Korpora measurement · WorkWhile · on-demand staffing
Ask AI which staffing app to use.
It names WorkWhile 7.4% of the time.
WorkWhile leads on-demand staffing on Hacker News, Reddit, and X by a wide margin. The models buyers actually ask have not caught up. That gap is the work, and it closes before the next training cycle locks the answer in.
- 7.4%
- Unprompted AI recall
- names WorkWhile without being told to
- 87.0pp
- Cold-start spread
- organic vs. forced-mention gap
- 3.9×
- Hacker News velocity
- post-cutoff mention ratio
WorkWhile · public dashboard
WorkWhile's AI mindshare, live
3 headline views
v3 snapshot · 2026-05-28
Where WorkWhile stands in the answers AI assistants give buyers, measured against its on-demand staffing cohort. Tab through the findings; each number is real, pulled from the v3 run on 2026-05-28, and re-pulled weekly on a paid engagement.
Action cards
What to ship before the next training corpus closes.
Seven cards, ordered by qualitative lift potential (cold-start evidence weight × ship feasibility × time-to-corpus). Each carries a paste-ready engineer prompt. AI assistants index operator discourse on a lag, so the signal shipped now is building toward the cohort after next, roughly 6–12 months out. Severity tags encode lift-potential, not urgency.
Active
7 cards · 0 shipped
In-flight and queued. Every card is an open recommendation today; on a paid engagement they push into the team's tools and ship against this board.
Shipped
0 cards executed
Public build history of the WorkWhile engagement. Empty because this is a prospective measurement and nothing has shipped yet. The slot is wired, not decorative.
No cards executed yet
The first executed card appears here the minute it pings back through the protocol above.
How we work
Two steps. You are on step one.
Korpora does not run these for everyone. WorkWhile cleared the internal fit review (scale, vertical, AI-channel relevance, execution capacity), so Korpora ran the measurement and shipped this dashboard. There was nothing to request and nothing to buy. What happens next comes down to one thing: whether the measurement in front of you earns the engagement.
- 1
Prospective measurement (you are here)
This is it. Korpora ran the full cross-channel measurement on WorkWhile and shipped the result as the dashboard you are reading: cross-stream findings, ranked action cards (each with a paste-ready engineer prompt and a brand-risk callout), full methodology disclosure, and the Ask Korpora chat for follow-up questions. Run once, on us. Nothing was requested and nothing is owed. You read it and decide whether the measurement earns the engagement. See /ssense and /eight-sleep for the same treatment on other brands.
- 2
Paid engagement (what continuing looks like)
When the measurement earns it, continuing is a paid engagement scoped to WorkWhile's cohort and depth: weekly re-measurement across every stream and every model, Western and Chinese, fresh action cards as the surfaces move, an alert the moment a rival accelerates into the next training corpus, and an operator who can run the moves alongside your team. There is no fixed self-serve tier; every cohort prices differently. Apply with a work email and we come back with scope and pricing, no call required to get the number.
Ask Korpora
Tell Korpora what you need
Methodology
How we measure, in full.
Every number on this page traces back to one of the streams below. Expand any section for the method, the headline-metric definitions, the live streams themselves, and the v3 caveats.
1 · How we measure
How each number on this page is produced. Every stream re-runs weekly on a paid engagement:
- AI assistant recall: buyer-decision queries (subject-blind and subject-named) run through frontier models, 108 cells this cycle, Wilson 95% confidence range on every share. Organic (unprompted) reported separately from evaluation (named).
- Operator-discourse velocity: earned brand mentions across the operator-discourse surfaces models read most for this category (Hacker News, Reddit, X), with recent publication velocity measured against a matched pre-cutoff window. Hacker News via Algolia, Reddit via public JSON, X via Xquik.
- Demand axis (not yet pulled): branded AI search volume per brand, the demand-side counterpart to the supply-side mindshare battery. From DataForSEO AI search volume. Not run for WorkWhile in v3; the next measurement to add.
2 · How to read the headline metrics
- Organic owned share (7.4% [Wilson 95% CI 2.9–17.6], the metric that drives action)
- WorkWhile was surfaced by AI without being named in the prompt, in 4 of 54 subject-blind cells (open-ended discovery + use-case questions). This is unprompted recall: a leading proxy for AI-driven discovery, not a measured conversion. The interval is wide (it spans 2.9–17.6%) because the subject-blind sample is only 54 cells; a larger battery tightens it materially.
- 87.0pp organic↔evaluation spread = cold-start classification
- The gap between organic (7.4%) and evaluation (94.4%, the 51 of 54 subject-named cells where WorkWhile is forced into the prompt) is 87.0 percentage points. A spread above 40pp is what Korpora flags as cold-start (our own working threshold): the model engages with WorkWhile when told to, but has limited independent signal to reach for it unprompted. Treat the high subject-named number as mostly query-framing artifact, not buyer signal.
