The resume is dead.Start from what they actually built.
Every hiring tool tries to filter candidates better, resumes, sourcing, AI interviews. They all operate on the same problem: weak, repeatable signals.
Clyris ranks real work and generates interviews from it, so you run fewer interviews, catch strong engineers earlier, and eliminate 50% of wasted hiring loops.
5 free analyses · No credit card · Pricing plans for higher volume
Sample analysis output
3 repos · analyzed 2h ago
Overall
7.50
Final verdict
Strong Senior · Ships AI features end-to-end, not just wrappers
"Rare combination of system-level depth and full-stack agility."
Questions from their code
You invalidated tokens at the edge, walk me through that decision.
The retry logic uses fixed backoff. What tradeoffs were you weighing?
+ 5 more tailored questions
From code to hiring decision.
How real work turns into a clear hire / no-hire call.
Every repo gets a real read.
Upload a project, repo or take-home. Clyris breaks down how the code is written, how deep the engineering goes, and what it signals about the person who built it, with clear reasoning.
- Architecture, depth, and production-readiness, not surface-level checks.
- Strengths and gaps surfaced with evidence from actual code.
- Scores that mean something, each tied to specific signals.
All their work. One candidate read.
Analyze multiple repos for the same person. Clyris synthesizes them into a single view, overall signal, seniority, and what actually matters.
- Clear candidate summary with final judgment.
- See which projects drove the signal, and why.
- Tech proficiency inferred from real implementation.
Your pipeline. One decision.
Candidates ranked against each other and the role. One output: who to hire, who to skip, and why, with the deciding factor and confidence level.
- Side-by-side comparison grounded in real work.
- "Hire X. Confidence: Medium.", clear, actionable output.
- Everything distilled into a single hiring brief.
Questions they can't prepare for.
Every question is built from the candidate's own code, tied to specific decisions, tradeoffs, and patterns, with guidance on what a strong answer looks like.
- Grounded in their actual implementation, not generic questions.
- "What to listen for" included for every question.
- Designed to probe real engineering thinking.
Your slot booking flow checks availability then uses connectOrCreate inside a Prisma transaction. How do you prevent two concurrent requests from booking overlapping slots?
Code evidence: explicit 'check then act' pattern; correctness under concurrency depends on DB constraints.
const appointment = await this.prisma.$transaction(async (prisma) => {
const slots = await prisma.slot.findMany({
where: { id: { gte: slotStart, lte: slotEnd }, salonId, barberId, date },
orderBy: { id: 'asc' },
});
if (slots.length > 0 && !slots.every(s => s.appointmentId === null))
throw new BadRequestException({ error: 'Slots not available' });
return prisma.appointment.create({
data: { slots: { connectOrCreate: createOrConnectManySlots }, ... },
});
});What to listen for
Mentions unique constraints, serializable isolation or explicit row locking, transaction retries on conflict.
Assumes transactions alone solve race conditions without constraints or locking.
Know who to advance before interviews
Ranking gives you ordered candidates based on actual skills and depth, not gut feel or resume order.
Run fewer, higher-signal interviews
Focus only on candidates worth spending time on.
Make decisions you can defend
Every recommendation is backed by evidence from real code. The ranking and reasoning stay. Months later you can point to exactly why you chose who you chose.
Start with the Project layer. Unlock the full system.
What you can use today is the same engine Clyris runs on. The full product builds on top of it, ranking candidates, comparing them, and making clear hiring decisions.
Analyze real code
Get a scored breakdown of how the project is built, what it signals about the engineer, and what to ask in an interview.
- Upload repos or take-homes from GitHub, GitLab, Bitbucket
- Scored project analysis with engineering assessment
- Key signals: strengths, gaps, production-readiness
- Interview questions generated from real code
- Free plan available · pricing for higher volume
Current scope
- –Project-level only, no synthesis across multiple repos
- –No ranking or comparison across candidates
- –No job description alignment
Make hiring decisions, not just evaluations
Built on the same analysis layer, adds candidate synthesis, ranking, and clear decisions across your pipeline.
- Candidate-level synthesis across multiple repos
- Ranking across candidates against your role
- Clear comparison: who to advance, who to skip, and why
- Structured interview brief built from actual code
- Final recommendation with confidence and deciding factors
- Volume discounts
The analysis layer is fully usable today. The full system expands it into complete hiring decisions.
What you stop doing when you hire this way
Most of the cost in hiring isn't the decision. It's everything you do before getting to one.
Stop screening resumes
Resumes tell you what candidates want you to think. Code tells you what they can actually do. Clyris makes repos the first filter, not the last resort.
Stop running coding rounds
LeetCode is coachable. System design is memorizable. Questions built from a candidate's own code can't be rehearsed, only answered by someone who wrote it.
Stop adding rounds to compensate
Teams pile on interviews when early signal is weak. Strong early signal means one focused conversation instead of four exploratory ones.
Stop making decisions you can't explain
"They seemed strong" doesn't hold up. Clyris produces a ranked, reasoned output your whole team can point at, during the process and months later.
Fewer loops. Stronger bar. Faster close.
The interviews don't disappear, they just start from actual knowledge instead of hope.
How it works
From raw code to a clear hiring decision, without relying on resumes or generic rounds.
Start from real work
Upload code, repos or take-homes. Clyris works across GitHub, GitLab, Bitbucket, or direct uploads.
Repos · Take-homes · Private or publicExtract signal from code
Each project is analyzed for depth, structure, and decisions, turning raw code into clear, comparable signal.
Scores · Strengths · Gaps · Interview questionsTurn signal into decisions
Across projects and candidates, Clyris helps you prioritize who to move forward, and why.
Comparison · Filtering · Interview focusBuilt for AI-native hiring
Most hiring tools were built for a world where candidates couldn't generate polished code on demand. That world ended.
Proof of work, not proof of writing
Resumes are optimized for keyword matches, not engineering depth. Starting from actual code removes the layer every candidate, AI-assisted or not, has learned to game. Your bar stays where it is. The signal finally catches up to it.
Domain depth, not claimed seniority
A candidate who lists "React, Node, AWS" could mean anything from weekend project to production system at scale. Clyris reads the actual implementation, architecture decisions, depth signals, production-readiness, not the label. "0–2 years backend" tells you nothing. The code does.
Interviews that survive AI assistance
Generic system design questions are solved by ChatGPT in seconds. Questions built from a candidate's own code aren't, they require actually understanding the decisions they made. Clyris generates those questions automatically, so every interview is a builder conversation, not a trivia round.
From signal to decision.
The full system takes all analyzed work across your pipeline and turns it into structured decisions, who stands out, who to skip, and what actually matters.
Instead of filtering candidates one by one, you see the entire pool clearly, grouped by domain, compared on real work, and reduced to a small set worth interviewing.
Early access, see it on your own pipeline.
Want to see it on your own pipeline before the call? Mention it when you book.
Move beyond role-based hiring
Want hybrid roles (frontend + backend + applied AI). Use domain filters to surface the right profiles instantly.
Deciding factor analysis
The specific signal that separated stronger candidates from the rest, with evidence from their code. See how candidates compare against each other, across projects, not resumes.
Interview brief built from code
Questions, context, and what to listen for, generated from real implementation decisions.
Clear advance / skip decisions
Not just scores, a structured explanation of who to move forward and why.