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How it works

A fair read is a structured read.

The same three steps, run the same way for every applicant, from the first to the two hundredth. No skimming, no auto-rejecting, no keyword filter dropping good people quietly.

01

Define the role once

Give Slate the role: a title, the must-haves, and the nice-to-haves. This is the standard every application is read against, the same for the first applicant and the last.

role: Customer Success Manager · 4 must-haves

The role

Customer Success Manager

Seed-stage SaaS · fully remote (US)

Owned a book of SaaS accounts
Comfort with a technical product
Evidence of reducing churn or driving expansion
Clear written communication
02

Slate reads every application

Each application is read in full and against the role. Slate works through the must-haves, checks each against the evidence, and keeps personal characteristics out of the read. This is the part only modern language models can do.

240 applications · read against the role · one rubric

Structured screen

Owned a book of SaaS accountsyes
Comfort with a technical productyes
Reduced churn or drove expansionyes
Clear written communicationyes
03

You get a fair read, with reasons

For every candidate: the fit, a plain-language summary, the reasons tied to the exact lines, and the requirement-by-requirement view. You review and decide.

18 shortlisted · every reason sourced · a human decides

Renata Alvarez

Strong fit

Renata has run a book of SaaS accounts for four years and points to a concrete retention result, not just responsibilities. The application shows comfort with a technical product and clear, structured writing. The main thing to check is the size and stage of the accounts she owned.

Directly owned SaaS accounts, which the role asks for. Managed a book of 40 mid-market SaaS accounts worth $2.1M in ARR.

Evidences a churn result, not just the responsibility. Cut gross churn from 14% to 8% over three quarters by rebuilding onboarding.

The guardrails

Fair by design, and a human decides

One rubric for everyone

The same must-haves, read the same way. Two similar candidates get similar reads, whether they applied first or last.

Our fairness approach

Model-agnostic

Slate routes across leading models per task, so the read stays consistent as the models improve, and is never locked to one vendor.

How the AI works

A human always decides

Slate surfaces the evidence and the gaps, then stops. It never auto-rejects and never makes the call.

See the product

See it read your own role.

Start free, paste a role and a few applications, and see the evidence-based read for yourself.