-feedback and performance-intelligence platform. The diagnostic is real. The substrate is research-grade. The system is built to learn — narrowing in on what matters for your team rather than running every team through the same laundry list.
CAMS — the foundational model
Performance is conjunctive. Find the one starving condition. Apply resource only there.
Capability, Alignment, Motivation, and Support are conjunctive — when any one is starving, performance follows. The diagnostic refuses the laundry list of “best practices” by design. The recommendation is one accountable action against the binding condition.
Three doors · one engine
Same diagnostic kernel. Three buyer conversations. The doors change; the product doesn't.
The same diagnostic kernel powers each vertical. The doors change the buyer conversation, not the product.
Architecture · precompute and playback
Metrics are computed once, on a schedule. The player never recomputes — it plays back insights already stored.
Precompute and Playback
Metrics are computed once, on a schedule. The player never recomputes — it plays back insights already stored.
Performix is not a dashboard you build. It is a player that plays back insights that have already been computed, scored, and suppression-checked upstream. The wall between compute and render is load-bearing — it is what makes the player feel inevitable and the executive view trustworthy.
Why now
The substrate is the product. AI did the ingest; the substance came from the literature.
WHY THIS IS POSSIBLE NOW
Pre-LLM, ingesting the I-O psychology literature into a structured measurement substrate was uneconomical. Now it isn't. The diagnostic substrate is the AI product; the rest is craft.
Every survey item in Performix traces back through a chain — source paper, finding, construct, validated measure — to a specific finding in peer-reviewed I/O psychology and organizational behavior research. Pre-LLM, ingesting that literature into a structured measurement substrate was uneconomical. Now it isn't. That is the AI claim.
The substrate, made browseable
Performance Science Library
Every evidence pill in Performix traces back through this library. Source papers and books are ingested by the Research-to-Model Engine; constructs, validated measures, and survey items are derived from them and weighted by evidence strength. Browse the live library →
Edmondson, A. C. (2018). The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth. Hoboken, NJ: Wiley.
Ingested 2026-03-14 · 23 findings extracted · 14 constructs derived · 41 survey items derived
Evidence weight: 0.84 · IRT difficulty range: -1.2 to +0.6 · last calibration: 2026-05-04
System of learning
Start with the minimum load-bearing instrument. Narrow in fast. Pay for depth only where it moves the needle.
Performix starts with the minimum load-bearing instrument — CAMS — and probabilistically selects what to ask next based on what your team's answers have already revealed. Deeper analytic tracks are gated by the expected value of the information they would yield. This is what the engine looks like running.
Three teams · three remedies
Same instrument. Different teams. Wildly different recommendations — because the binding constraint is different for each.
Design discipline
Don't build separate survey forms. Feedback is inherent to documents.
ONE PRIMITIVE · THREE USES
Feedback is inherent to documents. The same widget rates a job spec section, a CAMS item, and an intervention's fit — never a separate survey form.
One primitive backs three uses across Performix: rate a doc section, rate your team on a CAMS statement, rate an intervention's fit. The widget never displaces — once you answer, the picked choice highlights and a “change?” affordance stays in place. This is how feedback becomes part of the work instead of running parallel to it.
The widget, in flow
Binding-constraint diagnostic
Rate each statement for your team. As your responses come in, Performix identifies which of Capability, Alignment, Motivation, or Support is the dimension currently starving your team's performance — so you can apply resource where it actually moves the needle, rather than treating every dimension at once.
Team: Acme Engineering — Platform Group · Priya Mehta · 11 members
This team has the technical skills required to deliver on its current commitments.
Answered: 4 — Mostly true ·
This team has the time required to do its work to the standard the team itself wants to meet.
This team can get answers from other teams quickly when blocked.
Consumer · not fork
Performix vendors typed contracts of six analytical services. The algorithms live elsewhere.
When a service updates, Performix benefits with zero code change here. The boundary is enforced by typed contracts vendored at known versions — the same pattern Performix then exports to downstream consumers like the public consulting site and the AI-encyclopedia surface.
Operator surface
The diagnostic is only as good as the roster behind it. The roster is auto-mapped with per-column confidence.
Map columns to the canonical schema
We've inferred a mapping for each column in acme-platform-roster.csv from the column name, sampled values, and the canonical schema. Confirm or override; unmapped columns will be ignored.
| Canonical field | Source column | Sample values | Confidence | Status |
|---|---|---|---|---|
employee.id textRequired | E10481E10482 | 96% | Mapped | |
employee.fullName textRequired | Priya MehtaJordan Lee | 94% | Mapped | |
manager.email emailRequired | priya.mehta@acme.examplej.lee@acme.example | 91% | Mapped | |
employee.jobTitle textRequired | Senior Software EngineerEngineering Manager | 89% | Mapped | |
employee.department text | PlatformInfrastructure | 88% | Mapped | |
employee.hireDate dateRequired | 2022-03-142021-08-02 | 93% | Mapped | |
employee.location text | NYCRemote-US | 72% | Mapped | |
employee.employmentType text | FTFT | 81% | Mapped |
MgrEmail → manager.emailThe first time a team is onboarded, the operator uploads a CSV from their HRIS. Performix infers the mapping to the canonical schema column-by-column, surfaces a confidence score for each inference, and opens up the reasoning — column-name similarity, value pattern, header position — so the operator can override before anything downstream runs. No black-box pipeline.
Working with us directly
Some organizations engage Performix as a tool. Others engage the practice that built it — to run the diagnostic with you, interpret the result, and design the intervention. See the consulting engagement shape →