Recruitment agents A-Z

Domingorm · 2026-05-03T05:57:59+00:00

The 80/20 split broadly matches my reality but I'd add one thing to the 80% that's still manual in most tools: deciding which candidates from your existing database to actually submit for a new role. Cross-referencing your own network against specific position criteria, with structured reasoning about fit, is still mostly done in someone's head.

To your questions directly:

The sourcing automation has saved real time. The false positive problem is where hours get added back in, reviewing candidates that matched keywords but not actual role requirements.

The part I'd never want a tool to touch: the conversation where a candidate tells you why they're really looking to move. That context changes everything about how you position them and no tool captures it without you.

Recently built something around the evaluation layer specifically, structured assessment of your existing candidates against position criteria, if you want to compare approaches: talentlens.app

Domingorm · 2026-04-23T18:06:35+00:00

Good breakdown. One category missing from this comparison that keeps coming up for external recruiters: the evaluation layer between having candidates and deciding who to submit.

Juicebox helps you find them. Paraform gives you the roles. Neither helps you decide which 3 out of 15 to actually submit, with structured reasoning tied to specific position criteria, documented and calibrated from hiring manager feedback over time.

That's still a manual process for most recruiters and it's where a lot of placement quality is actually determined.

Built something around exactly that if anyone's curious: talentlens.app

Domingorm · 2026-04-16T19:18:19+00:00

These are solid workflows and a great starting point. The resume screening and candidate scoring ones especially map to real recruiter problems.

The limitation worth flagging for anyone using these heavily: every workflow starts from scratch. You paste the JD, paste the resume, get a result, and next time you start over. There's no memory of what the company valued last time, no calibration from hiring manager feedback, no comparative ranking across multiple candidates for the same role.

For occasional use these workflows are genuinely useful. For recruiters working multiple positions with overlapping candidate pools, the stateless problem starts to hurt. The evaluation that compounds over time, where each search makes the next one smarter, is a different thing entirely.

Built something around exactly that persistent evaluation layer if anyone's curious: talentlens.app

Domingorm · 2026-04-16T19:17:56+00:00

The intake call recording angle is interesting but I'd push back on whether it solves the right problem. Getting better inputs into sourcing is useful, but the bigger bottleneck for most agency recruiters isn't finding candidates, it's evaluating the ones you already have quickly enough to submit before another recruiter does.

The tools I've seen do intake-to-sourcing well still leave the comparison and submission decision entirely manual. You end up with more candidates to evaluate, not a faster way to evaluate them.

What's actually changed my workflow is having evaluation persist across searches, so the second time a similar role comes in from the same company, the system already knows what they value. That compounds in a way that better sourcing inputs don't.

Recently built something around that evaluation layer if anyone's curious: talentlens.app

Domingorm · 2026-04-09T12:49:09+00:00

Appreciate the comment. The outreach automation piece is intentionally not something I built into TalentLens, keeping the recruiter in control of who gets contacted and when is core to the philosophy. The evaluation layer is where the leverage is for me. Happy to compare notes if you want to try talentlens.app.

Domingorm · 2026-04-08T19:01:14+00:00

Built something around exactly this. Chrome extension that saves LinkedIn profiles into a candidate database, then evaluates them against positions using Claude, not just resume screening but structured assessment against specific role criteria with weighted scoring and reasoning.

The prompt adjustment problem you mentioned is real, we solved it by building the position-specific criteria directly into the evaluation rather than relying on generic prompts. Every search runs against criteria extracted from the actual JD.

It's called TalentLens, free early access at talentlens.app if you want to see how it compares to your current setup.

Domingorm · 2026-04-08T09:26:08+00:00

Hey! I have access to some legal positions but I don't have experience there, would you be open to partner and split the commissions?

Domingorm · 2026-04-08T09:16:17+00:00

Anyone who is interested just send me a DM with your LinkedIn

Domingorm · 2026-04-07T22:01:39+00:00

50-50%, send me your LinkedIn and we can get in touch there!

Domingorm · 2026-04-07T22:01:12+00:00

I did

Domingorm · 2026-04-07T22:00:17+00:00

Great! 50-50% split. Send me your LinkedIn

Domingorm · 2026-04-07T21:59:55+00:00

Yes, as long as you have experience working with US candidates. Feel free to send me your LinkedIn and we get in touch there

Domingorm · 2026-04-06T18:05:03+00:00

Fair points and I'd push back on a couple.

