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Layer Point
IndustriesBuilt for owner-operated businessesThe tools change from trade to trade. The problems usually don't: retyping, chasing, and systems that don't talk.
Our workAI candidate sourcing for recruiters

AI candidate sourcing for recruiters

AI agents

A recruiting firm's team spent hours on every search: finding candidates, checking them against the brief and ranking them by hand. We designed an agent that does the searching and ranking, so recruiters start from a shortlist and spend their time on people.

recruiter reviewing a shortlist at her desk
The story

From the point to the layer

The pointRecruiters spent hours searching for and ranking candidates by hand.
The layerAn agent that finds and ranks candidates, with a recruiter approving every shortlist.
What changedRecruiters start from a ranked shortlist instead of a blank search.
Who we worked with

The client

A recruiting firm that fills specialist roles for its clients. The quality of each shortlist is what the firm is known for.

Where it got stuck

The challenge

Every new brief meant starting from scratch: running searches, reading profiles, checking each person against the must-haves and ranking the best fits. It was slow, and the time went into searching rather than talking to candidates and clients.

The firm also needed to be sure any tool worked responsibly: no automated activity on anyone's social accounts, and a recruiter making the final call on every candidate.

The layer

What we built

01A brief the agent can work fromEach role's must-haves and nice-to-haves captured in a structured brief.
02Search through licensed dataCandidates found through licensed data providers, not by automating anyone's social media account.
03Hard filters firstCandidates who miss a must-have are filtered out before anything else.
04Scoring with reasonsEach remaining candidate is scored against the brief, with the reasoning shown, so a recruiter can see why someone ranked where they did.
05A recruiter approvesThe agent builds the shortlist. A recruiter reviews it, approves it and decides who gets contacted.
06It learns from feedbackRecruiter decisions are logged so the ranking improves over time.
The process

How we worked

01We found the pointWe worked through how recruiters ran a search today and where the hours went.
02We built the layerWe designed the pipeline in stages, from brief to ranked shortlist, with a recruiter checkpoint built in.
03We tested it in stagesClear milestones meant each stage could be checked on real briefs before moving on.
What changed

The result

The design gives recruiters a ranked shortlist with the reasoning attached, instead of a blank search, so their time goes into conversations rather than searching.

Inside the build

What the team sees

Shortlist, ranked by the agent
Candidate A92%
Candidate B88%
Candidate C81%
Waiting on recruiter approvalApprove
Services used
Next case studyEquipment tracking with live alerts
FAQ

About this project

01

Does the agent message candidates on its own?

No. It builds and ranks the shortlist. A recruiter decides who is contacted, and how.

02

Does it automate LinkedIn?

No. It uses licensed data providers, not automated activity on anyone's account.

Could SR&ED cover part of your build? Read how it works

How we work

Every project, the same three steps

How we work
01We find the pointWe sit with your team and pick the one problem costing you the most.
02We build the layerSoftware, AI and integrations on top of the tools you already have.
03We keep it runningYour team gets trained, you own everything, and we stay on for support.

Tell us what's slowing you down

We listenHow the work flows today, and where it gets stuck.
We pointThe one thing we would fix first, and why.
You decideA straight answer on fit. No follow-up sequence.
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