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The Contingent Sourcing Agent (CSA) is a VNDLY capability that surfaces candidate recommendations to help PMOs, Managers, and MSPs source contingent talent faster and with greater confidence. This case study captures the design and research work heading into GA — giving product and engineering partners a clear, shared reference point for the decisions made and the direction ahead.
Two goals shaped the work throughout: first, giving users recommendations that are transparent enough to act on confidently — not just a score, but a rationale; and second, standardising the terminology and UI patterns across the recommendations experience, moving away from inherited HiredScore language toward something that feels native to VNDLY.
Participants saw CSA as moving them beyond purely reactive sourcing. By surfacing available, qualified talent immediately, PMOs could make smarter upfront decisions — including whether to pursue contingent vs. full-time hiring.
100% of PMO participants identified worker redeployment as a high-value feature. Organisations had no easy way to track or redeploy previously engaged contractors — talent was disappearing "into the ether" with no visibility into work history.
A strong need for transparency in AI-generated grades and recommendations was expressed. Users needed to understand why a recommendation was made before acting on it — preferring AI as a "co-pilot" rather than a passive black-box feature.
Participants were concerned the AI would be making judgements on stale, incomplete, and intangible data — noting that candidate profiles are only updated when submitting for a specific role, leaving data perpetually out of date.
The CSA v1 concept test returned strong signal on the value of strategic forecasting, worker redeployment, and work history visibility. The research directly shaped the design direction for the production feature — ensuring transparency and user control were baked in from day one, not bolted on.