This MVP was built to help recruitment teams cut through large volumes of applications by summarizing each candidate, organizing them by relevant experience, and streamlining the early screening steps that otherwise consume the most recruiter time.
Recruitment teams often face a stack of applications that vary wildly in format and length, with relevant experience buried in inconsistent places. Reviewing each one in full is slow, and it's easy for a strong candidate to get lost in the volume.
This MVP focused on giving recruiters a faster first pass — a summary of each candidate and a way to see who actually has relevant experience — without removing the recruiter's judgment from the final decision.
Recruiters were spending most of their time reading full applications just to determine which candidates were even worth a closer look, leaving little time for deeper evaluation of the strongest fits.
Applications describe experience in inconsistent formats and terminology, making it easy to overlook a genuinely relevant candidate whose resume didn't use the expected keywords or structure.
Without a shared way to group and compare applicants, recruiters were often screening candidates in submission order rather than in order of relevance to the role.
A screening assistant that summarizes applications and organizes candidates by relevance, so recruiters can focus their attention where it counts.
Each application is condensed into a short, readable summary, letting recruiters get a sense of a candidate in seconds instead of reading a full resume and cover letter.
Experience relevant to the specific role is surfaced within each candidate's summary, helping recruiters spot strong fits even when a resume doesn't use expected keywords.
Applicants can be grouped and ordered by relevance to the role rather than submission date, so recruiters review the strongest candidates first.
Candidates can be moved through screening stages within the same view, giving recruiters and hiring managers a shared picture of where each applicant stands.
Recruiters can compare summarized candidates against each other directly, making shortlisting decisions faster than switching between separate documents.
Recruiters can add their own notes next to each AI-generated summary, keeping human judgment part of the record rather than replacing it.
We looked at how recruiters were actually screening applications today, identifying where the most time was lost — largely in reading full applications just to make an initial pass/no-pass call.
We designed how candidate summaries should be structured and what "relevant experience" needed to mean for the specific roles being hired for, so highlighting stayed meaningful rather than generic.
We built the summarization and relevance-highlighting engine, testing it against real applications to confirm summaries stayed accurate and useful, not just shorter.
We built the candidate organization board and comparison view, focused on making it fast to move from a stack of applicants to a shortlist.
We validated the full screening flow with the people who would actually use it day to day, confirming the tool sped up their first pass without removing their control over decisions.
We measured this MVP by how much faster a recruiter could get to a shortlist, not by how much text the AI could generate.
Candidate summaries were designed around what recruiters actually need to decide on a next step, not a generic resume rewrite.
Highlighting relevant experience was built to reference the specific role's requirements rather than generic keyword matching, reducing the chance of overlooking a strong but differently-worded candidate.
The system was built to support recruiter judgment with summaries and organization, not to make hiring decisions or filter candidates out automatically.
× Recruiters read full applications just to make an initial screening decision
× Relevant experience was easy to miss due to inconsistent resume formats and terminology
× Candidates were reviewed largely in submission order rather than by relevance
× Comparing candidates meant switching between separate documents manually
✓ Recruiters review a short summary of each candidate before deciding on next steps
✓ Relevant experience is highlighted against the specific role's requirements
✓ Candidates are organized by relevance, surfacing strong fits earlier
✓ Recruiters can compare candidates side by side within the same view
The goal was never to have AI decide who gets hired — it was to help recruiters reach their own decisions faster.
By focusing on summarization and relevance-based organization rather than automated filtering, this MVP gives recruitment teams a faster first pass while keeping the actual evaluation in human hands.
"Good recruitment tooling gives people more time to judge, not less reason to.
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If your recruitment team is spending more time reading than deciding, we can help you scope an MVP that speeds up screening without taking judgment out of the process.
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