Candidate Screening Agent
One auditable candidate-screening pipeline - CV parsing, resume screening, shortlist generation and continuous bias monitoring - built to satisfy the EU AI Act's high-risk obligations, the EEOC four-fifths rule, NYC Local Law 144 and the UK Equality Act by construction, not retrofit.
AI-bias-audited CV screening: Title VII/EEOC Four-Fifths Rule, EU AI Act 2024 Annex III(4)(a) high-risk AI conformity, NYC Local Law 144 AEDT and UK Equality Act 2010 - Article 14 human oversight.
Analyse your processA selection from over 5,000 projects in 25 years of software development
One auditable candidate-screening pipeline across CV parsing, resume screening, shortlist generation and AI bias monitoring
The Agent breaks screening into 13 documented decision steps, each with a defined decider - rules engine, AI agent or human - and per-criterion explainability replacing black-box scoring. Knockout criteria run deterministically through a rules engine (work authorisation, licensure, security clearance). Profile matching runs through AI extraction that gives an individually justified score per requirement, never one composite figure. Bias monitoring runs continuously against the EEOC four-fifths rule with statistical-significance testing. The output is an auditable shortlist that satisfies the EU AI Act's record-keeping, transparency and human-oversight articles by construction, alongside the NYC Local Law 144 bias audit and the UK Equality Act objective-justification standard.
Outcome: For 200 to 800 applications per role across the UK, EU and US, the Agent produces an auditable shortlist instead of a black-box score, with full per-candidate documentation that meets the EU AI Act high-risk deadline (2 August 2026 under current law, provisionally postponed to 2 December 2027 under the Digital Omnibus of 7 May 2026, formal adoption still pending), the NYC Local Law 144 annual independent bias audit, OFCCP Internet Applicant record-keeping, and GDPR Article 22 challengeability with a per-criterion rationale a candidate can dispute. Time-to-shortlist drops from three to six weeks to under one week for a 5,000-employee group, and the auditor finding rate on AI screening falls from a typical 8-15% to under 2%.
The thirteen deterministic screening steps span every applicable regime - and precisely because each one is fixed by statute, regulation or standard, the pipeline is machine-reproducible and audit-defensible:
The EU AI Act makes every CV-filtering system high-risk, with obligations from 2 August 2026 (postponement to Dec 2027 provisionally agreed - Digital Omnibus, May 2026) - and the EEOC four-fifths rule, NYC Local Law 144 and the UK Equality Act already apply. One auditable screening pipeline answers all of them.
International candidate screening does not run on one regulatory standard - it runs on six overlapping regimes at once across the UK, EU and US. Every AI system that filters job applications in the EU is already a high-risk system under Annex III(4)(a) of the EU AI Act; the high-risk obligations attach from 2 August 2026 under current law, provisionally postponed to 2 December 2027 under the Digital Omnibus of 7 May 2026 (formal adoption still pending, as of June 2026). An organisation that cannot show its conformity assessment, declaration of conformity, CE marking, worker-representative information, fundamental-rights impact assessment, human oversight and record-keeping by that deadline has to take the automation offline until it can. The classification is not changing, so the time should be used to put that documentation in place.
A US-headquartered group with 5,000 employees running 200 to 800 applications per role concurrently faces, all at once: US Title VII, ADEA, ADA, the Equal Pay Act and GINA, with the EEOC four-fifths rule; the OFCCP Internet Applicant Rule and affirmative-action obligations; the NYC Local Law 144 bias audit alongside the Illinois, Maryland and California AI hiring laws; the UK Equality Act with the EHRC Code and the Section 60 health-enquiry prohibition; the GDPR’s automated-decision and impact-assessment rules as read by the CJEU SCHUFA judgment; the EU AI Act itself; and the EU Pay Transparency Directive.
One auditable candidate-screening pipeline
This Agent follows the Decision Layer principle: each decision is either rule-based, AI-assisted, or explicitly assigned to a human - with per-criterion explainability replacing black-box scoring.
