About Performix
Performance is a system.We make the system measurable.
Three questions, answered in order: where this is going, what runs today, and what actually makes it different. Every difference below is a stance we can show you in the product — not a claim we ask you to take on faith.
The platform
One system, three modules.
Performix has many parts — but they group into three. Each is built to work as one for Performix, and each is strong enough to stand on its own.
Clarix
The intelligent layer leaders actually touch. It does the reading for you — sorting every finding into a personal, ranked queue, answering questions in plain language, and meeting you on mobile.
- Per-user insight lists — the algorithm ranks what matters for you
- Ask AI — query your performance data in plain languageIn development
- Mobile-native delivery
Performance Library
The body of measurement science baked into the platform — validated metrics, peer-reviewed research, and pre-composed analyses. It's what makes every diagnosis research-grade from day one, and a product in its own right.
- The metrics library
- Peer-reviewed research substrate
- Pre-built analyses, ready to run
DevPlane
The AI-driven ETL layer that gets your data in. Agents connect your sources, map them to a common shape, and clear the integration board in days — not a months-long project.
- AI source connectors
- Automatic schema mapping
- The integration board, run by agents
The result you see
Open Performix and the answer is already there — one accountable card per team.
Show how it worksHide
The result you see
Open Performix and the answer is already there — one accountable card per team.
Acme Engineering — Platform Group: Support is the binding constraint.
Settled from 24 items · 11 respondents · 3-day window closing 2026-05-19
Support has been declining across three consecutive measurements, with the steepest drop on cross-functional response time. Capability and motivation are stable above 4.0; this is a system problem, not a talent problem.
Convene a 45-minute working session with engineering and product to identify the top three cross-functional handoffs introducing the most delay; assign a single owner per handoff with a two-week resolution target.
Owner: Priya Mehta · Target close: 2026-06-05 · Remeasure: Week 26
You don't build a dashboard or read a wall of metrics. Each team gets one settled card: which condition is holding performance back, the research behind it, the single recommended action, and when to remeasure. Findings play back like a queue — a now-playing surface, an up-next list, and the evidence one click underneath.
Short surveys that get sharper
The full science is thousands of questions. Nobody finishes that — so Performix asks only the few that matter for your team.
See the proofHide
Short surveys that get sharper
The full science is thousands of questions. Nobody finishes that — so Performix asks only the few that matter for your team.
A complete diagnostic would include every validated question in the research — thousands of them, and no employee would finish it. Instead, Performix asks the smallest set that reaches your team's load-bearing issue fastest: each person answers fewer than 20 questions, in five to ten minutes. And it adapts on every submission, not just between survey versions — so the questions sharpen as it learns, instead of waiting for the next redesign.
Where the questions come from
Every question traces back to peer-reviewed research — source, finding, construct, measure.
See the proofHide
Where the questions come from
Every question traces back to peer-reviewed research — source, finding, construct, measure.
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.
The questions aren't invented in-house. Every survey item traces through a chain — source paper, finding, construct, validated measure — back to a specific result in peer-reviewed industrial-organizational psychology and organizational behavior research. You can follow that chain yourself:
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
Getting your data in
Most analytics projects don't fail on the analytics. They fail on setup.
See the proofHide
Getting your data in
Most analytics projects don't fail on the analytics. They fail on setup.
Wired Acme Robotics into Performix.
Salesforce + Workday + Gong → Performix canonical schema. 11 mapping decisions, 2 human reviews, 9 auto-confirmed.
Support is the binding constraint for Acme's Platform Group.
Computed from: Salesforce activity (last 90d) + Workday tenure + Gong call sentiment. Cited research: Edmondson 2018, Hackman & Oldham 1976.
Every organization stores its data differently — different systems, different shapes, the same role measured a dozen ways. That's where enterprise analytics projects stall: getting the data in becomes its own project — a board full of integration tickets, a team working them, and a standup every morning to move them across. Months before anyone sees a result.
