Understand candidate fit before opening a profile
If you source your own talent, you already know where you lose the most time. It isn't running the search. It isn't writing the outreach. It's the middle part, where you're presented with countless plausible profiles and try to figure out where to start.
Who should you view first? Will the next profile be empty like the last one? When you're a few profiles in, the problem of viewing misfit, empty profiles, is not too pronounced. As you start to get thirty profiles into the process your standards may start to drift. Once you're around fifty your mind may be spinning.
As talent acquisition experts, we lose so much time chasing down empty leads. We've been thinking about this problem for a long time. We know this process should be easier. Now it is.
What bulk AI candidate review does
We built bulk AI candidate review into Fetcher. It evaluates multiple candidates against a role and returns a quality-of-fit review with reasoning. We're rolling this out throughout our software suite. It is available in the Fetcher web app and on our Chrome extension where it works on top of Google search, LinkedIn, Github, and more sites.
Using it is simple. Whenever you're presented with a list of search results, click "review," and Fetcher will measure how well each person fits that specific searches requirements. You get a quality-of-fit review on every candidate in seconds.
Why this matters more for self-sourcing
When you're sourcing for your own roles, as a founder or an in-house recruiter or an agency owner, that's not your only job. You're managing meetings, returning emails, and answering to hiring managers who want updates yesterday. And underneath all of that, you're still reading profile after profile, often only to find the person isn't close to a fit.
Fetcher has spent years running sourcing campaigns for our customers. Our recruiters evaluate candidates well because they do it all day. The AI behind bulk review was built on what that team does and how they decide. We are not trying to replace recruiters. We are extending what a recruiter can do. The judgment work in sourcing is real. Deciding which candidate is worth a real conversation. Knowing how to frame a role for someone happy in their current job. Reading between the lines of a hiring manager's brief. That work belongs to a person.
The volume work does not. Reading fifty profiles to find the five worth a closer look is pattern recognition. Bulk review handles that. You keep the parts of sourcing that need a human, and you do more of them.
What the AI is actually doing
Our bulk review reads the candidate's actual background and assesses it against the role you're sourcing for. It looks at what someone has done, where, for how long, and how their work has progressed. It weighs context, not just titles. A "Senior Engineer" at a fifteen-person startup did different work than a "Senior Engineer" at a Fortune 500, and the AI knows the difference. A candidate with the right keywords but the wrong trajectory will be flagged. A candidate with the wrong title but a strong underlying fit will be surfaced.
You get a recommendation and the reasoning behind it. The reasoning is specific. It will tell you the candidate is a fit because of their three years building zero-to-one product at companies similar in size to yours, or that they are a stretch because their last two roles were managerial when you need an individual contributor. The goal is the judgment a senior recruiter would give you, applied to every candidate on the list.
We've built enterprise-grade evaluations with an audit trail. Every recommendation is logged with the reasoning that produced it. You can revisit a decision three weeks later and see why a candidate was rated the way they were. You can show a hiring manager the reasoning behind a recommendation and have a real conversation about it. You can review your team's accepted and rejected candidates and look for patterns. When the AI gets something wrong, you can see exactly where it went wrong and adjust.
The audit trail also matters for compliance. AI in candidate evaluation is a regulated area, and the rules are tightening. Our review is built to apply the same logic to sourced candidates and inbound applicants alike. It does not use protected characteristics in its evaluation. Fetcher has been independently audited, and every recommendation comes with the reasoning that produced it, so a recruiter, a hiring manager, or a legal partner can see how a decision was made.
This is also why bulk review works for inbound applicants, which we announced earlier this year. You can review a sourced batch and a pool of applicants in the same workflow, with the same defensibility, without switching between systems or worrying about which set of rules applies.
And now you can sign up directly
Fetcher is fully self-serve.
The Self-Serve plan is $115 a month, or $99 with an annual plan. You get 300 candidates a month, bulk AI review on all of them, email sequencing, and unlimited data export. Sign up, install the extension, and run your first bulk review in minutes.