The obvious challenge is familiar: 200, 400, sometimes 800 applications per role. Recruiters who, after the fiftieth CV, no longer apply the same standards as for the first. Hiring managers who ask after three weeks why the shortlist is not ready.
The real problem runs deeper. Most organisations using AI in screening do not know how their algorithms evaluate. They do not know the weighting. They cannot explain why Candidate A is on the shortlist and Candidate B is not. That is precisely where risk accumulates - and where each jurisdiction now demands documented architecture.
The Mobley v. Workday case makes this tangible. An applicant sues not the prospective employer but the software vendor whose AI screened him out. The US federal court certifies the claim as a class action - alleging systematic discrimination by age, ethnicity, and disability. A University of Washington study shows: in AI-driven CV screenings, names associated with white ethnicity were preferred in 85% of cases. In some occupational groups, Black male applicants were disadvantaged in 100% of test cases. In the US this exposes both vendors and deployers under the Title VII Section 703(k) burden-shifting test; in the UK the equivalent exposure arises under the Equality Act 2010 indirect-discrimination test.
Every screening failure carries direct cost. In the US, an EEOC charge can run through conciliation to litigation and a consent decree with monetary relief, injunctive relief and ongoing monitoring; an OFCCP notice of violation can mean a conciliation agreement or debarment from federal contracts; NYC Local Law 144 carries daily-accruing penalties; and the Illinois, Maryland, California and Colorado AI laws add overlapping state-level liability. In the UK, Equality Act tribunal awards are uncapped, including aggravated and exemplary damages, and the ICO can fine up to GBP 17.5 million or 4% of global turnover. In the EU, AI Act penalties reach EUR 35 million or 7% of worldwide turnover for prohibited practices, on top of GDPR penalties of up to EUR 20 million or 4% and the CJEU SCHUFA line of Article 22 enforcement.
Why cross-jurisdictional screening needs thirteen steps, not eight
A single-jurisdiction screening takes eight to ten steps; a cross-jurisdictional one needs thirteen, because the regimes overlap. The pipeline runs the EU AI Act conformity gate, GDPR lawful-basis validation, document parsing with protected-characteristic redaction, rule-based knockout, per-criterion AI matching with explainability, continuous bias monitoring, adverse-impact escalation, the per-candidate decision record, recruiter shortlist review, documented decisions, downstream synchronisation, EU pay-transparency handling and periodic AEDT bias-audit reporting - end to end.
A concrete cross-border example: a US-headquartered S&P 500 manufacturer with 5,000 employees - 3,200 across 14 US states (including 250 NYC roles), 1,200 in the UK and 600 in the EU - with 280 open requisitions and 65,000 applications a year. That produces 65,000 EU AI Act decision records, 250 NYC Local Law 144 audit submissions, 3,200 EEO-1 entries, 280 OFCCP Internet Applicant logs, 1,200 UK tribunal-defence dossiers, 600 GDPR challenge-mechanism records, and the quarterly bias-monitoring reports and annual conformity-assessment refresh.
In the Decision Layer, seven of the thirteen steps are rule-engine decisions - the conformity gate, lawful-basis validation, knockout criteria, adverse-impact escalation, decision-record generation, pay-transparency handling and periodic audit reporting. Four are AI-augmented: document parsing with redaction, per-criterion matching, bias monitoring and downstream synchronisation. Two require human judgement - recruiter shortlist review with the structured-interview kit, and documented decisions with a protected-characteristic neutrality attestation. Every step carries a timestamp, decider type, rationale and challenge mechanism.
What sets screening apart from audit and compliance
Six dimensions distinguish this Agent from generalised HR audit support. First, CV parsing with provenance tracking and protected-characteristic redaction - name, photo, age, gendered pronouns and alma-mater proxies. Second, rule-based knockout criteria for work authorisation, regulated-profession licensure and security clearance. Third, per-criterion AI matching with individually justified scores, never one composite black-box figure. Fourth, continuous bias monitoring against the EEOC four-fifths rule with statistical-significance testing and Hazelwood standard-deviation analysis. Fifth, the EU AI Act high-risk conformity assessment, CE marking, fundamental-rights impact assessment, human oversight and record-keeping. Sixth, the NYC Local Law 144 annual independent bias audit, with its ten-business-day candidate notice and public posting.
