AI Candidate Sourcing Software and Talent Discovery

What is AI candidate sourcing, and how is it different from a search box?

A search box matches the words you typed. AI sourcing tries to work out what you meant. In Pitch N Hire the feature sits under Talent Network as AI Talent Discovery, and it opens with a prompt asking you to describe the role you are hiring for. You write it in ordinary English, with no boolean operators, and there is a microphone if you would rather say it. The system parses that description into structured criteria, then ranks people against them on skills, experience, location, seniority, domain expertise and hiring intent, ordering results by how close each one sits to what you described. Each candidate carries a numerical match score. The workflow the interface itself describes is short: describe the role, let it rank by fit, read the scores, message someone, add them to a pipeline. The feature is currently in beta.

Why does sourcing consume more recruiter hours than anything else?

Because the work is invisible and repetitive. Building a search, reading profiles, deciding who is plausible, finding a way to contact them, writing something that does not read like a template, then doing it again next week for a role that is only slightly different. Most of that time is spent on the two ends: constructing the query and rejecting people who never fit. The middle, judging a genuinely plausible person, is the part worth a recruiter's attention and gets the least of it. Boolean strings compound the problem, because they are precise about words rather than about meaning, and a good engineer who described the same skill differently is invisible to them. This is why the plain-English approach matters. It shifts effort from constructing syntax to describing the job, which is the thing a recruiter already knows how to do.

How do you write a prompt that returns useful people?

Write the role, not the resume you hope to see. Include the skills that genuinely cannot be learned in the first month, a realistic experience range, the location or work arrangement, the seniority you mean rather than the title you use internally, and the domain if it actually matters. A prompt saying senior engineer with strong Python returns something. A prompt saying a backend engineer with five to eight years building payment systems in Python, based in or willing to relocate to Noida, comfortable owning reliability for a live service returns people you can act on. Then iterate. Read the first ten results and adjust the description where the mismatch is obvious, exactly as you would refine a brief with a hiring manager. Two rounds of that is usually enough. Keeping the job description and the prompt aligned is what stops the shortlist drifting from the role.

What does a match score actually mean?

It means the system judged this person close to the criteria it extracted from your description, on skills, experience, location, seniority, domain expertise and hiring intent. It does not mean this person will accept your offer or succeed in the job. Treat it as a triage order rather than a verdict. The practical use is sequencing: work down the ranked list, contact the strongest fits first while your attention is fresh, and stop when the quality visibly drops rather than when you reach a number. Two things are worth watching. A score computed from a description you wrote badly will be confidently wrong, so a low-quality prompt produces a high-quality-looking list. And ranking rewards similarity, which means it will under-value the unconventional background that occasionally turns out to be your best hire. Read past the top five. The person at rank fourteen is sometimes the interesting one.

What do you do with a ranked shortlist?

Act on it in the same place you found it. From the results you can message a candidate directly and add them to a specific job's pipeline, which matters more than it sounds, because sourcing that ends in a spreadsheet is sourcing you will repeat next quarter. Adding someone to a pipeline makes them a tracked candidate with a stage, a history and an owner. Messaging from the results keeps the first contact attached to that record rather than sitting in a personal outbox. Set a simple rule for yourself before you start: everyone you contact gets added, including the ones who say no, because a no today is a strong candidate with a known reason in six months. Record the reason as well as the answer. What happens after the reply is ordinary pipeline work, handled in candidate management software.

Where does AI sourcing go wrong?

Four ways, and only one of them is the technology's fault. Recruiters describe a candidate instead of a job, listing every tool the previous person used, and get a narrow list that misses good people. They trust the score as a decision rather than an order, so the human review that catches nonsense never happens. They send outreach that reads as generated, which is now obvious to the people you most want to hire and does more damage than sending nothing. And they treat a beta capability as a finished workflow, then blame the tool when the interface changes. Beta means it is still moving. The honest position is that ranked search compresses the boring part of sourcing and leaves the judgement untouched. Expect a better starting list, not fewer decisions. The outreach discipline that turns a shortlist into replies is a separate craft, covered in candidate sourcing software.

How does AI sourcing fit with the rest of your hiring stack?

It sits in front of the pipeline and feeds it. Sourcing finds people who did not apply; the applicant tracking system handles everyone once they are in process, whether they arrived through an advert, a referral or a search. Keep that boundary clean, because the common failure is a sourcing tool that becomes a second, partial database nobody reconciles. The other integration point is your own history. People you contacted last quarter, candidates who reached a final interview and were not chosen, and applicants to a similar role are all cheaper to reach than anybody new, so search your existing records before you search anywhere else. That habit alone changes the economics of a quarter. If you are assessing several AI features at once rather than sourcing specifically, the evaluation questions are collected in AI recruiting tools. Keep one system of record and let sourcing feed it.

What to put in a sourcing prompt