For · Headhunters / RPO / recruiting service providers

Same team, double the placements

Built for the trade — extreme efficiency, professional tooling.

Screening cost ↓75% 50% time saved Channel cost ↓40%
Capacity model

One consultant + one AI engine = a delivery team

AI parallelises the time-sinks — sourcing, screening, outreach — so consultants only judge and close. Same headcount, several times the roles in flight.

顾问
1 consultant
Judgement & closing only
+
AI engine · in parallel
Talent search
NL sourcing at scale
Parsing
Instant structured screening
Recruiting Agent
Bulk outreach & follow-up
Matching
Auto-rank best fit
N roles in flight Output / head ×N
Java Architect
Head of Product
ML Engineer
Data Analyst
Frontend Engineer
Product Designer
Sales Director
Marketing Manager
Finance BP
HR Business Partner
The challenge

For headhunters and RPO, efficiency is everything — capacity is revenue.

Scenario
Scaling by headcount
Adding consultants adds cost, not placements.
Scenario
Screening eats the day
Screening devours the day; there’s no time left to close.
PainTodayCost
Slow screening Consultants read resumes by hand Time lost on junk resumes
Slow sourcing Keyword search, manual digging Long delivery cycles
Costly channels Buying resumes on many platforms Channel spend stays high
Scattered outreach Manual messaging, lost follow-ups Conversions leak away
Rollout plan

A clear rollout — every step has a metric

01 Week 1–2
Onboard
Import your talent pool, enable search
Faster sourcing, visibly
02 Month 1
Ramp up
Automate screening & Agent outreach
Screening cost drops
03 Month 2–3
Scale
Full workflow, multi-consultant teamwork
Placements per head doubled
Proof
Screening ↓75%
Time saved 50%
Channels ↓40%
With Xiaoxi, RHR multiplied the open roles each consultant can run.
客户 logo
Customer story

Make every consultant a team.

Deployment
Public-cloud SaaS or private deployment with in-domain data; open APIs and a dedicated account manager.
Public cloud SaaS Private deployment
* Figures shown are illustrative and will be replaced with measured or industry-benchmark data before launch; customer-story placeholders are pending real cases.