The architecture satisfies the EU AI Act’s record-keeping, transparency and human-oversight requirements by construction, not retrofit. Human oversight is real: no committee can manually review 400 applications and exercise oversight at the same time, but it can review a shortlist with documented per-criterion assessments and trace why one candidate scored 89 on competence fit and another scored 61. Transparency is real: a rejected applicant learns which named requirement the application failed on, not an aggregate score that explains nothing. And the records that enable later evaluation are the Decision Log itself - every step with its timestamp, decider type and rationale, captured as a core function rather than a by-product.
Cross-system integration
The Agent integrates with the full global recruiting stack: Workday Recruiting, SAP SuccessFactors Recruiting, Oracle Recruiting Cloud and ADP Recruiting Management for HCM-embedded ATS. For dedicated applicant tracking it connects to Greenhouse and Greenhouse Inclusion, Lever, iCIMS, SmartRecruiters, Workable, Recruitee, JazzHR, BreezyHR, Bullhorn, Avature, Lattice Recruiting, Personio Recruiting and BambooHR. For AI talent intelligence it connects to Eightfold, Beamery, HiringSolved, Hiretual and Pymetrics; for video interviewing and assessment to HireVue, Modern Hire and Plum; and for executive search and RPO to Korn Ferry, Heidrick & Struggles, Russell Reynolds and the major consultancies. The Candidate Screening Agent is the litmus test for high-risk governance readiness - an organisation that can deploy it in full EU AI Act compliance can deploy any Annex III high-risk agent.
Micro-Decision Table
Who decides in this agent?
13 decision steps, split by decider
Confirm EU AI Act high-risk classification and conformity before screening Is the screening system high-risk under EU AI Act Annex III(4)(a) for recruitment and selection, and is it lawful to deploy? Has the provider completed the Article 16 conformity assessment, issued the EU declaration of conformity and affixed the CE marking, and has the deployer completed the fundamental-rights impact assessment and informed worker representatives? Rules Engine Auditor
A deterministic compliance gate under the EU AI Act: the screening of candidates is high-risk under Annex III(4)(a), so without the Article 16 conformity assessment, the CE marking, the fundamental-rights impact assessment and worker-representative consultation, the system does not meet the high-risk obligations once their deadline applies - 2 August 2026 under current law, provisionally postponed to 2 December 2027 under the Digital Omnibus of 7 May 2026 (formal adoption still pending, as of June 2026).
Decision Record
Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.
Challengeable by: Auditor
Validate the GDPR lawful basis for processing each applicant What is the lawful basis for processing each candidate's personal data? Pre-contractual measures under Article 6(1)(b) for shortlist generation; legitimate interests under Article 6(1)(f), with a documented balancing test, for talent-pipeline development; the Article 9(2)(b) employment exception for any diversity monitoring; and, for any solely automated decision, Article 22 explicit consent or contract necessity with safeguards (human intervention, the right to express a view and to contest). The EU Pay Transparency Directive's ban on pay-history questions also applies. Rules Engine Auditor
Lawful-basis classification is deterministic under GDPR Articles 6, 9 and 22, following the EDPB guidance and the CJEU SCHUFA judgment, which treats credit-score-style profiling as an Article 22 decision. The EU Pay Transparency Directive's pay-history prohibition and the UK ICO guidance on AI also apply, as do the Member State employment derogations under Article 88.
Decision Record
Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.
Challengeable by: Auditor
Parse application documents into structured data with provenance How should structured data be extracted from the CV, cover letter, application form, LinkedIn profile and portfolio? Document parsing combined with AI extraction, with per-field provenance (source document, location, confidence), and mandatory exclusion of GINA-prohibited genetic information and of protected-characteristic markers (age, race and disability proxies, religion, sexual orientation, marital and parental status) under Title VII, the ADA, GINA and the UK Equality Act's nine protected characteristics. AI Agent
AI-driven document parsing with deterministic redaction of protected characteristics. The AI extracts structured data from the varied formats of a CV, cover letter and LinkedIn profile; a deterministic redaction layer then strips name, photo, age, address and alma-mater proxies and gendered pronouns before any matching, to reduce disparate-impact risk under the EEOC four-fifths rule.
