Use AI to match resumes to roles, predict retention risk, and reduce time-to-fill by 50%. Intelligent workforce matching that finds the right candidates faster and reduces turnover.
Organizations struggling with hiring bottlenecks, high turnover, or difficulty finding qualified candidates. Ideal for companies with high-volume hiring, specialized skill requirements, or retention challenges where better candidate matching can significantly impact business outcomes.
50% faster
AI-powered matching identifies qualified candidates faster, reducing time from posting to offer
30-40% better
Predictive retention models identify candidates likely to stay, reducing turnover and hiring costs
40-60% lower
Faster hiring, better matches, and reduced turnover lower overall recruitment costs
25-35% improvement
Data-driven matching improves fit between candidates and roles, reducing bad hires
Intelligent matching system that analyzes resumes and job descriptions to identify best-fit candidates. Uses semantic analysis to match skills, experience, and qualifications beyond keyword matching
AI models that predict candidate retention risk based on historical data, candidate attributes, and role characteristics. Identifies candidates likely to stay long-term and flags high-risk hires
Identifies skill gaps in candidate pools and internal talent. Suggests internal mobility opportunities and training needs. Matches internal candidates to open roles
Automated candidate scoring and ranking based on role fit, retention probability, and hiring manager preferences. Prioritizes top candidates for review, reducing recruiter workload
Real-time dashboards tracking time-to-fill, cost-per-hire, retention rates, and matching accuracy. Insights to optimize recruitment strategies and improve hiring outcomes
We analyze your historical hiring data, successful hires, and retention patterns. We train AI models on what makes a good match for your organization. We configure matching algorithms based on your role requirements and company culture.
AI analyzes incoming resumes and matches them to open roles based on skills, experience, and fit. We predict retention risk for each candidate. We rank candidates and prioritize top matches for recruiter review. We identify skill gaps and internal mobility opportunities.
We track hiring outcomes and retention to continuously improve matching accuracy. We refine models based on feedback from hiring managers and recruiters. We provide analytics and insights to optimize recruitment strategies. We expand to additional roles and departments.
6-10 weeks
From data analysis through model training, deployment, and initial optimization
2-3 hours/week
HR team interviews, historical data review, role requirement definition, and system training
Timeline factors:
Per-placement fee or SaaS license
Flexible pricing models: per-placement success fees (typically 10-15% of first-year salary) or monthly SaaS license ($500-$2K/month based on volume). Setup fee for initial implementation and training ($10K-$25K).
Our recruitment and workforce matching solutions directly impact talent acquisition and retention metrics.
Time-to-fill (days)
Cost-per-hire ($)
Offer acceptance rate (%)
New hire retention (% after 90 days)
Voluntary turnover rate (%)
Retention rate by segment (%)
Candidate quality score
Interview-to-offer ratio
Onboarding satisfaction (0-100)
Employee engagement score
Skill gap closure rate (%)
Internal promotion rate (%)
We use modern AI platforms and integrate with your existing HR and recruitment systems. Our approach is flexible—we select the best-fit solution for your environment.
Risk: AI models may perpetuate or amplify hiring biases, leading to discrimination and legal issues
Safeguard: We implement bias detection and mitigation techniques, audit models for fairness, and ensure compliance with EEOC and other employment regulations. We use diverse training data and remove protected attributes from matching algorithms. We provide transparency in how candidates are scored and allow human override. We regularly audit outcomes for demographic parity.
Risk: AI incorrectly matches candidates to roles, leading to poor hires or missed qualified candidates
Safeguard: We train models on your historical hiring data and successful hires. We validate matching accuracy and continuously improve based on outcomes. We provide confidence scores and allow human review of AI recommendations. We track matching accuracy and refine models based on hiring manager feedback. We also maintain human-in-the-loop review for final hiring decisions.
Risk: Candidate data privacy violations or security breaches compromise sensitive information
Safeguard: We implement data encryption, access controls, and secure storage. We comply with data privacy regulations (GDPR, CCPA) and ensure candidate consent for data processing. We provide audit trails and access logging. We can deploy on-premises or in secure cloud environments. We also ensure data retention policies and secure data deletion.
Challenge: Fast-growing tech startup needed to hire 100+ engineers in 6 months. Recruiters were overwhelmed with 500+ resumes per role, time-to-fill was 60+ days, and 40% of new hires left within 6 months. High cost-per-hire ($25K+) and low offer acceptance rates (30%).
