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Solution: Screening assistant

You screen hundreds of resumes by hand

Every application is summarized against the criteria you define, with evidence from the resume. Recruiters see strong matches and gaps at a glance, and people make every decision.

Sounds familiar?

Signs this is costing you time

Popular job posts attract hundreds of applications. Recruiters skim resumes in seconds each, and good candidates with unusual backgrounds are missed while the pile grows. Applicants wait weeks for a reply, and the best ones accept other offers.

Screening is also inconsistent. Different reviewers focus on different things, and fatigue sets in by the fiftieth resume.

An AI screening assistant reads every application the same way against criteria you set, explains its reasoning with evidence from the resume, and gives recruiters a clear starting point.

It does not reject or hire anyone. It organizes the pile so people can make better, fairer decisions much faster.

  • Hundreds of applications arrive for a single role.

  • Candidates wait weeks for a first reply.

  • Screening quality varies between reviewers and by time of day.

  • Strong candidates with non-traditional backgrounds are overlooked.

  • Key details, such as notice period or work rights, are missing and chased later.

Before and after

How it works today, and how it works after

Before

How it works today

  1. Applications pile up in the applicant tracking system or inbox.

  2. Recruiters skim resumes quickly.

  3. Shortlists depend on who reviewed which batch.

  4. Missing information is requested one candidate at a time.

After

How it works after

  1. Each application is summarized against your written criteria.

  2. Evidence from the resume is quoted for every criterion.

  3. Missing details trigger an automatic, polite request.

  4. Recruiters review ranked summaries and make every decision.

  5. All candidates receive timely updates.

What we build

What we build

Clear criteria

Must-have and nice-to-have criteria written with the hiring manager, focused on job-relevant skills.

Resume reading

Resumes and applications read into structured summaries, whatever the format.

Evidence-based summaries

Each criterion marked met, unclear or not met, with the quote that supports it.

Missing detail requests

Automatic requests for information such as availability or work rights.

Recruiter review screen

Summaries side by side with the resume, for quick human decisions.

ATS integration

Summaries and statuses saved in your applicant tracking system.

In practice

What it looks like in practice

An illustrative walk-through, not a client story.

A staffing agency receives around four hundred applications for a batch of warehouse supervisor roles. Previously, two recruiters spent several days on first screening.

Now each application is summarized within minutes: forklift certification (met, quoted from the resume), supervisory experience (unclear, one mention of "team lead"), shift availability (missing, request sent automatically). Recruiters review summaries sorted by criteria met, read the full resume for anyone who looks promising, and call shortlisted candidates the same week. Every applicant gets an update within days instead of weeks.

Models and tools

AI models and MCP servers that usually fit

We choose models by task, data sensitivity and cost, and test them on your real examples before we commit. These are common starting points, not a fixed recipe.

How we build it

The services behind this solution

Most solutions combine two or three of our services. These are the ones this one usually needs.

AI workflows

Step-by-step automations where AI handles the reading, sorting and drafting inside a process you control.

Document automation

Read invoices, forms, contracts and IDs, pull out the right fields and route them for review.

Internal tools

Custom software for your own team: trackers, approval flows and back-office systems that replace spreadsheets.

Industries where it fits best

Honest limits

Limits and human checks

AI screening can reflect and amplify bias if criteria or examples are biased, and a language model can misread a resume or invent details. We design against this: criteria focus on job-relevant requirements, the assistant does not see or consider protected characteristics, every judgment is supported by a quote, and all decisions are made by people. We recommend regular reviews of outcomes for fairness.

Employment and data protection laws in some places regulate automated decision-making in hiring and require transparency with candidates. We build for human decision-making and help you inform candidates, but you should confirm requirements for your locations with an employment adviser.

Resumes contain personal data. Access is restricted, data stays in your systems, and retention follows your agreed policy. Candidates can ask what data you hold, and your team can answer quickly and accurately because it is all in one place.

Keep exploring

FAQ

Questions about screening assistant

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