DevPlane runs that same board with AI agents instead of people — and they just get the work done, fast. The agents connect your sources, map them to a common shape, and clear the mapping decisions themselves. When one genuinely needs a human call, DevPlane drops you a simple form to answer — no ticket queue, no standup. The setup project becomes a run that finishes in days.
What you can run
A library of ready-made analyses — each one a single decision, not a dashboard to build.
See the proofHide
What you can run
A library of ready-made analyses — each one a single decision, not a dashboard to build.
Discover
8 Recipes
What do you want to learn?
Each Recipe is a packaged combination of inputs, analysis, and outputs your org can deploy as a single decision.
Revenue Variance Diagnostic
Why some sales teams hit and others miss — diagnoses the binding constraint across CAMS for each team.
Manager Support Heatmap
Cross-team comparison of manager-level Support scores across the sales org.
Quota-attainment Longitudinal
Track how CAMS conditions track against quota attainment over the last four quarters.
AI Adoption Readiness Diagnostic
Diagnoses whether the human system is ready to perform inside AI-transformed work.
Resistance Decomposition
When teams slow on AI rollout, decompose into capability, alignment, motivation, or support root cause.
Integration Performance Diagnostic
Detects where execution breaks during integration — across acquired and acquiring teams.
Talent Exit Risk Signal
Surfaces protected feedback themes correlating with departure intent during integration.
Binding-Constraint Pulse
The standard team-level CAMS diagnostic — pick a team, get one accountable action.
Beyond the standard team diagnostic, Performix ships a catalog of pre-composed analyses — each shaped for a specific question and deployable as a single decision. Filter by where it matters — sales performance, AI readiness, or post-acquisition integration.
Why it gets better
The five steps are one loop — and each turn sharpens the next.
Measuring, naming the binding constraint, acting, and remeasuring isn't a one-time report — it's a cycle. Every turn tells the model which questions mattered for your team and which didn't, so the next diagnosis is shorter and sharper. The system is worth more the longer you run it.
See it on your own teams.
What's our vision?
Measure performance as honestly as we measure money — in every kind of work, where performance means something different each time.
Leaders run the financial side of the business on instruments and the people side on instinct and best-practice lists. We're building the measurement layer for the people side — and extending it into every context where performance means something different. Sales isn't customer service isn't engineering isn't a hospital floor. We don't think you're all the same, so we build the diagnostic out one context at a time, until it applies — remarkably — almost anywhere.
Whichever problem brings you in, the diagnostic underneath is the same — one product, not three tools to stitch together.
That work is already underway. Each context we research, instrument, and verify becomes a profile you can see end to end. See the use cases.
What's the solution today?
The missing layer for managing performance — measurement science for leadership decisions, and it runs today.
A short, adaptive diagnostic finds which of four conditions — Capability, Alignment, Motivation, Support — is the one holding a team back. It recommends a single accountable action, schedules a remeasurement, and plays the result back over time. The questions come from peer-reviewed research, every number carries its source and its uncertainty, and the whole thing is packaged as Recipes you run for your situation. You don't have to take that on faith — you can watch it run.
The output — one settled card per team
Support is the binding constraint for the Platform Group.
Support is starving primarily on two subconstructs: cross-functional response time and tooling fit. The capability and motivation evidence is strong; alignment is within normal range.
Convene a 45-minute working session with engineering and product to identify the top three cross-functional handoffs introducing the most delay; assign a single owner per handoff with a two-week resolution target.
Remeasure Support in 4 weeks (12 items, 8 min).
The playback — insights queued like a music player
Acme Engineering — Platform Group: Support is the binding constraint.
Settled from 24 items · 11 respondents · 3-day window closing 2026-05-19
Support has been declining across three consecutive measurements, with the steepest drop on cross-functional response time. Capability and motivation are stable above 4.0; this is a system problem, not a talent problem.