Solution: Deployed AI-powered resume matching system that analyzed resumes and matched to roles. Implemented retention risk prediction to identify candidates likely to stay. Automated candidate ranking and prioritization. Integrated with Greenhouse ATS for seamless workflow.
Impact: Reduced time-to-fill by 55% (from 60 to 27 days). Improved offer acceptance rate from 30% to 48% through better candidate matching. Reduced turnover from 40% to 25% in first 6 months through retention prediction. Lowered cost-per-hire by 45% ($25K to $14K). Enabled hiring of 120 engineers in 6 months. ROI: $1.2M savings from reduced hiring costs and turnover.
Challenge: Manufacturing company with 500 employees struggled with high turnover (35% annually) in skilled positions. Difficulty finding candidates with specific technical skills. Time-to-fill for skilled roles was 90+ days, impacting production capacity. High recruitment costs and training investments lost to turnover.
Solution: Implemented AI matching system for skilled positions with retention risk prediction. Analyzed historical data to identify attributes of long-term employees. Matched candidates based on skills, experience, and retention probability. Identified internal mobility opportunities for existing employees.
Impact: Reduced turnover from 35% to 22% through better candidate matching and retention prediction. Reduced time-to-fill by 50% (from 90 to 45 days). Improved internal mobility—15% of open positions filled internally. Reduced recruitment costs by 40% and training costs by 30% through lower turnover. ROI: $800K annual savings from reduced turnover and recruitment costs.
No, it's much more sophisticated than keyword matching. We use natural language processing and machine learning to understand the meaning and context of resumes and job descriptions. The AI analyzes skills, experience, qualifications, and even soft skills to find semantic matches. For example, it understands that "project management" and "program coordination" are similar skills. It also learns from your historical hiring data to understand what makes a successful hire for your organization.
Our retention prediction models achieve 75-85% accuracy in identifying candidates likely to stay vs. leave. We analyze historical data including candidate attributes, role characteristics, and retention patterns to build predictive models. While not perfect, it significantly improves hiring outcomes—clients typically see 30-40% reduction in turnover when using retention prediction. The models identify risk factors (job hopping, skill mismatches, etc.) and flag high-risk candidates for additional screening.
No—AI augments recruiters, not replaces them. AI handles the time-consuming task of screening and ranking resumes, allowing recruiters to focus on relationship building, interviewing, and closing candidates. Recruiters still make final hiring decisions and manage the candidate experience. Most clients see recruiters become more effective and satisfied as they work with pre-qualified, high-quality candidates rather than sifting through hundreds of unqualified resumes.
We implement comprehensive bias detection and mitigation. We remove protected attributes (age, gender, race) from matching algorithms. We audit models for demographic parity and fairness. We use diverse training data and test for disparate impact. We provide transparency in how candidates are scored. We allow human override of AI recommendations. We regularly audit outcomes to ensure fair hiring practices. We also comply with EEOC and other employment regulations.
Yes, we integrate with all major ATS platforms (Workday, Greenhouse, Lever, Bullhorn, etc.) and can work with custom systems via APIs. The AI matching works within your existing recruitment workflow—it analyzes resumes as they come in, ranks candidates, and provides recommendations directly in your ATS. Recruiters see AI scores and recommendations alongside candidate information, making it seamless to use.
We need historical hiring data including resumes, job descriptions, hiring outcomes (hired/not hired), and retention data (how long employees stayed). The more data, the better the models, but we can start with as little as 50-100 past hires. We also need current job descriptions and role requirements. We handle data privacy and security requirements, ensuring candidate data is protected.
Typical ROI is 3-5x within the first year. For example, if you hire 50 people per year with average cost-per-hire of $20K, a 40% reduction saves $400K annually. Reduced turnover (30-40% improvement) saves additional costs from rehiring and training. Faster time-to-fill enables business growth. Most clients see payback within 3-6 months from reduced recruitment costs and improved retention.
Let's discuss your recruitment challenges and explore how AI-powered matching can help you find the right candidates faster.
Support new hire onboarding and engagement to improve retention. Perfect complement to recruitment for complete talent management.
Address skill gaps through training and upskilling programs. Ideal follow-on when AI identifies skill gaps in candidate pools.
Last updated: November 2025