Skip to content
Artifisys
Menu
Start a conversation Talk to us

Staffing & recruitment

Five million people hired and five million separated in the same month

Why does filling a role still take weeks when the candidates already exist?

Book a 20-minute call Free. No demo, no slides.

Figures and rules on this page apply to

United States

Working somewhere else? The shape of the problem usually travels. The deadlines do not.

What this looks like

A candidate is placed on Tuesday. On Thursday a different consultant in the same firm calls that person about a similar role. The placement lives in one system and the call list came from another. The candidate notices. That is the moment the relationship costs more than it earns.

The numbers

Every figure here is someone else’s. Check them.

  • 5.1Mhires in a single month across the US economy
  • 5.1Mtotal separations in that same month, which is the churn in one figure
  • 7.3Mopen jobs at the end of the month, a rate of 4.4 percent
  • 3.1Mof those separations were people choosing to quit

Why it happens

It is not a people problem.

A placement is a chain of events owned by different systems. The advert sits with a job board and the conversation sits in a mailbox. The compliance pack sits in a folder and the timesheet sits in a payroll tool. Every handover between them is done by a person retyping something, and every retype is a place where the chain can break.

Why your current software has not fixed it

Because it was never built to.

Your applicant tracking system is authoritative about the pipeline it was told about. It does not know what was said on a call, because that was speech. It does not know the certificate expired, because that was a scanned page. These are not gaps in the product. They are the parts of the job that were never data.

Intelligence, plumbed in

The reason a candidate said no is in a call note. No field in any tracker holds it.

The build is one candidate history, with documents read on upload and expiry tracked. Add the placement chain joined end to end, and a screen for pipelines going quiet. Underneath it: a connector, one agreed meaning per field, and a test set scored on your own records.

How we make AI survive real data
  • Connectors
  • A semantic layer
  • Evals you can check

However hard, whatever it is

Placement speed is one example. Bring the one you assume cannot be fixed.

We wrote this up because federal turnover data is published monthly. If yours is margin per desk, client reporting or payroll queries, the method holds.

  1. 01

    We sit with you

    Days where the work happens, not a workshop in a meeting room. We watch the job get done and write down the shortcuts nobody wrote down.

  2. 02

    We read everything

    Your data, your rules, your vendors and their documentation, and the published research on your sector. We report what is actually in there.

  3. 03

    We break it to first principles

    Not which tool fixes this. What is actually causing it, taken apart until we reach the piece that cannot be divided further.

  4. 04

    Then we build

    Weeks, not quarters. By this point we are not guessing what to build, and guessing is the thing that makes projects long.

What we build

Specific enough to argue with.

Four mechanisms, not four features. Each one is a thing that happens on its own, every day, whether or not anyone remembers to run it.

  • Notes, mail and call summaries attached to the right person automatically, so one candidate has one history instead of four partial ones.

  • Right-to-work documents and certificates read on upload, with expiry tracked rather than remembered.

  • Job board, tracking system, payroll and invoicing joined, so a placement stops being retyped at each step.

  • A consultant screen showing which candidates have gone quiet, because the pipeline decays silently.

How you would know it worked

Numbers in your own reporting, not ours.

  • Time from a role opening to a shortlist a client accepts.
  • Share of candidate records with one joined history rather than duplicates.
  • Compliance documents expiring without anybody noticing, which should go to zero.

Straight answers

Where a model is involved, it is scored against your own records first. Accuracy per source, not one flattering average.

The questions this raises

  • Does this replace our applicant tracking system?

    No. It feeds it and reads it. Ripping out a tracking system your consultants know is a large cost for a change they did not ask for.

  • Our consultants keep notes their own way.

    That is fine and it is not worth fighting. Free text is readable. The point is to stop asking people to write in a shape a database will accept.

  • We handle sensitive personal data.

    Then that decides the design. Retention, deletion and access are agreed first, and a candidate record is not copied anywhere it does not need to be.

  • Do these numbers describe our sector?

    They describe the whole labour market and we say so. We use them because the churn is the business, not because they are your figures.

Where the figures come from

We did not make these up, and you should not take our word for them.

  1. BLS: Job Openings and Labor Turnover Survey news release
  2. BLS: JOLTS programme home
  3. BLS: JOLTS latest numbers

Somebody noticed first, and nothing consumed it

The earliest warning is a note in a box nothing reads.

A driver, a fitter, an engineer or an adviser saw it coming and wrote it down. It went into free text. Free text does not trigger anything, does not add up, and never tells you that four people reported the same thing this week.

Different work, same box nobody reads. Whatever your people notice first, it is being written down somewhere already.Tell us what yours write down.

A placement is one chain of events living in four systems that never speak.

The build is one candidate history, with documents read on upload and expiry tracked. Add the placement chain joined end to end, and a screen for pipelines going quiet.

See everything we build
  • Software
  • Hardware
  • Ways of working
  • Whole ventures

Is this happening to you? Tell us the size of it.

Twenty minutes. We will tell you honestly whether the numbers justify doing anything about it.