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Give Sai a stack of CVs and the role you're filling. It reads every one against your actual requirements, scores each candidate with a reason, and hands back a ranked sheet — not a keyword filter.
Most conversations about how to source candidates with AI focus on the top of the funnel — finding more people, faster, from more places. That part has genuinely improved. The consequence is that the bottleneck moved. A role that used to draw forty applicants now draws four hundred, and the hours saved on sourcing get spent reading CVs instead.
This is where AI screening earns its place, and also where it most often goes wrong. Keyword filters were the first attempt, and every recruiter knows how that ends: the candidate who wrote "led the migration to Kubernetes" is rejected because the JD said "container orchestration," while someone who listed every buzzword in a skills section sails through. Filtering on strings punishes the people who write about their work naturally.
The difference with an agent is that it evaluates rather than matches. Sai reads each CV as a document and assesses it against what the role actually needs — so a candidate who describes the right work in different words is recognized, and one who lists the right words without evidence is not.
For each candidate it captures the factual layer — name, contact, current title and company, years of experience, education, the skills that map to your requirements — and then does the part that takes a human the longest: forming a judgment and explaining it. Every score comes with a one-line reason. That reason is what makes the output usable, because a ranked list without rationale is something you end up re-checking manually, which defeats the purpose.
A score compresses a candidate into a digit, and some of the most useful information does not survive that compression. So alongside the score, Sai flags the things a careful reader would notice:
These are the cases where an automated pass is normally weakest, and they are precisely the candidates worth protecting from a filter.
An underrated benefit of screening this way has nothing to do with speed. A person reading CV number two hundred is not applying the same standard they applied to CV number three — attention drifts, and the bar moves. An agent applies the same criteria to the first and the last, which means the ranking reflects the candidates rather than the order they happened to arrive in.
It also means the standard is written down. When someone asks why a candidate was screened out, the answer is in the sheet, in a sentence, next to the score.
The output is a ranked sheet, not a decision. Sai screens and explains; you decide who to talk to. This distinction is worth being deliberate about — both because judgment about a person should sit with a person, and because a screening pass is most useful when you can disagree with it. If the top three do not look right, the reasons are right there, and usually the fix is one line added to your requirements rather than a different tool.
Name the requirements you weight heavily and they will drive the score rather than sitting equal with everything else — a specific tool, a minimum depth in a domain, exposure to a particular stage of company. If you screen for a structured rubric, paste it in and Sai will score against your dimensions instead of a general fit rating. For roles where you deliberately want non-obvious candidates, say so, and adjacent backgrounds get surfaced rather than marked down.