3 · The live measurement streams
Two supply-side streams plus a demand axis not yet pulled. Stream 1 is the AI-recall battery (what the models say); Stream 2 is the operator-discourse footprint that feeds the next corpus (what people say where the models read).
Stream 1 of 2 · AI assistant recall · Korpora 108-cell battery · 2026-05-28
AI assistant recall composition
Buyer-decision queries (subject-blind and subject-named) run through frontier models, 108 cells total. Subject-blind cells (organic) reported separately from subject-named (evaluation).
Organic mindshare composition (54 subject-blind cells)
92.6% of buyer-decision queries are either contested or unclaimed. That is the addressable mindshare for the next two corpus cycles.
Organic owned share 7.4%[Wilson 95% CI 2.9–17.6]. How to read this and the 87.0pp cold-start spread is in section 2 above.
Aggregate co-occurrence share · reference-only (within WorkWhile's battery)
Read this as reference, not headline. Every bar here is co-occurrence inside WorkWhile's own battery. A competitor's share is how often it surfaced in cells whose queries were built around WorkWhile, not that rival's own mindshare: Instawork at 86.1% means it appeared in 93 of these 108 cells, not that Instawork owns 86.1% of the category unprompted. So do not read two competitor bars against each other as if they were each brand's standing. The subject bar is inflated on top of that, because the subject-named cells (two of the four query framings name WorkWhile explicitly) are included; strip those out and WorkWhile's true unprompted recall is the 7.4% above. A rival's true number needs that rival run as the subject of its own battery (a v4 step). The subject-blind composition above is the only headline metric.
Stream 2 of 2 · operator-discourse velocity · v3 foundation pull
Operator-discourse footprint + post-cutoff velocity
Lifetime brand mentions on the operator-discourse channels that feed the next training corpus, with the post-cutoff mention ratio (velocity) per channel. Bars are scaled per channel: the leader fills the row.
Hacker News
3.9× WorkWhile post-cutoff velocity
2.3× WorkWhile post-cutoff velocity
Twitter / X
3.2× WorkWhile post-cutoff velocity
On these channels WorkWhile out-mentions the cohort, the inverse of its 7.4% organic AI recall (counts re-measuring, see below). Velocity (the post-cutoff mention ratio) is the fastest-moving input: AI assistants index this discourse on a 6–12 month lag, so the recall catch-up arrives one to two training cycles out, provided the indexable signal (Wikipedia, comparison pages, G2 / Capterra) is in the corpus when it closes. That conditional is exactly what the action cards above address.
Source: workwhile-v3-2026-05-28 foundation pull (Hacker News via Algolia, Reddit via public JSON, X via Xquik). Counts are lifetime mentions; velocity is the post-training-cutoff mention ratio. These counts use unfiltered brand-name matching; because “WorkWhile” collides with the phrase “work while,” the absolute counts are being re-measured with a disambiguating matcher and should be read as indicative, not final.
Measurement scope · demand axis not yet pulled
Everything above is the supply side: whether AI recommends WorkWhile (mindshare) and the channels that feed it (velocity). The demand side, how AI search volume for on-demand staffing is trending (DataForSEO AI search volume), has not been pulled for WorkWhile. It is the next measurement to add. Demand direction matters because a contracting category raises supply-side urgency rather than lowering it: fewer total queries means each unprompted recommendation carries more weight.
4 · v3 notes & caveats
- Query battery.v3 ran buyer-decision queries inferred from the category, not WorkWhile's real on-site search terms or demand-side analytics. A paid engagement swaps the inferred queries for the terms buyers actually use, so the organic-mindshare number and the per-query gaps get materially more representative.
- Aggregate is reference-only. The 50.9% aggregate subject share mixes subject-blind and subject-named cells and overstates the position; it is shown for back-comparison only. The headline organic number is 7.4% (subject-blind cells). Per-rival subject-own batteries (each competitor as subject of its own battery, as run for the SSENSE cohort) are a v4 step.
- Demand axis not pulled. This cycle measured the supply side only (mindshare + operator-discourse velocity). Branded AI search volume (DataForSEO) was not run for WorkWhile; it is the next measurement to add.
- Card lift quantification. Cards give the mechanism, the hypothesis, and the re-measurement criterion, not yet a calibrated “N points over M corpus cycles” forecast. The ordering is a qualitative lift ranking.
Re-measurement cadence on engagement: weekly across all streams. The Korpora measurement model and the channel surfaces it measures both evolve continuously; weekly captures inflections that quarterly would miss.
Methodology question or factual correction? Ask in the chat above.
Underlying data: v3 full report · v2 (prior) · full methodology · home