For agencies doing 50+ placements/year
Your AI team screened 412 CVs, scheduled 22 interviews, and flagged 1 red flag reference while you slept.
Six AI employees work inside your Greenhouse, Lever, and LinkedIn. They screen CVs in 30 seconds, keep every candidate informed, schedule multi-panel interviews, match roles to your existing database, generate client reports, and complete references in 1.5 days — then send you one Slack message with [Approve] or [Review]. You make 10-15 decisions per day. They handle everything else.
412 applications · 35 shortlisted · Top match: 94%
#1: James Park — 8yr PM, B2B SaaS, ex-Stripe. 94% match.
Referee rated 2/5 on team collaboration. "Preferred working alone."
Real examples from this morning
Here is what your AI team would have caught today
Each example below is a real scenario that costs recruitment agencies thousands per month. Your AI employees catch them automatically and ask you what to do.
Your Senior PM role received 200 applications over the weekend. Your consultant will spend Monday and Tuesday screening them -- 13+ hours of reading CVs. Meanwhile, the top 3 candidates are interviewing with competitors by Tuesday afternoon.
"200 CVs processed. 12 shortlisted (85%+ match). Top match: James Park, 94%, 8yr PM at Stripe. [View Shortlist]"
David Kim accepted an offer for a Frontend Engineer role. Start date is in 2 weeks. References have been pending for 5 days -- one referee is not responding, the other submitted a concerning review. The client is asking why the start date keeps slipping.
"Reference flag: David Kim. Referee 1 rated 2/5 on team collaboration, noted 'preferred working alone.' Referee 2 completed: 4.5/5, strong endorsement. [Review Full Reference]"
A new Director of Engineering role just landed from your best client. Your team plans to post on LinkedIn and Indeed -- $2,400 in job board fees. Meanwhile, you have 8,000 candidates in your ATS from the last 3 years, 6 of whom are perfect matches that nobody remembers.
"6 database matches for Director of Engineering. Top: James Park, placed as Senior Engineer 18 months ago, updated LinkedIn last week. [View Matches]"
Your top candidate for a $180K role has been in process for 2 weeks. She has not heard from your agency in 4 days. She just received a competing offer with a 48-hour deadline. She accepts it because she assumed your process had stalled.
Ava would have sent a status update on Day 2: "Hi Sarah, your candidacy is progressing -- the panel is reviewing feedback and we expect a decision by Thursday. I will update you as soon as I hear." Candidate stays engaged.
How it works
Your AI employees work. You approve.
They work inside your ATS
Greenhouse, Lever, LinkedIn, Calendar. No new software.
They surface decisions
Review this shortlist? Schedule this panel? Flag this reference? Send this report?
You approve on Slack
Full context + buttons. Each decision takes 5-15 seconds.
They execute
Shortlist sent, interview booked, report delivered. You close placements.
Thursday 2:00 PM EST · 3 interviewers across 2 time zones · Zoom link generated
Scorecards distributed · Candidate briefing pack sent
Pipeline: 142 candidates total · 23 interviewing · 4 offers pending
Alert: Marketing Manager trending 20% over SLA. Rec: adjust salary range.
Your AI operations team
Six employees. Six real job descriptions.
Each one replaces a hire you cannot afford yet -- or a role nobody is doing consistently.
- Processes every CV in under 30 seconds, any format
- Multi-dimensional scoring: skills, trajectory, cultural fit
- Ranked shortlists with match rationale and interview questions
- Personalized acknowledgments within 15 minutes
- Status updates, interview prep, offer coordination
- 25% reduction in candidate dropout
- Multi-panel scheduling across time zones in hours
- Scorecard distribution and candidate briefing packs
- Automatic rescheduling without recruiter involvement
- Semantic search across your entire ATS database
- Passive candidate identification with LinkedIn signals
- 35% more placements from existing talent pool
- Pipeline reports with time-to-fill and source analysis
- Proactive risk flagging before SLA breach
- Cost-per-hire and source effectiveness tracking
- Mobile-friendly forms with 85% referee response rate
- Structured summaries with red flag detection
- Complete in 1.5 days instead of 5
A typical Monday
What your AI team does before you finish your standup
The math
What you are paying now vs. what you could pay
| Role | Human cost | AI employee | What changes |
|---|---|---|---|
| Tyler -- Screening | 15+ hrs/wk recruiter time | Included | Every CV scored in 30 seconds |
| Ava -- Comms | $35-45K/yr coordinator | Included | Every candidate updated within the hour |
| Ben -- Scheduling | 8+ hrs/wk admin time | Included | Multi-panel interviews in hours, not days |
| Hannah -- Matching | Senior recruiter time | Included | 35% more placements from existing database |
| Charlie -- Reports | Nobody (ad-hoc) | Included | Automated pipeline reports with risk flagging |
| Nina -- References | 2-3 hrs/candidate | Included | 1.5 days instead of 5, red flag detection |
| Total | $80-130K/yr + lost placements | Fraction of the cost | 28% faster time-to-fill |
A resume parser
Bullhorn Amplify and HireEZ parse CVs. That is one step. Screening is 15% of the problem.
Another ATS feature
Greenhouse and Lever have automation. You still configure, trigger, monitor, and intervene. Every day.
