Recruiting & Talent
The AI workspace for recruiting and talent teams.
Source candidates, enrich profiles, and keep pipelines warm without switching between six tools.
The workflow today.
The friction your team works through every week.
01
Candidate research lives in LinkedIn tabs and shared spreadsheets that never quite agree.
02
Enriching hundreds of profiles by hand is how recruiters burn out.
03
Interview debriefs scatter across Slack threads and Notion docs, so the decision still rests on memory.
How Clavy helps.
A unified workspace for your research, documents, and agents, built for any workflow.
Source and enrich in one pass
Pull background, signals, and motivation across an entire candidate list.
Every role and rubric in one place
Job specs, scorecards, and notes stay searchable with citations.
Ask across your pipeline
Chat across requisitions and candidate tables to see where every search stands.
Use cases.
Common workflows teams run on Clavy. Each one starts as a table and scales from there.
Candidate | Move signalAI | Top matchAI | Gap to watchAI | Fit scoreAI | |
|---|---|---|---|---|---|
1 | Sarah Kim | 3+ years at Stripe; recent GitHub activity shows growing interest in early-stage AI infra beyond her current scope | 3+ years of LLM production work at Stripe; rare at this level | No public signal of being open to new roles | 94 |
2 | Marcus Lee | Publicly posting about AI developer tooling gaps; 2 years at Vercel suggests the itch to do something new is building | Staff-level infra at Vercel with deep Next.js architecture exposure | Toronto-based; confirm remote flexibility | 88 |
3 | Priya Patel | Recent conference talks signal interest in applied work; lab research track limits product impact | NeurIPS paper co-authored in this exact research area | Research background; applied scope may take adjustment | 91 |
4 | Daniel Wu | 18 months at Linear; posting about design system constraints suggests desire for broader product ownership | Built the design system from scratch; strong taste and craft | Primarily frontend; limited backend systems exposure | 82 |
5 | Elena Vasquez | 8 months of vesting left at Figma; posted about wanting to work closer to ML in her next role | ML plus design-tool background is a rare and exact fit | Equity still vesting; timing the approach matters | 89 |
6 | James Park | Under 2 years at Ramp; publicly posting about wanting to expand scope beyond payments infra | Payments infra at scale across multiple high-growth fintechs | Short tenure; may be building toward a staff promotion first | 85 |
7 | Aisha Rahman | Blogged about wanting to see research applied in products; recent papers are increasingly applied | ICML paper on this exact problem domain; 5-year pedigree | London-based; confirm timezone and remote policy | 93 |
8 | Tom Becker | 3 years at Airtable; recent posts suggest frustration with enterprise pace and interest in smaller teams | Collaborative database products at scale is directly relevant experience | No ML background; learning curve on the AI product side | 80 |
9 | Nina Kapoor | Actively engaging with AI-native startup content on LinkedIn; tenure suggests she is ready for a step up | Background maps directly to the use-case problem space | Applied ML track; not positioned for research-heavy work | 87 |
10 | Chris Yang | 2 years at Webflow; consistently engaging with design-engineering content and early-stage company posts | CSS and animation depth; strong match for the frontend role | No enterprise-scale shipping experience yet | 83 |
Enterprise-grade security.
Security built into every layer, from encrypted data storage to role-based access and a firm policy that your data is never used to train foundation models.
Run it on your data.
Bring a real use case to the demo. We will show how it runs on Clavy end to end.