Convene a 45-minute working session with engineering and product to identify the top three cross-functional handoffs introducing the most delay; assign a single owner per handoff with a two-week resolution target.
Owner: Priya Mehta · Target close: 2026-06-05 · Remeasure: Week 26
What makes us genuinely different?
Seven things only we do — each a stance, materialized in the product.
These aren't feature bullets. Each one is a philosophical choice about how performance should be measured, made real in something you can see and run.
01
We find the one thing, not forty.
Performance is conjunctive: when any one of the four conditions is starving, performance follows — so the fix is the starving condition, and the rest is noise. Most tools hand you a dashboard and a best-practice list. We refuse the list by design and name one accountable action against the binding condition.
See the proofHide
01
We find the one thing, not forty.
Performance is conjunctive: when any one of the four conditions is starving, performance follows — so the fix is the starving condition, and the rest is noise. Most tools hand you a dashboard and a best-practice list. We refuse the list by design and name one accountable action against the binding condition.
02
The measurement is real, not generated.
The math is psychometrics — adaptive item selection, value-of-information, confidence intervals — read off the research, not produced on demand by a language model. AI did the slow work of turning the literature into runnable math; it is a consumer of that math, not its author. That boundary is the whole differentiator.
See the proofHide
02
The measurement is real, not generated.
The math is psychometrics — adaptive item selection, value-of-information, confidence intervals — read off the research, not produced on demand by a language model. AI did the slow work of turning the literature into runnable math; it is a consumer of that math, not its author. That boundary is the whole differentiator.
03
Every number shows its receipts.
Every survey item traces a chain — source paper, finding, construct, validated measure — and every estimate is reported with its uncertainty. A measured constraint is falsifiable; a favored narrative is not. You can follow any number back to the paper it came from.
See the proofHide
03
Every number shows its receipts.
Every survey item traces a chain — source paper, finding, construct, validated measure — and every estimate is reported with its uncertainty. A measured constraint is falsifiable; a favored narrative is not. You can follow any number back to the paper it came from.
The research, 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
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.
04
It asks the fewest questions that work — and sharpens every time.
A complete diagnostic would include every validated question in the research — thousands of them, and nobody would finish it. Instead the diagnostic asks the smallest set that reaches your team's load-bearing issue: fewer than 20 questions per person, in five to ten minutes. And it adapts on every submission, not just between survey versions — the value of the next question decides whether it's worth asking.
See the proofHide
04
It asks the fewest questions that work — and sharpens every time.
A complete diagnostic would include every validated question in the research — thousands of them, and nobody would finish it. Instead the diagnostic asks the smallest set that reaches your team's load-bearing issue: fewer than 20 questions per person, in five to ten minutes. And it adapts on every submission, not just between survey versions — the value of the next question decides whether it's worth asking.
05
Insights are ready before you open them.
Performix isn't a dashboard you assemble. Open it and the insights are already there — computed on a schedule, scored, and privacy-checked — so the executive view loads instantly and stays consistent for everyone looking at it. Computed once, played back to whoever needs it.
See the proofHide
05
Insights are ready before you open them.
Performix isn't a dashboard you assemble. Open it and the insights are already there — computed on a schedule, scored, and privacy-checked — so the executive view loads instantly and stays consistent for everyone looking at it. Computed once, played back to whoever needs it.
Ready when you open it
Computed and privacy-checked ahead of time — so the views you open are instant, and read-only, so everyone sees the same numbers.
06
Feedback lives in the work, and it's protected.
The same lightweight rating shows up wherever you need it — on a section of a document, on a statement about your team, on whether an intervention fits — never in a separate survey. Responses are anonymized and gated by a minimum count, so the lens can't be aimed at any individual. Feedback becomes part of the work instead of running parallel to it.
See the proofHide
06
Feedback lives in the work, and it's protected.
