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Kanban and AI Scoring

OFM Jobs was good at introductions and bad at everything after. An employer could open an applicant, message them, and then the product ran out of road: no pipeline, nothing to come back to. So hiring finished where it always had, in someone's inbox, off our platform. Kanban and AI rebuilt the missing middle: a pipeline employers want to live in, with AI ranking who to open first.

The Story

The product ended at "message sent."

See who applied, view their contacts, send a message. That was the whole journey.

  • After the first message, there was nowhere to put the candidate.
  • So the moment real hiring started, it left the platform.
  • A job platform doesn't win on introductions. It wins when someone gets hired on it.

Two problems wearing one coat.

The leak was really two problems:

  • No structure to hold a candidate after first contact.
  • Hundreds of applicants per role, and no way to tell who to open first.
  • Kanban answers the first. AI answers the second.
  • Built for someone who hires twice a year, not a full-time recruiter.

The Decisions

Kanban, because the stages already existed.

Every employer already pictures hiring as stages, so I drew the board already in their head, not a new concept to learn:

  • People to review
  • People I'm talking to
  • People I'm interviewing
  • An offer out

Let them shape the pipeline.

A café hires nothing like an agency. A board that only fits our idea of hiring makes employers bend around it, then leave.

  • Stages are theirs to add, rename, and reorder.
  • A tool shaped to your work is a tool you stay in.
  • Customization is the retention mechanism.

The conversation lives on the board.

Messaging was where the leak began: one reply and everyone jumped to WhatsApp.

  • The whole thread lives on the card, beside the candidate it's about.
  • Reach out and follow up without exporting anything.
  • The pipeline holds the talking, not just the tracking.

Rank, and show your work.

A list treats the 200th applicant like the 1st. The real question is "who do I open first?"

  • Every candidate carries an AI match score, in the loudest spot on the card.
  • The board doesn't just hold candidates, it ranks them.
  • A score nobody understands is a score nobody trusts, so every score opens into its reasons.
  • Nod and move on, or overrule it, but always knowing why.

The human holds the pen.

AI orders the column, but a drag always wins. The score is a fast first pass, never the gatekeeper. The person hiring makes the call; a machine shouldn't quietly decide who gets seen. Suggest hard, decide never.

The Full Flow

Your jobs, in one place.

Every role you're hiring for on one screen: who's applied, who's new, what needs you today.

  • One row per role: status, applicants, interviews in progress, and who owns it.
  • A quiet pulse marks what arrived since you last looked.
  • Click a role and you're on its pipeline.

See the post the way applicants do.

Open a role and see the posting itself, the same page applicants apply from, and the one you hire from.

  • A preview banner says it plainly: this is what applicants see.
  • Location, type, and pay as labeled facts, not buried in prose.
  • The rail keeps your side of it: live stats and the way back to the pipeline.

The person behind the card.

Open an applicant and the board steps aside for the whole picture:

  • The score, broken into skills, experience, and role fit.
  • Their experience, languages, and resume.
  • Enough to decide without opening ten tabs.

The Outcome

Impact.

A contact list became a pipeline.

  • Employers build their own stages; the best fit rises on its own.
  • Hundreds of applicants become a ranked shortlist, not a scroll.
  • Hiring happens on OFM now, not in an inbox, so we earn a seat at every hire.

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OFM92%of hiring nowhappens on OFM200 → 12hundreds become aranked shortlist
Applied5
Sarah Chen

Sarah Chen

Senior Chatter

92
AI match
Skills94
Experience89
Role fit92
English C288 WPM
Applied 2d ago
Marcus Johnson

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Chatter

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AI match
Skills85
Experience80
Role fit86
SalesRetention
Applied 3d ago
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Skills78
Experience73
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English C1PPV
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Experience70
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EmpathyCRM
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Experience67
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Aisha Rahman

Aisha Rahman

Senior Chatter

88
AI match
Skills89
Experience85
Role fit90
RetentionEN/FR
Applied 1w ago
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83
AI match
Skills84
Experience80
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EN/RUCRM
Applied 2w ago
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Tomoko Sato

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Experience90
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Retention95 WPM
Applied 1w ago
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SalesNight shift
Applied 2w ago
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89
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Experience85
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87
AI match
Skills86
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Role fit89
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Liam Carter

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AI match
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Experience92
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