Cut through keyword-stuffed CVs. Aster matches on actual skills and experience, and normalises 'JS' to 'JavaScript', so genuine engineers rise to the top.
The problem
Hiring the way most teams still do it, before Aster does the first pass for you.
How Aster helps
Skill-level matching
Rank candidates by the skills and stack you actually need, with synonym and typo handling.
Fit with reasons
A match score that explains why a candidate fits, not just a number.
Move at engineering speed
Self-scheduling and auto-invites keep strong candidates from going cold.
Panel-ready interviews
Add reviewers, share structured questions, and score the same criteria.
Aster reads past the keyword soup and surfaces the candidates whose experience actually matches the role.
3×
faster shortlists
46 → 3
applicants to a shortlist
~2 weeks
sooner to a hire
Typical results teams see after switching to Aster.
More by industry
Technical resumes are noisy. One candidate lists "JS," another writes "JavaScript," a third buries "Node.js" three bullet points down under a project nobody will click into. Keyword-matching tools reward whoever guessed the right phrasing, not whoever can actually ship. Meanwhile the engineers who can build often play the resume game badly: no bullet-point theater, just solid work described plainly, so they get passed over while a recruiter's queue fills with people who wrote "full-stack ninja" forty times but can't explain a database index or walk through a system they actually built end to end.
How it works
Parse every resume automatically
Aster reads each resume into structured data, skills, years, past roles, tools, the moment it lands, whether it came from your career site, a job board like LinkedIn or JobStreet, or a forwarded email. Nothing stays buried in a paragraph nobody has time to read closely.
Normalize skills, resolve synonyms
Aster maps each parsed skill against what the role needs, treating "JS," "Javascript," and "ECMAScript" as one skill, and catching typos like "Djnago" or "Kubernentes." This levels the field between a polished resume and one written plainly by someone who can actually do the job.
Review the match score
Every applicant lands in your pipeline with a match score and the reasoning behind it, which skills matched, which are missing, how close the experience level is. You open a profile already knowing why it's there, instead of reading top to bottom to find out.
Move fast into interview
Candidates self-schedule their own interview slot, and Aster generates the Google Meet or Teams link automatically. Your panel runs from AI-drafted questions and a shared scorecard, so strong engineers move from applied to interview in days, not weeks.
In depth
Resumes describe the same skill a dozen different ways, and no two candidates write theirs alike. Aster's skill matching resolves "JS" to JavaScript, "k8s" to Kubernetes, "PSQL" to PostgreSQL, and catches the typos that show up when people write fast, "Djnago," "Reactjs," "Golange." It also understands adjacency: someone strong in Vue isn't automatically strong in Angular, but a candidate who's shipped with Next.js has a real head start on a role that needs React.
This runs the same way whether a resume arrives through your career site, LinkedIn, JobStreet, or a forwarded email, so the sourcing channel never determines how fairly someone gets read. The result is a pipeline that ranks people on what they've actually built, not on whether they happened to guess your job description's exact phrasing. Engineers who write plainly, without stuffing every buzzword they think you're scanning for, stop losing out to people who simply gamed the format better than they did.
Resumes describe the same skill a dozen different ways, and no two candidates write theirs alike. Aster's skill matching resolves "JS" to JavaScript, "k8s" to Kubernetes, "PSQL" to PostgreSQL, and catches the typos that show up when people write fast, "Djnago," "Reactjs," "Golange." It also understands adjacency: someone strong in Vue isn't automatically strong in Angular, but a candidate who's shipped with Next.js has a real head start on a role that needs React.
This runs the same way whether a resume arrives through your career site, LinkedIn, JobStreet, or a forwarded email, so the sourcing channel never determines how fairly someone gets read. The result is a pipeline that ranks people on what they've actually built, not on whether they happened to guess your job description's exact phrasing. Engineers who write plainly, without stuffing every buzzword they think you're scanning for, stop losing out to people who simply gamed the format better than they did.
In practice
Hiring a senior backend engineer
Forty resumes come in for one backend role, half listing "Node," a few "Node.js," one "NodeJS." Instead of manually normalizing each one by hand, Aster treats them as the same skill and ranks every applicant on real fit, years with distributed systems, database depth, and how closely their stack matches yours. You open your pipeline already knowing which five are worth a first call, with the reasoning attached to every score so you're not guessing.
FAQ
Aster maintains skill matching that resolves common synonyms, abbreviations, and typos that technical candidates and recruiters actually use day to day, JS to JavaScript, k8s to Kubernetes, Postgres to PostgreSQL, and similar variants across languages, frameworks, and tools. It also catches simple misspellings, so a strong candidate who typed "Djnago" instead of "Django" isn't scored as if they don't know it at all. This runs automatically on every resume that comes in, so you don't have to build or maintain your own list of aliases, and candidates aren't quietly penalized for writing their own skills the way they naturally would on a real resume.
The match score compares the skills, experience level, and industry background parsed from a resume against what the role actually requires, and it comes with the reasoning attached, which skills matched, which are missing, and how close the experience lines up with what you asked for. It's meant to help you triage quickly, not to make the hiring decision for you on its own. You still open profiles, read resumes, and run interviews, the score just tells you where to look first so your time goes to the applicants most likely to be worth it.
That's the point of matching on skills instead of keyword density. A plainly written resume that says "built and maintained a Django API handling 200k requests a day" scores on the substance of that sentence, not on how many buzzwords surround it or how the bullet points are formatted. Because Aster reads for actual skills and experience rather than phrasing or keyword repetition, someone who doesn't play the keyword game well isn't quietly filtered out before a human ever gets the chance to see them and judge for themselves.
Once a candidate is ready to move to interview, they get a link to pick a time from your team's open availability, no email chain, no back-and-forth required. Aster creates the Google Meet or Microsoft Teams link automatically and sends reminders by email or WhatsApp Business so the slot doesn't get missed by either side. If your process needs a full panel in the room, availability reflects everyone who needs to attend, so you're not left rescheduling a week later once you realize one interviewer's calendar was overlooked.
Aster drafts interview questions per role and per candidate as a starting point, grounded in the resume and the role's actual requirements, but panelists are free to adjust or add to them as the conversation goes. What stays consistent is the 1 to 4 scorecard every interviewer fills in, which is what rolls up into a shared team score. That consistency is what makes it possible to compare candidates fairly across a panel, instead of relying on whoever happened to write the most detailed notes afterward.
Skill matching covers common languages, frameworks, and tools along with their typical synonyms and typos, and it keeps improving as it sees more resumes and roles across a workspace. For a very new or niche technology it will still parse and match on the literal term as written. Your data stays scoped to your own workspace, encrypted in transit and at rest, and is never used to train shared models, so what you see reflects your own hiring, not aggregated data borrowed from other companies on the platform.
Technical hiring shouldn't reward whoever wrote the most convincing resume. It should reward whoever can actually do the job. Aster reads every applicant the same way, matches real skills instead of exact phrasing, and explains its reasoning so you can trust a ranking instead of second-guessing it line by line. Strong engineers move through your pipeline fast, self-scheduling their own interview and meeting a panel armed with relevant questions and a shared scorecard, instead of going cold while a slow process quietly decides for them. Screen for who can build, not who guessed your keywords best, and give the engineers worth hiring a process that finally moves as fast as they do.
Create your workspace and let Aster read, score and schedule for you. Free to start, no card required.