The same lightweight rating shows up wherever you need it — on a section of a document, on a statement about your team, on whether an intervention fits — never in a separate survey. Responses are anonymized and gated by a minimum count, so the lens can't be aimed at any individual. Feedback becomes part of the work instead of running parallel to it.
FEEDBACK, WHEREVER YOU WORK
The same rating lives wherever you work — on a document, on a CAMS statement, on whether an intervention fits. You answer in place; there's never a separate survey.
07
We don't think you're all the same.
One engine underneath — but the definition of performance differs by context, so we research and build each one out instead of shipping a generic tool and asking you to map it onto your world. This is the difference that becomes the vision: a tool that earns the right to say it understands your context, one context at a time.
See the proofHide
07
We don't think you're all the same.
One engine underneath — but the definition of performance differs by context, so we research and build each one out instead of shipping a generic tool and asking you to map it onto your world. This is the difference that becomes the vision: a tool that earns the right to say it understands your context, one context at a time.
One engine — filtered to where performance breaks for you
Discover
8 Recipes
What do you want to learn?
Each Recipe is a packaged combination of inputs, analysis, and outputs your org can deploy as a single decision.
Revenue Variance Diagnostic
Why some sales teams hit and others miss — diagnoses the binding constraint across CAMS for each team.
Manager Support Heatmap
Cross-team comparison of manager-level Support scores across the sales org.
Quota-attainment Longitudinal
Track how CAMS conditions track against quota attainment over the last four quarters.
AI Adoption Readiness Diagnostic
Diagnoses whether the human system is ready to perform inside AI-transformed work.
Resistance Decomposition
When teams slow on AI rollout, decompose into capability, alignment, motivation, or support root cause.
Integration Performance Diagnostic
Detects where execution breaks during integration — across acquired and acquiring teams.
Talent Exit Risk Signal
Surfaces protected feedback themes correlating with departure intent during integration.
Binding-Constraint Pulse
The standard team-level CAMS diagnostic — pick a team, get one accountable action.
Each context gets researched, instrumented, and verified before it earns a profile. See the use cases
Key concepts
The handful of ideas the rest of it rests on.
CAMS — the foundational model
Capability, Alignment, Motivation, Support — the four conditions performance is built on.
The four conditions are conjunctive: when any one is starving, performance follows. The diagnostic's whole job is to find which one — for your team, right now — and point resource only there.
Show the diagramHide
CAMS — the foundational model
Capability, Alignment, Motivation, Support — the four conditions performance is built on.
The four conditions are conjunctive: when any one is starving, performance follows. The diagnostic's whole job is to find which one — for your team, right now — and point resource only there.
Three teams · three remedies
Same instrument, different teams, wildly different recommendations — because the binding constraint is different for each.
Show the diagramHide
Three teams · three remedies
Same instrument, different teams, wildly different recommendations — because the binding constraint is different for each.
The roster, auto-mapped
The diagnostic is only as good as the roster behind it — so the roster is auto-mapped with per-column confidence and an override.
On first onboarding, the operator uploads a CSV from their HRIS. Performix infers the mapping to the canonical schema column-by-column, scores each inference, and opens up the reasoning — name similarity, value pattern, header position — so the operator can override before anything downstream runs. No black-box pipeline.
Show the diagramHide
The roster, auto-mapped
The diagnostic is only as good as the roster behind it — so the roster is auto-mapped with per-column confidence and an override.
On first onboarding, the operator uploads a CSV from their HRIS. Performix infers the mapping to the canonical schema column-by-column, scores each inference, and opens up the reasoning — name similarity, value pattern, header position — so the operator can override before anything downstream runs. No black-box pipeline.
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.emailWhere decisions get made
Built mobile-native, so the diagnostic lands where leaders actually decide — not only at a desk.
Show the diagramHide
Where decisions get made
Built mobile-native, so the diagnostic lands where leaders actually decide — not only at a desk.
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 →