Parsing, match scoring, deduplication and candidate insights. Aster does the first pass on every applicant and hands you a ranked shortlist with reasons, not a pile.
Resume parsing
Drop in a CV and get structured skills, experience and a one-line summary in seconds. No manual data entry.
Role-fit match score
Every applicant is scored against the role with the reasoning behind it, so the best fits rise on their own.
Skill & industry matching
Search your database by the skills or industries you need and let AI rank everyone by fit, typos and synonyms included.
Deduplication
One person, one record, even when they've applied twice with an old and a new CV. No more double-counting.
Aster surfaces the best-fit candidates with reasons so your team reviews a shortlist instead of a stack. You always make the call.
Every open role turns into a pile of resumes in a dozen formats: PDFs from LinkedIn, Word docs from a career fair, plain text pasted into an application form. Somewhere in that pile is the person who is actually right for the job, but finding them usually means reading line by line, guessing at overlapping skills, and hoping you do not miss someone because their resume used a different word for the same experience. Most teams do not have hours to spare per role, so screening turns into a skim: recency bias, keyword luck, whoever happened to apply first. Aster Intelligence reads every resume the same way, every time, and hands you a ranked shortlist with the reasoning attached.
How it works
Parse every resume
Aster reads each resume, whatever the format or source, and extracts structured data: skills, job titles, employers, dates, and years of experience. No more skimming PDFs or fighting formatting quirks. Every applicant becomes a consistent, comparable record the moment they apply, regardless of which channel they came through.
Score against the role
Each candidate is scored against that specific role's requirements, not a generic template borrowed from another opening. The score reflects real overlap in skills, experience, and industry background, and it arrives with the reasoning behind it, so you know why someone ranked where they did before you even open their profile.
Match skills, catch synonyms
Matching accounts for how people actually describe their own work: synonyms, adjacent job titles, regional phrasing, and common typos. A candidate who wrote 'GCP' instead of 'Google Cloud Platform,' or 'acct payable' instead of 'accounts payable,' still gets matched correctly instead of falling through the cracks.
Merge duplicate candidates
When the same person applies more than once, through a referral, a job board, and your career site, Aster recognizes them and merges every application into a single record with full history. You see one candidate, one timeline, one score, never three fragmented entries to reconcile by hand later.
In depth
Resumes are not standardized documents. One candidate lists skills in a bulleted table, another buries them in a narrative paragraph, a third submits a resume built for a completely different industry. Aster Intelligence parses each one into the same structured shape regardless: named skills, job titles, employers, dates of employment, and total years of relevant experience. That structure is what makes everything downstream possible. Instead of a recruiter manually copying details into a spreadsheet or re-reading a PDF for the third time to check a start date, the data is already there, attached to the candidate's profile, searchable, and comparable across every other applicant for the role.
It also means nothing gets lost to a bad file format or an unusual layout. A resume exported from Canva and one exported from Word end up equally readable to the system, because the extraction is not looking for where information sits on a page, it is looking for what the information actually means.
Resumes are not standardized documents. One candidate lists skills in a bulleted table, another buries them in a narrative paragraph, a third submits a resume built for a completely different industry. Aster Intelligence parses each one into the same structured shape regardless: named skills, job titles, employers, dates of employment, and total years of relevant experience. That structure is what makes everything downstream possible. Instead of a recruiter manually copying details into a spreadsheet or re-reading a PDF for the third time to check a start date, the data is already there, attached to the candidate's profile, searchable, and comparable across every other applicant for the role.
It also means nothing gets lost to a bad file format or an unusual layout. A resume exported from Canva and one exported from Word end up equally readable to the system, because the extraction is not looking for where information sits on a page, it is looking for what the information actually means.
In practice
A support role draws 300 applicants in a week
A customer support opening goes out on LinkedIn and your career site and pulls in hundreds of resumes within days. Reading each one is not realistic on top of a full workload. Aster Intelligence parses and scores every applicant against the role as they arrive, so by the time you sit down, you already have a ranked shortlist with the reasoning behind each score, instead of an inbox you have not opened yet.
FAQ
The score compares the structured data parsed from a candidate's resume, their skills, job titles, years of experience, and industry background, against the specific requirements of the role they applied to. It is not a generic fit score reused across every opening. Alongside the number, you get the specific reasoning: which skills matched, which experience counted, and where the gaps are, so you can verify the score rather than just trust it.
Yes. Resumes arrive as PDFs, Word documents, and plain text, with wildly different layouts, and Aster parses all of them into the same structured shape: skills, titles, employers, dates, and experience. The extraction is built to find what the information means, not where it sits on the page, so unusual formatting, tables, or non-traditional resume layouts do not cause candidates to be misread or skipped.
That is exactly what synonym and typo tolerant matching is for. If your job description says 'accounts payable' and a resume says 'AP' or has a typo like 'acounts payable,' the match still recognizes the overlap. The same applies to adjacent job titles and industry background, so candidates are not penalized for describing their own experience in their own words.
No. It removes the hours spent reading resumes that were never going to be a fit and gives you a ranked, reasoned starting point. Every score and insight is visible and explainable, not hidden, so you can agree with it, question it, or override it based on context the system does not have, like a reference conversation or something said in an interview.
Aster looks across the details available on each application, including things like name, contact information, and resume content, to recognize when the same person has applied more than once, even through different channels or with small variations like a different email address. Matches are merged into a single candidate record with the full application history attached, so nothing is lost and nothing is duplicated in your pipeline.
Everything stays scoped to your workspace. Resumes, scores, and AI-generated insights and interview questions are encrypted in transit and at rest, and none of it is used to train shared models across other companies using Aster. You can export or delete your data at any time. The intelligence is built to work on your hiring, not to become training material for anyone else.
Hiring does not slow down because you stopped caring, it slows down because reading resumes one at a time does not scale to the volume good roles attract. Aster Intelligence takes on the part of the job that is repetitive and error-prone, parsing every resume the same careful way, scoring it against the actual role, catching the candidate who described their skills differently than you expected, and merging duplicates before they cost you a second look at someone you already met. What is left for you is the part that actually needs a person: reading the reasoning, trusting or questioning the score, and having the conversations that decide who gets hired. See what a ranked, reasoned shortlist looks like on your own open roles.
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