Decision Record
Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.
Challengeable by:
Apply rule-based knockout criteria for statutory minimum qualifications Which knockout criteria apply? Work authorisation (US I-9 and E-Verify, UK Right to Work and Skilled Worker visa, or the EU Member State equivalent); regulated-profession licensure (medical, legal, accounting, engineering); statutory minimum age; the required security-clearance level; and any language requirement that is documented as a bona fide occupational requirement under Title VII or the UK Equality Act objective-justification test. Rules Engine
Knockout criteria run deterministically through the rule engine, with no AI judgement. A bona fide occupational qualification under Title VII or the UK Equality Act objective-justification test must be documented and minimally restrictive. Work authorisation is checked against the relevant immigration law, and licensure against the state board, UK regulator or EU professional-qualifications recognition under Directive 2005/36/EC.
Decision Record
Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.
Challengeable by:
Match qualifications to the requirement profile with per-criterion scoring For each requirement-profile criterion (technical skills, years of experience, industry background, education, certifications, language, portfolio), what is the candidate's match score from 0 to 100, with a rationale citing specific CV evidence and the weighting? The AI gives an explainable partial score per criterion, never a single black-box composite, and the weights are validated against bona fide occupational qualifications and reviewed quarterly for disparate-impact risk. AI Agent
AI-driven semantic matching with per-criterion explainability addresses the EU AI Act's transparency and human-oversight requirements, the EEOC validation rules and the UK Equality Act objective-justification test for indirect discrimination. The per-criterion rationale also gives the candidate a basis to challenge the decision under GDPR Article 22 - the rights to human intervention, to express a view, to contest and to an explanation.
Decision Record
Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.
Challengeable by:
Run continuous bias monitoring against the EEOC four-fifths rule Across the candidate pool, what is the selection rate per protected group (race-ethnicity per EEO-1 categories, sex, age 40+, disability, veteran status), and what is the impact ratio against the highest-rate group? Does any group fall below the 0.80 four-fifths threshold, and is the disparity statistically significant on Fisher's Exact, chi-square or a Z-test for two proportions at p<0.05? AI Agent Auditor
Statistical fairness analysis applies the EEOC Uniform Guidelines, the four-fifths rule and the Hazelwood standard-deviation tests. It also feeds the NYC Local Law 144 annual independent audit of selection rate and impact ratio, and the EU AI Act's data-quality and accuracy requirements. Thresholds are configured per jurisdiction.
Decision Record
Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.
Challengeable by: Auditor
Escalate any adverse-impact finding to the compliance team If the four-fifths rule or the significance threshold is breached, what is the escalation? Pause shortlist generation; alert the compliance team, the DPO and the EU AI Act human-oversight controller; and generate an adverse-impact report covering the affected group, the impact ratio and significance, a business-necessity review and a less-discriminatory-alternative analysis under the Title VII Section 703(k) burden-shifting framework. Rules Engine Auditor
Threshold-based alerting under the Title VII disparate-impact framework, the EEOC Uniform Guidelines, the NYC Local Law 144 remediation requirement and the EU AI Act's corrective-action duty. An auto-pause prevents continued use of a biased system pending the business-necessity review and a less-discriminatory-alternative analysis.
Decision Record
Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.
Challengeable by: Auditor
Generate the per-candidate decision record with a challenge mechanism What should each candidate's decision record contain? The candidate and job IDs and timestamp; the filters applied and knockout results; the per-criterion match scores, rationale and AI confidence; the bias-monitoring snapshot; any recruiter override; the final decision (advance, reject or hold); the GDPR Article 13/14 transparency notice and Article 22 challenge mechanism, plus the ten-business-day NYC Local Law 144 notice for NYC roles; and the retention schedule (two years for OFCCP Internet Applicant data, the UK Equality Act six-month claim window, and GDPR storage limitation). Rules Engine
Decision-record generation is deterministic, covering the EU AI Act's record-keeping and transparency articles, the GDPR information and contest rights, OFCCP Internet Applicant record-keeping, the NYC Local Law 144 candidate notice and the EEO-1 reporting feed. The record format is harmonised for cross-jurisdiction audit.
