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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.

# recruiting-decisions
T
Tyler APP 7:04 AM
Shortlist Ready: Senior Product Manager (Acme Corp)
412 applications · 35 shortlisted · Top match: 94%
#1: James Park — 8yr PM, B2B SaaS, ex-Stripe. 94% match.
View Shortlist Send to Client Adjust Criteria
N
Nina APP 3:12 PM
Reference Flag: David Kim (Frontend Engineer)
Referee rated 2/5 on team collaboration. "Preferred working alone."
Review Full Reference Discuss with Client
4xscreening throughput
28%faster time-to-fill
10-15decisions/day on Slack
90 daysto positive ROI

Bitontree Workforce is an AI workforce platform that deploys 6 specialized AI agents for recruitment agencies and staffing firms, delivering 4x screening throughput with 28% faster time-to-fill: Screen (CV parsing and scoring against job requirements with experience depth analysis, skill adjacency matching, career trajectory evaluation, Boolean search string generation for proactive sourcing, blind screening mode for DEI compliance, technical assessment scoring, and red-flag detection for job-hopping patterns or resume inconsistencies), Connect (personalized candidate communication with sub-1-hour acknowledgment emails, phone screen scheduling with timezone-aware slot management, interview prep material delivery, stage-transition notifications, and multi-channel outreach via email, SMS, and LinkedIn InMail), Schedule (multi-party interview coordination across candidates, hiring managers, and panels with conflict resolution, room booking, Zoom link generation, interviewer scorecard distribution, debrief scheduling, and SLA tracking for time-between-stages), Match (passive candidate identification from existing ATS databases with recency scoring, availability windows, salary expectation alignment, competing offer detection, and recommended outreach messaging based on candidate engagement history), Update (automated client pipeline reports with requisition-level metrics: applications received, shortlisted, interviewing, offered, placed, declined — time-in-stage analytics, fill-date predictions, source channel ROI, and benchmark comparisons against industry averages), and Verify (reference check automation with structured questionnaires tailored to role requirements, transcription, sentiment analysis, concern flagging, background check coordination, and compliance documentation for hire files). All agents integrate with Bullhorn, Workday, Greenhouse, LinkedIn Recruiter, and Indeed — reading candidate profiles, writing placement notes, generating job descriptions, and updating pipeline stages within your existing ATS workflow. Every agent operates within GDPR/EEOC/Equality Act boundaries with bias-mitigated screening criteria and full audit trails on every scoring decision. Built as AI for staffing agencies and recruitment firms, the platform includes AI resume screening that evaluates candidates on skills and trajectory rather than keywords, helping agencies reduce time-to-fill by 28% while maintaining DEI compliance.

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.

T
Tyler CV Screening
The scenario

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.

What Tyler sends you

"200 CVs processed. 12 shortlisted (85%+ match). Top match: James Park, 94%, 8yr PM at Stripe. [View Shortlist]"

Without Tyler 13 hours screening. Top candidates interview elsewhere. Placement lost: $22K fee.
With Tyler Shortlist ready by 7 AM Monday. Client presentation by noon. First to submit.
N
Nina Reference Checking
The scenario

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.

What Nina sends you

"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]"

Without Nina 5+ days waiting. Candidate anxious. Client frustrated. Placement at risk.
With Nina References complete in 1.5 days. Red flag caught early. Informed discussion with client.
H
Hannah Job Matching
The scenario

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.

What Hannah sends you

"6 database matches for Director of Engineering. Top: James Park, placed as Senior Engineer 18 months ago, updated LinkedIn last week. [View Matches]"

Without Hannah $2,400 on job boards. 2 weeks to source. Candidates already in your database.
With Hannah 6 matches found instantly. $0 sourcing cost. Outreach drafted and ready.
A
Ava Candidate Communication
The scenario

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.

What Ava sends you

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.

Without Ava Candidate accepts competing offer. $22K placement fee lost. Client relationship damaged.
With Ava Candidate informed at every stage. Stays engaged. Accepts your client's offer.

How it works

Your AI employees work. You approve.

1

They work inside your ATS

Greenhouse, Lever, LinkedIn, Calendar. No new software.

2

They surface decisions

Review this shortlist? Schedule this panel? Flag this reference? Send this report?

3

You approve on Slack

Full context + buttons. Each decision takes 5-15 seconds.

4

They execute

Shortlist sent, interview booked, report delivered. You close placements.

B
Ben APP 9:22 AM
Panel Interview Confirmed: Director of Engineering
Thursday 2:00 PM EST · 3 interviewers across 2 time zones · Zoom link generated
Scorecards distributed · Candidate briefing pack sent
Looks Good Reschedule
C
Charlie APP 8:00 AM
Client Report: TechCorp (8 active roles)
Pipeline: 142 candidates total · 23 interviewing · 4 offers pending
Alert: Marketing Manager trending 20% over SLA. Rec: adjust salary range.
Send to Client Edit First

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.

A typical Monday

What your AI team does before you finish your standup

7:00a
T
Tyler — 412 weekend applications processed across 14 roles. 35 shortlisted. Top 5 for Senior PM ready.
7:30a
A
Ava — Acknowledgment emails sent to all 412 applicants. Phone screens scheduled with 35 shortlisted.
8:00a
C
Charlie — All 8 client reports generated. 2 flagged: Marketing Manager role trending over SLA.
9:00a
B
Ben — 22 interviews scheduled this week. Panel for Director of Engineering confirmed Thursday 2 PM.
10:00a
H
Hannah — 6 database candidates matched to 2 hard-to-fill roles. Top match: last placed 18 months ago.
3:00p
N
Nina — 2 references complete (satisfactory). 1 flagged: referee rated 2/5 on team collaboration.
5:00p
A
All — 412 CVs screened, 22 interviews booked, 3 references done. Pipeline velocity: on track.

The math

What you are paying now vs. what you could pay

RoleHuman costAI employeeWhat changes
Tyler -- Screening15+ hrs/wk recruiter timeIncludedEvery CV scored in 30 seconds
Ava -- Comms$35-45K/yr coordinatorIncludedEvery candidate updated within the hour
Ben -- Scheduling8+ hrs/wk admin timeIncludedMulti-panel interviews in hours, not days
Hannah -- MatchingSenior recruiter timeIncluded35% more placements from existing database
Charlie -- ReportsNobody (ad-hoc)IncludedAutomated pipeline reports with risk flagging
Nina -- References2-3 hrs/candidateIncluded1.5 days instead of 5, red flag detection
Total$80-130K/yr + lost placementsFraction of the cost28% faster time-to-fill
4xscreening throughput
25%less candidate dropout
35%more database placements
90 daysto positive ROI
Not this

A resume parser

Bullhorn Amplify and HireEZ parse CVs. That is one step. Screening is 15% of the problem.

Not this

Another ATS feature

Greenhouse and Lever have automation. You still configure, trigger, monitor, and intervene. Every day.

This

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

GreenhouseLeverLinkedIn RecruiterIndeedGoogle CalendarZoomSlackEmail

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.