A CV tells you what someone claims.
Mia helps you understand what it means.
Mia analyzes a candidate’s experience against what actually matters for the role — the work they’ve done, the context behind it, and the evidence that supports the match.
Every evaluation is connected to evidence your recruiting team can review.
Candidate experience, understood in context. Decisions grounded in evidence.
Matching skills is easy.
Understanding experience is harder.
Finding Python, Enterprise Sales, Kubernetes, or any other skill on a resume doesn’t tell you how deeply someone used it, what they were responsible for, or whether that experience is relevant to the job.
Mia looks at responsibilities, projects, ownership, seniority, outcomes, domain, and the environments in which that experience was built.
Not just what they know.
What they’ve actually done with it.
Not just where they worked.
What kind of environment they worked in.
Not just their title.
What they actually owned.
Not just years of experience.
How relevant those years are to this role.
Mia evaluates experience, not just terminology.
The job description is only the beginning.
Your team usually knows things about the ideal candidate that never make it into the public job description. Ownership may matter more than years of experience. A particular domain may be critical. One technical skill may be essential while another is nice to have.
Mia combines the job description with the hiring criteria, priorities, and nuances your team defines before evaluating candidates.
Critical. Important. Lower priority.
Criteria contribute differently to the overall evaluation based on how important they are for the role.
The criteria behind the match.
Hard skills, soft skills, relevant experience, years of experience, seniority, domain knowledge, ownership, leadership, and specific types of projects.
You define what matters.
Mia looks for the evidence.
Every score should have a reason.
Mia evaluates different parts of the candidate’s profile and combines them into an overall score based on your hiring priorities. But the score isn’t the interesting part. The evidence is.
For every evaluation criterion, recruiters can see what Mia found in the CV and which parts of the candidate’s experience support it.
From evaluation to original CV.
Select the supporting evidence to see the corresponding part highlighted directly in the original CV, without manually searching through the resume.
Ownership — strong evidence
“Owned the rollout across three markets.”
“Led the end-to-end migration of the billing platform.”
See the CV evidence and gaps behind each score.
Experience doesn’t happen in a vacuum.
A title only tells you part of the story. The company around that experience matters too.
Mia researches publicly available information about previous employers: their websites, products, markets, business models, funding events, growth, and relevant public news.
This provides context for the candidate’s experience and identifies useful areas to explore. Public research does not prove an individual’s performance or explain why they left.
A title can mean very different things.
“Head of Sales” at a five-person startup and in a 500-person organization can represent very different responsibilities. Company context helps your team see what ownership should be clarified.
Timing can create useful questions.
A salesperson leaving after a funding round does not explain why they left or mean they performed badly. It may create a useful question for the next conversation.
Relevant experience can hide behind an irrelevant title.
A previous company may have sold to exactly the customers, operated in the market, or solved the problem relevant to the new role — even when the candidate’s title looks different.
Research creates better questions.
Not assumptions.
Know what’s strong.
Know what’s missing.
Know what to ask.
Mia turns the analysis into something a recruiter can use, combining these signals into an overall evaluation based on the criteria and priorities configured for the role.
Strengths
Evidence that supports the candidate’s fit against your key hiring criteria.
Gaps
Where the CV provides limited or no evidence of the required experience, and clarification is needed.
Context
Relevant information about the experience and the companies where it was built.
Questions
What should be clarified before your team makes the next decision.
CV Intelligence shapes the conversation.
CV Intelligence doesn’t disappear when the candidate reaches the interview. The role, hiring criteria, CV, evidence, gaps, previous-company research, and unanswered questions become context for the conversation.
Mia noticed
The candidate says they led five engineers, but their management responsibility is unclear.
So Mia can ask
“You mention leading five engineers during the migration. Were you responsible for managing the team, or were you acting as the technical lead?”
The interview starts where the CV stops.
Mia gives your team evidence.
Your team makes the decision.
Understand candidates faster and more consistently without turning hiring into an unexplained pass/fail decision.
Your team can review the evidence, inspect the original CV, understand the gaps, see the context, and decide what happens next.
Use Mia with the recruiting setup you already have.
Different companies can bring the same Mia into their process in different ways.
Already have an ATS?
Analyze applications and add candidate intelligence inside your existing recruiting workflow.
Mia in your ATSWant candidates to interact with Mia immediately?
Start the conversation as soon as they apply through your career page.
Mia on your career pageDon’t have an ATS?
Run jobs, candidates, analysis, and interviews in the MakeHire recruiting platform.
MakeHire Recruitment PlatformDon’t just screen the CV.
Understand the candidate.
See how Mia turns candidate experience into evidence, context, and better questions for your recruiting team.