Decision Record
Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.
Challengeable by:
Recruiter reviews the shortlist with a structured interview kit How does the recruiter evaluate each shortlisted candidate against the requirement profile? Using a structured-interview kit with consistent questions across all candidates (meeting the Title VII and UK Equality Act objective-justification standard), a blinded comparator review of each criterion against the cohort distribution, and the authority to override the AI score where it conflicts with the recruiter's assessment, with a documented rationale. Human
A human decision is required under the EU AI Act's human-oversight article and the GDPR prohibition on solely automated decisions, as well as Title VII and the UK Equality Act. The structured-interview method is validated by the Schmidt and Hunter 1998 meta-analysis, which shows structured interviews predict job performance more reliably than unstructured ones (r=0.51 versus r=0.38). The Agent provides structure for the recruiter's judgement, never the verdict.
Decision Record
Challengeable: Yes - via manager, works council, or formal objection process.
Challengeable by:
Document the recruiter's decision with a neutrality attestation What does the recruiter record for the screening decision? The decision (advance, reject or hold); a rationale citing specific job-requirement criteria and interview evidence; an override rationale where it differs from the AI score; an attestation of protected-characteristic neutrality and structured-interview consistency; and, where an adverse-impact alert was raised, the consideration of less-discriminatory alternatives - all persisted in the immutable Decision Log, which the EU AI Act requires deployers to retain for at least six months. Human
Documentation is mandatory under the EU AI Act's record-keeping and deployer-log articles, the EEOC Uniform Guidelines, the OFCCP Internet Applicant Rule and the UK Equality Act evidential burden in tribunal claims. It also underpins GDPR Article 22 challengeability and the Section 703(k) burden-shifting test under Title VII.
Decision Record
Challengeable: Yes - via manager, works council, or formal objection process.
Challengeable by:
Synchronise the final decision with the ATS, EEO/OFCCP reporting and CRM Has the screening decision reached every downstream system? The ATS candidate status (Workday, SAP SuccessFactors, Oracle Recruiting Cloud, Greenhouse, Lever, iCIMS, Personio); the EEO-1, VETS-4212 and OFCCP Internet Applicant record-keeping feed; the NYC Local Law 144 audit-data feed for NYC roles; the candidate-relationship system for talent-pool development, with explicit consent under GDPR Article 6(1)(a); and the rejection notification carrying the Article 13/14 transparency information, the Article 22 challenge mechanism and the NYC ten-business-day notice? AI Agent Auditor
Downstream synchronisation is automated via SCIM, REST APIs and SFTP feeds. The AI surfaces synchronisation failures for human review rather than auto-correcting a decision error, and the integration is tested for cross-jurisdiction record-keeping consistency.
Decision Record
Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.
Challengeable by: Auditor
Handle EU pay-transparency rules and cross-border applicant data For EU and UK applicants, what cross-border compliance applies? The EU Pay Transparency Directive's ban on pay-history questions, with mandatory pay disclosure in the vacancy notice or before interview, gender-neutral job titles and descriptions, and a reversed burden of proof in pay-discrimination claims; plus a cross-border transfer assessment under GDPR Chapter V, relying on the EU-US Data Privacy Framework and the US-UK Data Bridge. Rules Engine
Cross-border compliance is deterministic under the EU Pay Transparency Directive, the UK Equality Act and gender pay gap reporting, and the GDPR Chapter V transfer rules. Transatlantic transfers rely on the EU-US Data Privacy Framework and the US-UK Data Bridge.
Decision Record
Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.
