Your HR team is screening 200 CVs manually for every hire and still missing the people who would have stayed.

AI that reads incoming applications and scores them against defined role criteria. Skills extraction from CVs uses named entity recognition to identify technical skills, tools, certifications, and years of experience from unstructured text, extracting "5 years of Python, AWS Lambda, Kubernetes" from a paragraph of self-description in a CV that was never filled into a structured form. Scoring against required and preferred qualifications uses a weighted matching model: mandatory skills missing from the CV produce a hard penalty, preferred skills add to the score with configurable weights per role. Relevance ranking orders the shortlist by composite score. Duplicate detection flags multiple applications from the same candidate across job boards.

Output delivered as a ranked list with scoring factors visible: a recruiter sees "Ranked #3 because: Python, confirmed, AWS, confirmed, Team Lead experience, not found, Kubernetes, partial match (mentioned Docker, no K8s)." Integration with your ATS (Greenhouse, Lever, Workday, SmartRecruiters) updates application status and score fields automatically so the ranked list is in the system your recruiters already use, not a separate tool. Reduces hours of manual screening to minutes, for a role receiving 200 applications, the first 20 worth reviewing are surfaced in under 3 minutes from application close.

Churn prediction models trained on your HR and engagement data that surface at-risk employees before they start looking elsewhere. Model inputs include tenure, compensation history, engagement survey scores, manager changes in the past 12 months, time-since-last-promotion, performance rating trend, peer network size (derived from collaboration data if available), and flight risk signals from HRIS systems that track PTO usage patterns and benefits utilisation changes. Training uses logistic regression or gradient boosting depending on dataset size, with SHAP values applied to explain each individual prediction, so when a high-performer surfaces as high-risk, HR sees "reasons: compensation below market median (+0.43 risk), no promotion in 27 months (+0.38 risk), negative manager feedback change (+0.29 risk)" rather than an unexplained score.

Output is a risk score per employee with the top contributing factors surfaced for HR review, critically, the list is filtered to employees where intervention is likely to make a difference, not all high-risk employees. Low-compensation risk triggers a compensation review workflow. Development-gap risk triggers a manager 1:1 prompt with suggested discussion topics. Engagement risk triggers an anonymous survey or a skip-level meeting. Intervention effectiveness is tracked: for employees who received interventions, retention rates at 6 and 12 months are measured against the risk cohort that did not receive interventions, building the ROI case for the programme.

Data tools for headcount decisions that reduce reliance on intuition and incomplete spreadsheet models. Current headcount by department, role, and level. Attrition forecasts by team based on current risk scores and historical attrition rates. Time-to-hire data by role type for planning when positions need to open. Capacity modelling: what happens to delivery capacity if you lose X people in engineering over the next six months. Cost-per-hire by channel and role type. The data layer for HR leaders and executives to make evidence-based headcount decisions in planning cycles.

HR chatbot that handles the routine questions that currently fill HR inboxes: benefits information, leave policy, expense policy, onboarding checklists, payroll query routing, and HR process guidance. Integrated with your knowledge base, policy documents, and HRIS for personalised responses based on the asking employee's data. Escalation routing to HR team members for questions the chatbot cannot resolve. Available through Slack, Microsoft Teams, or a dedicated HR portal. Reduces HR email volume for common queries and gives employees instant answers to questions that currently wait for an available HR team member.

Performance data analysis that surfaces patterns in your performance review data. Distribution of ratings by team and manager to identify calibration inconsistencies. Correlation between performance scores and retention, promotion rates, and compensation progression. High performer identification and risk scoring: which high performers have the profile signals that correlate with departure risk. Performance trend analysis over review cycles. Manager effectiveness data: teams with high performance, high retention, and engaged employees versus teams where the opposite is true. The analytical layer that makes performance data useful for HR strategy rather than sitting in a system that generates annual reports nobody reads.

Recommendation engines that surface relevant learning content to employees based on their role, skills gap data, and career goals. For companies with existing learning management systems, a recommendation layer that improves content discovery and completion rates. Learning path design tools that map role requirements to skill gaps and suggest development sequences. Completion tracking and manager visibility into team development activity. For companies building internal knowledge bases and training programmes, a custom LMS with AI-powered recommendations built in. The difference between a learning platform employees log into once and one they return to because the content is relevant to them.