AI Matching Engine - Physician-Survey Matching
A white-label AI matching engine that pairs physicians with healthcare surveys by specialty and response history.
The Opportunity
The client is one of the top market research companies, and a large share of its work is healthcare: surveys fielded for pharmaceutical, device, and payer clients that depend on reaching physicians with exactly the right profile. The survey itself is cheap; finding the qualified respondent is the expensive part. Matching physicians to studies was a manual discipline; recruiters worked project by project from static panel attributes. It was slow, it depended on individual recruiters' memory of who had performed before, and when a panel could not be filled in time the project stalled. Every stalled project meant revenue leaking out of the quarter.
The company already had the data to solve this problem. Years of fielded studies had produced a deep record of which physicians respond and complete, in which therapeutic areas, and under which screening conditions. None of that history was being utilized, because no recruiter could hold it in their head and no manual process could search it at project speed. The opportunity was to build a matching engine that treats specialty and response history as structured signals: score every physician in the panel against every open survey, and hand recruiters a ranked list for every open survey.
Key Challenges
Recruiters paired physicians to surveys by hand, one project at a time, from static panel attributes.
Healthcare studies need exact specialty, subspecialty, and history fits that manual review often missed.
Panels that could not fill inside the fielding window stalled projects, and revenue slipped out of the quarter.
Who completes and qualifies sat in past project records nobody could search, so proven respondents went untapped.
The Process
Mapped how projects move from fielding to panel fill, and where manual matching stalled them. The pattern was consistent: the harder the specialty requirement, the longer the manual search, and the higher the odds the project slipped past its fielding window.
Defined the matching signals: specialty and subspecialty from panel attributes, plus completion rates, screener pass rates, and data quality per physician as first-class inputs to every match.
Built the white-label AI engine that scores every panel physician against every open survey and returns a ranked match list. Recruiters start each project from the strongest candidates instead of a manual search.
Tuned matches against real projects and deployed the engine into the panel workflow. The engine runs under the company's own brand, so nothing about the client-facing experience changed.
Our Solution
Ajaia built a custom, white-label AI engine that matches physicians to surveys by specialty and response history. The engine scores every physician in the panel against every open survey and returns a ranked match list. Recruiters start each project from the strongest candidates instead of starting from a manual search. Invitations flow to physicians whose history indicates they will complete and qualify.
The engine runs under the company's own brand and inside its existing project workflow. It is built on the company's data, and every fielded study adds response history that makes the next match better. Matching is now a capability the company sells against.
Key Capabilities
Impact
The engine closed the gap between what the company knew about its panel and what its recruiters could act on. Panels that once stalled for want of the right subspecialists now fill from a ranked list, and invitations flow to physicians whose history indicates they will complete and qualify. The company's ability to close projects effectively improved 25%, and recovered projects plus reduced recruitment effort now represent more than $10M in annual impact from revenue uplift and cost reduction. The engine is white-label, built on the company's data, and every fielded study adds response history that makes the next match better.
Key Results
- $10M+ in annual impact from revenue uplift and cost reduction
- 25% improved ability to close projects effectively
- Panels fill from ranked match lists instead of manual recruiter searches
- Every fielded study adds response history that makes the next match better
- Matching is now a competitive capability the company sells against
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