AI employees who do the work
Six employees, six roles, six sets of daily deliverables. They work. You close placements.
Works inside your existing tools
Frequently asked questions
Does the AI CV screening have bias?
Screen is designed to mitigate bias in screening. It evaluates candidates on skills, experience depth, career trajectory, and qualification relevance — never on age, gender, ethnicity, or protected characteristics. Every score includes an explanation of contributing factors so your compliance team can audit decisions. The algorithm is regularly tested against bias benchmarks and adjusted accordingly.
How does the AI integrate with Bullhorn?
All 6 agents integrate bidirectionally with Bullhorn via its API. Screen reads job requirements and writes candidate scores and summaries. Connect reads candidate contact details and writes communication logs. Schedule reads interviewer availability and writes confirmed appointments. Update reads pipeline data and generates client reports. All operations happen within your existing Bullhorn workflow.
What is the candidate experience like with AI agents?
Candidates receive immediate, personalized communication rather than silence. Connect sends acknowledgment emails within minutes of application, schedules phone screens with available time slots, and keeps candidates informed at every stage. The communication is personalized — referencing the specific role, the candidate's relevant experience, and clear next steps. Candidate response rates run at 90% within the first hour.
Can AI agents automate reference checking?
Yes. Verify contacts provided references, conducts structured reference checks via phone or email, transcribes responses, and highlights any concerns for recruiter review. It asks role-specific questions based on the position requirements and flags inconsistencies between the candidate's claims and referee feedback. All reference data is stored within your ATS with full audit trails.
How does the AI handle GDPR for candidate data?
All candidate data is processed with lawful basis under GDPR. Data retention policies are enforced automatically — candidates past the retention window are flagged for review or deletion. Right-to-erasure requests propagate across all agent systems. Candidates are only contacted through consented channels. Unsubscribe requests are processed immediately. All data handling is logged and auditable.
How much does AI improve time-to-fill?
Agencies using Bitontree agents report a 28% reduction in average time-to-fill. Screen processes 200+ CVs per role in minutes rather than days. Connect engages top candidates within the hour. Schedule eliminates days of back-and-forth on interview coordination. The combined effect compresses the hiring cycle significantly, particularly for high-volume roles.
Can AI agents identify passive candidates in our existing database?
Yes. Match searches your existing ATS database to identify candidates who were not active applicants but whose profiles match current open roles. It considers recency of last contact, stated availability, and skill fit. Each flagged candidate includes a note on when they were last engaged and a recommended outreach approach, ensuring your existing candidate pool is fully leveraged before external sourcing.
How long does deployment take for a recruitment agency?
A typical 6-agent recruitment deployment takes 6-10 weeks. Phase 1 (Workforce Discovery, 2 weeks) maps your sourcing, screening, and placement workflows. Phase 2 (Build & Deploy, 4-8 weeks) deploys agents incrementally, starting with Screen and Connect which deliver the fastest throughput improvement. Phase 3 is ongoing optimization based on placement data and candidate feedback metrics.
Can AI agents write job descriptions and requisitions?
Yes. Screen generates job descriptions based on the hiring manager's requirements, calibrated against successful past requisitions for similar roles. JDs include must-have vs. nice-to-have skill differentiation, salary band guidance based on market data, and inclusive language review. Each JD is staged for hiring manager approval before posting to job boards.
How does Boolean search and proactive sourcing work?
Match generates optimized Boolean search strings for LinkedIn Recruiter, Indeed, and other sourcing platforms based on the role requirements. It searches your existing ATS database first (passive candidates who were strong fits for previous roles), then identifies external candidates. Each sourced candidate includes a match explanation and recommended outreach message.
Can AI agents support offer negotiation?
Match provides market compensation benchmarking for the specific role, location, and experience level — including base salary, equity/RSU ranges, sign-on bonus norms, and benefits expectations. When a candidate counters, Match generates a comparison analysis and recommended response range. All offers require recruiter and hiring manager approval — Match provides the data, humans make the decision.
How do AI agents handle background checks?
Verify coordinates background checks through integrated screening providers, including criminal history, education verification, employment verification, and credit checks where legally permitted. Results are compiled into a structured summary with any flags highlighted. Adverse findings are escalated to the recruiter and hiring manager with the specific details and recommended next steps per FCRA requirements.
How much do AI agents for recruitment cost?
Bitontree Workforce pricing is per-agent, not per-req or per-placement. Most agencies start with Screen (CV parsing) and Connect (candidate communication) for fastest throughput improvement. Pricing scales with requisition volume and ATS integration complexity. Most agencies see ROI within the first quarter as recruiters handle significantly more open roles. Contact us for a custom quote.
Will AI replace recruiters?
No. Bitontree Workforce AI agents handle the volume work that prevents recruiters from building relationships — CV screening, outreach, scheduling logistics, reference administration, and pipeline reporting. Your recruiters still do discovery calls, sell the opportunity, negotiate offers, manage client relationships, and close placements. AI handles the operational throughput; humans handle the judgment and relationship work that actually places candidates.
Your first hire takes 15 minutes
Book a Workforce Discovery session. We map your sourcing, screening, and placement workflows and show you which AI employees would have the biggest impact on your pipeline velocity.