Challengeable by:
Generate the periodic AEDT bias audit and EEO-1, VETS-4212 and OFCCP filings Which periodic compliance reports are generated? The NYC Local Law 144 annual independent AEDT bias audit covering selection rate and impact ratio across sex, race-ethnicity and intersectional EEO-1 categories, with public posting and the ten-business-day candidate notice; the annual EEO-1 Component 1 filing; the annual VETS-4212 protected-veteran filing; the two-year OFCCP Internet Applicant record-keeping; and the EU AI Act record-keeping and deployer logs (six-month minimum). Rules Engine Auditor
Periodic compliance reporting is deterministic, driven by the fixed deadlines of the NYC Local Law 144 annual audit, the EEO-1 and VETS-4212 filings, OFCCP record-keeping retention and the EU AI Act's record-keeping articles. The NYC Local Law 144 audit must be performed by an independent third-party firm under the Department of Consumer and Worker Protection rule.
Decision Record
Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.
Challengeable by: Auditor
Decision Record and Right to Challenge
Every decision this agent makes or prepares is documented in a complete decision record. Affected employees can review, understand, and challenge every individual decision.
Does this agent fit your process?
We analyse your specific HR process and show how this agent fits into your system landscape. 30 minutes, no preparation needed.
Analyse your processGovernance Notes
Assessment
Prerequisites
- Cloud HCM-embedded or dedicated ATS with API access: Workday Recruiting plus Talent Optimization plus Skills Cloud, SAP SuccessFactors Recruiting plus Recruiting Marketing plus Onboarding, Oracle Recruiting Cloud plus Talent Acquisition Cloud, Greenhouse Recruiting plus Greenhouse Inclusion plus Greenhouse Connect, Lever ATS plus LeverTRM, iCIMS Talent Cloud, Personio Recruiting (DACH plus EU), SmartRecruiters, Workable, Recruitee, BambooHR ATS - with full per-applicant record access including application date, source, requirement-profile match, recruiter disposition, plus complete decision history
- AI-talent-intelligence integration: Eightfold AI Talent Intelligence, Beamery, HiringSolved, Hiretual, Pymetrics, HireVue Video Interviewing, Modern Hire (HireVue), Plum - subject to EU AI Act 2024 Annex III(4)(a) high-risk AI compliance plus NYC Local Law 144 AEDT bias-audit requirement plus Illinois HB 645 plus Maryland HB 1202 consent requirements plus EEOC Strategic Enforcement Plan AI scrutiny plus FTC Section 5 oversight
- EU AI Act conformity assessment documentation per Article 16 (Annex VI internal control or Annex VII third-party route), EU declaration of conformity per Article 47, CE marking per Article 48, registration in EU AI database per Article 71, plus deployer fundamental rights impact assessment per Article 27, plus Article 26(7) worker-representative information
- Bias monitoring framework per EEOC Uniform Guidelines on Employee Selection Procedures 29 CFR Part 1607 with Four-Fifths Rule plus standard-deviation analysis per Hazelwood plus statistical-significance testing (Fisher's Exact Test, Chi-Square, Z-test); plus NYC Local Law 144 annual independent third-party AEDT bias audit; plus EU AI Act Article 10 data quality plus Article 15 accuracy
- Decision logging infrastructure per EU AI Act Article 12 record-keeping plus Article 26(6) deployer logs (6 months minimum) plus OFCCP Internet Applicant Rule 41 CFR 60-1.12 recordkeeping (2 years federal contractor) plus UK Equality Act tribunal disclosure plus GDPR Article 22 challengeability plus retention schedule per OFCCP plus EEOC plus EU Member State
- Works council or worker representative consultation per EU AI Act Article 26(7) plus German BetrVG (Works Constitution Act) plus French CSE (Comité Social et Économique) plus Italian Statuto dei Lavoratori plus Netherlands COR plus EU Information and Consultation Directive 2002/14/EC;
- Data Protection Impact Assessment per GDPR Article 35 plus UK GDPR plus ICO Guidance on AI and Data Protection 2023 plus EDPB Guidelines on Article 22 automated decision-making plus CNIL Guidance plus BfDI Guidance plus Garante Privacy Guidance plus AEPD Guidance
- Human-in-the-loop workflow ensuring no Article 22 GDPR solely-automated decisions plus EU AI Act Article 14 human oversight by individuals with necessary competence plus training plus authority to interpret AI output, decide not to use, override, or intervene
- OFCCP Internet Applicant recordkeeping platform plus EEO-1 Component 1 plus VETS-4212 protected-veteran reporting plus NYC Local Law 144 annual audit posting plus state-level pay-data reporting (California SB 1162 plus Colorado plus Illinois plus Massachusetts plus New York plus Washington)
Infrastructure Contribution
What this assessment contains: 9 slides for your leadership team
Personalised with your numbers. Generated in 2 minutes directly in your browser. No upload, no login.