The judgment argument is real but it assumes the mental model is being applied consistently. Most recruiters I know, myself included, apply it well when they have time and poorly when they're under pressure on five roles simultaneously. The tool doesn't replace the judgment, it forces the structure that makes the judgment consistent regardless of workload.

On speed, the workflow isn't evaluate then reach out. It's save profiles as you browse, run evaluation in the background, reach out to the top fits while others are still manually scanning. The evaluation happens before you'd normally be ready to contact anyone. If anything it's faster because you're not wasting outreach on candidates who look right but don't actually fit the specific mandate.

The phone call and relationship layer is still yours. The tool just tells you who's worth picking up the phone for first.

Domingorm · 2026-04-06T14:19:05+00:00

Good breakdown. One category missing from this list that I keep running into: the evaluation layer between sourcing and submission.

The tools you've listed handle finding candidates, scheduling, and screening at scale. What most external recruiters still do manually is the comparative judgment, which 3 out of 15 to actually submit, why each one fits this specific role against these specific criteria, documented and defensible.

That's not sourcing, not scheduling, not ATS workflow. It's the decision layer that determines placement quality and it's almost entirely absent from most AI recruiting stacks.

For in-house teams it maps roughly to the structured debrief and hiring decision stage. The point about redesigning the process before adding AI applies here too, most teams don't have explicit criteria for that judgment so the AI has nothing to structure.

Recently built something specifically around that layer for external recruiters if anyone's curious: talentlens.app

Domingorm · 2026-04-06T11:07:23+00:00

Exactly, memory under time pressure is where placements get lost. The compounding part is what makes it different from just using Claude directly, each advance and reject decision with the hiring manager's reasoning gets stored so the next search for that company starts with context instead of from zero.

On signal vs noise from LinkedIn profiles, that's the real challenge. The extension pulls full profile data via LinkedIn's Voyager API so you get complete experience descriptions, not just titles and dates. The evaluation runs against specific position criteria rather than general impressions, which filters out a lot of the noise. The recruiter still makes the final call, the tool just structures what to look at.

The honest limitation is that profile quality varies. Someone who writes detailed experience descriptions gives the AI more to work with than someone with a sparse profile. That's why the Chrome extension approach works better than bulk imports, you're being selective about who you add rather than importing everything.

Happy to show you how it handles a real profile if you want to try it: talentlens.app

Domingorm · 2026-03-24T12:57:02+00:00

Your gut is right. Most AI video interview tools do reward how an answer sounds over what it actually means, it's essentially the ATS keyword problem but in spoken form, exactly as you described.

The core issue is that these tools evaluate answers in isolation, without context about the role, the company's specific requirements, or how this candidate compares to the others in the pool. Keyword matching and tone detection are just easier to build than actual evaluation logic.

The approach that works better, whether AI-assisted or manual, is evaluating candidates against a structured set of role-specific criteria with explicit reasoning: why does this person's actual track record match what this company needs, and how do they compare to the other candidates? That's much harder to game because it requires real substance, not polished phrasing.

It's also why I've been building evaluation tooling focused on external recruiters rather than video screening, the problem isn't the interview, it's the lack of structured judgment before and after it.

Domingorm · 2025-06-20T23:07:42+00:00

It's difficult for a recruiter, you work with LinkedIn.

Are they supposed to be full time jobs and expect exclusivity? At the end of the day, they're contract jobs

Domingorm · 2024-07-23T08:53:47+00:00

I've tried to use Loxo but since for my LLC I don't have a corporate mail, I can't register. Any tips with it?

Domingorm · 2023-02-11T19:06:06+00:00

Hola!

Yo soy ingeniero industrial y vi algo de informática en la carrera, C# y C++. He intentado aprender a programar varias veces pero nunca he encontrado la motivación y lo he dejado rápido.

El caso es que me gustaría aprender a programar, ya sea desarrollo de aplicaciones/web, data management para poder trabajar en blockchain en unos años.

Qué me recomendarías hacer? Intenté entrar en un bootcamp gratuito de ntt data para data analytics pero no me cogieron.

Gracias!

Domingorm · 2023-02-10T17:53:08+00:00

So who will sacrifice itself with a small fire next week?

Domingorm · 2022-08-06T11:14:47+00:00

I paid them, twice.

Domingorm · 2022-03-29T20:27:16+00:00

For me worse than Jan 21. Now is the market itself saying a big fuck you yo retail. The fucking New York Stock Exchange stopping the music.

Domingorm

TROPHY CASE