A general AI hire has to be taught recruiting. I already know it.

    General AI employees are impressive and genuinely useful across support, ops and finance. Hiring is where general breaks down, because the hard part is not the workflow. It is knowing which engineer is actually senior, what the comp band really is, and who is worth the hiring manager's hour.

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    The short answer

    Every general agent platform ends up asking the same thing of you: teach me your standard, wire my tools, maintain the setup. That is a fair deal when the work is repetitive and the standard is obvious.

    Recruiting knowledgeBuilt in: seniority signals, skill inference, comp bands, disqualifiers and scoring
    Candidate dataMy own index of the open web, plus the candidates already in your ATS
    Time to first shortlistOne sentence in the channel, same day

    What you get either way

    Both sides, written plainly. The one on top is mine.

    By choosing me, you get

    A specialist, on day one

    Hiring judgement included

    • Seniority read from shipped work, not from the title
    • Comp bands and disqualifiers derived per role
    • The same standard for every recruiter in the team

    Data nobody else has

    • My own crawl of the open web
    • Your ATS re-read on every brief
    • 97.80/100 on the independent people search benchmark

    No build phase

    • One sentence in Slack, Teams or WhatsApp
    • First shortlist the same day
    • 1,000+ tools underneath, including your ATS and calendar
    #hiring-ops · day one
    SetupInvite me to the channel
    ConfigNone. Describe the role in one sentence
    OutputScored shortlist with evidence, drafts pending approval
    OwnerYou approve, I do the rest
    By choosing a general AI employee, you get

    Breadth, once it is configured

    A standard you write

    • Recruiting judgement lives in instructions you maintain
    • Quality drifts as roles and markets change
    • Someone has to own the setup

    Borrowed candidate data

    • Queries the same licensed databases everyone licenses
    • No index of its own to search
    • Three teams, one brief, the same five names

    A build before the value

    • Workflows, connections and permissions first
    • Hiring tools treated like any other app
    • The first shortlist waits on the project
    Workspace · configuration
    SetupWorkflows, prompts, integrations, permissions
    ConfigOngoing, owned by someone on your team
    OutputAs good as the instructions written last quarter
    BreadthSupport, finance, ops. Hiring as one of many

    The difference in one table

    General AI employeeLaidback
    Recruiting knowledgeWhatever you write into instructions and keep maintainedBuilt in: seniority signals, skill inference, comp bands, disqualifiers and scoring
    Candidate dataWhatever the connected tools expose, usually a licensed databaseMy own index of the open web, plus the candidates already in your ATS
    Time to first shortlistAfter the workflows, prompts and integrations are configuredOne sentence in the channel, same day
    ScoringModel judgement, phrased differently each runDeterministic score, same brief and same shortlist twice, evidence attached
    Integrations that matter hereBroad catalogue, hiring tools treated like any other appATS, calendar and channel treated as first class, 1,000+ tools underneath
    BreadthSupport, finance, ops and marketing in one hireRecruiting only. That is the whole point

    General is a feature everywhere except hiring

    Taste is not a setting

    Knowing that a staff title at a 40 person startup is not a staff title at a bank is a judgement, not an instruction you remember to write. It ships with me, the same for every role and every recruiter on your team.

    The index is the moat

    A configured agent still queries someone else's licensed snapshot. I built my own index of the open web, which is why my shortlist contains people who have no searchable profile at all.

    Nothing to build first

    No workflow canvas, no prompt library, no ops person maintaining it. You describe the role the way you would to a colleague and the first shortlist lands the same day.

    Where a general AI employee wins

    If you want one system covering support, finance, ops and marketing, and hiring is a small part of the year, breadth is worth more than depth. Buy the generalist and let me sit out.

    #hiring-ops
    Alice
    Alice

    We are trialling a general AI employee. It can do recruiting tasks too.

    Laidback

    It can run the tasks you describe. The question is who writes the standard it applies to a candidate.

    Alice
    Alice

    We would. In the instructions.

    Laidback

    That is the difference. My scoring, skill inference and comp logic are the product, not the setup. Give me one role and compare the first shortlist against the one you configured.

    Built in taste

    What people ask when they are trialling a general AI hire

    We want one agent for the whole company. Why buy a specialist?

    If hiring is a small part of your year, do not. Buy the generalist. If hiring is how the company grows this year, the depth is the difference between a list and a hire.

    Can we run both?

    Yes. They rarely touch the same work. Keep the generalist for support and ops, hand me the reqs and the pipeline.

    What exactly is built in that we would otherwise configure?

    Skill inference from shipped work, seniority calibration, comp bands per market, disqualifier logic, intent expansion into thousands of queries, and a deterministic score with evidence per line.

    Where does your candidate data come from?

    My own index of the open web: repositories, portfolios, publications, personal sites, company writing and talks, plus the candidates already sitting in your ATS. Profiles are built from what people ship, not from a licensed snapshot every vendor buys.

    Do you send anything on your own?

    No. I draft, you review, you send. Where a habit runs on its own you pick the mode: Autopilot or Ask approval.

    Hiring is neither. The standard is the hard part, it is different per role, and it is the thing nobody has time to write down. So I built it in instead of asking for it.

    On the independent people search benchmark I score 97.80 out of 100. The method and every scored query are published on the benchmark page.

    Recruiting judgement ships with me

    You are not writing prompts to teach me what a strong candidate looks like. I already know what this job is, which is why the first shortlist is worth reading.

    Different data, not the same resold list

    Most tools query the same three databases. I build from work people leave in public, so the person who never updated a profile is still findable.

    Skills nobody wrote down

    Nobody lists that they carried a migration or ran the on call rotation. I infer it from the work and then check it.

    I widen the brief when it is too narrow

    If your search returns eleven people, I show you the adjacent titles and teams worth opening up to, and say what the trade is.

    The people who never applied

    Silver medallists, referrals from last year, the engineer whose company just froze hiring. I keep watching so you do not restart from zero.

    Where to go next

    I run on top of your ATS, I do not replace it

    Templates you can use today

    Laidback

    Run one role through both

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    Same brief, the configured generalist and me. The names that appear in only one list settle it.