- 1
Title slide - Process name, decision points, automation potential
- 2
Executive summary - FTE freed, cost per transaction before/after, break-even date, cost of waiting
- 3
Current state - Transaction volume, error costs, growth scenario with FTE comparison
- 4
Solution architecture - Human - rules engine - AI agent with specific decision points
- 5
Governance - EU AI Act, works council, audit trail - with traffic light status
- 6
Risk analysis - 5 risks with likelihood, impact and mitigation
- 7
Roadmap - 3-phase plan with concrete calendar dates and Go/No-Go
- 8
Business case - 3-scenario comparison (do nothing/hire/automate) plus 3×3 sensitivity matrix
- 9
Discussion proposal - Concrete next steps with timeline and responsibilities
Includes: 3-scenario comparison
Do nothing vs. new hire vs. automation - with your salary level, your error rate and your growth plan. The one slide your CFO wants to see first.
Show calculation methodology
Hourly rate: Annual salary (your input) × 1.3 employer burden ÷ 1,720 annual work hours
Savings: Transactions × 12 × automation rate × minutes/transaction × hourly rate × economic factor
Quality ROI: Error reduction × transactions × 12 × EUR 260/error (APQC Open Standards Benchmarking)
FTE: Saved hours ÷ 1,720 annual work hours
Break-Even: Benchmark investment ÷ monthly combined savings (efficiency + quality)
New hire: Annual salary × 1.3 + EUR 12,000 recruiting per FTE
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Candidate Screening Agent
Initial assessment for your leadership team
A thorough initial assessment in 2 minutes - with your numbers, your risk profile and industry benchmarks. No vendor logo, no sales pitch.
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Related Pages
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One auditable interview-scheduling pipeline that stays Title VII compliant - pay ranges disclosed where the law requires, a bias audit on every slot proposal under EU AI Act Annex III(4)(a), and ADA accommodation built in - across the UK, EU and US.
Frequently Asked Questions
How does the Agent meet the EU AI Act's high-risk obligations - conformity assessment, CE marking, worker-representative information and the fundamental-rights impact assessment - for candidate-screening AI?
How does the Agent run EEOC four-fifths-rule and disparate-impact analysis under Title VII, the ADA, GINA and the EEOC Uniform Guidelines?
How does the Agent handle OFCCP federal-contractor obligations - the Internet Applicant Rule, affirmative-action programmes and pre-offer self-identification?
How does the Agent comply with the US state AI hiring laws - NYC Local Law 144, the Illinois and Maryland consent rules and California's pay-disclosure rules?
How does the Agent apply the UK Equality Act 2010 - the Section 60 health-enquiry ban, the indirect-discrimination test and the reasonable-adjustment duty?
How does the Agent integrate with Workday Recruiting, SAP SuccessFactors Recruiting, Oracle Recruiting Cloud, Greenhouse, Lever, iCIMS, Eightfold AI, Pymetrics, HireVue, and Personio Recruiting?
What Happens Next?
30 minutes
Initial call
We analyse your process and identify the optimal starting point.
1 week
Discover
Mapping your decision logic. Rule sets documented, Decision Layer designed.
3-4 weeks
Build
Production agent in your infrastructure. Governance, audit trail, cert-ready from day 1.
12-18 months
Self-sufficient
Full access to source code, prompts and rule versions. No vendor lock-in.
Implement This Agent?
We assess your process landscape and show how this agent fits into your infrastructure.