Capability track
Evaluate experience, skills, and transferable capability against role responsibilities with cited examples.
AI resume matching
AI resume matching should not stop at one score
Reliable resume matching answers three different questions: can the candidate perform the work, do they meet non-negotiable eligibility conditions, and is the opportunity practically feasible? Aimme evaluates these tracks separately and keeps verifiable candidate evidence beside each conclusion.
The recruiting problem
Keyword matching treats the presence of a term as proof of capability. A single total score can let impressive experience hide a failed hard requirement. More seriously, a model may infer unstated information as fact, leaving recruiters unable to distinguish evidence from speculation.
How Aimme handles it
Break recruiting judgment into inspectable steps
Eligibility gates
Check explicit requirements such as certification, language, education, or work authorization separately.
Feasibility track
Identify what is known about location, availability, compensation range, and other practical constraints.
Evidence and unknowns
Mark support, counter-evidence, conflict, or unknown rather than imagining missing resume facts.
From question to reviewable result
Preserve context—and the opportunity to stop and review
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01
Decompose requirements
Turn a natural-language job description into criteria the team can confirm and inspect.
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02
Locate evidence
Find facts relevant to each requirement in resumes and other authorized candidate material.
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03
Produce track-level results
Present capability, eligibility, feasibility, and the overall recommendation separately.
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04
Review
Inspect strongest evidence, key risk, and questions that should be validated next.
Why the result is reviewable
Fluent model output is not treated as fact
- A match matrix places every role requirement beside the candidate evidence.
- A failed hard gate cannot be offset by unrelated capability points.
- Missing information remains unknown and becomes a screening or interview question.
- Match versions retain requirement changes instead of overwriting historical conclusions.
Operating boundaries
Be explicit about what the system does—and does not do
- Aimme does not recommend candidates based on protected traits such as name, gender, age, or ethnicity.
- Model output is recruiting support, not a hiring, rejection, or compensation decision.
- Missing evidence, conflicting sources, and unknown hard requirements are surfaced for human verification.
- Customers control candidate data. It is not used to train public models, and teams remain responsible for lawful, platform-compliant use.
Frequently asked questions
Answer the important questions first
Is an AI resume-match score accurate?
A score is meaningful only when the role criteria, evidence quality, and human calibration are explicit. Aimme treats it as prioritization and review support, not a hiring probability.
What if a skill is not stated on the resume?
The system marks insufficient evidence or unknown and proposes validation through screening, a portfolio, or an interview instead of assuming absence.
Does Aimme score names or age?
No. Matching should use job-related capability, eligibility, and feasibility evidence, not protected characteristics.
Aimme
Start with one real role
Describe an active role. Aimme clarifies the requirements, searches and matches candidates, and organizes the evidence. Your recruiting team confirms every progression decision.