• How many hours per open role does your HR team spend screening CVs that do not meet the basic requirements?

  • Are you finding out about employee churn risk when someone resigns, or before?

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

Growing companies outgrow their HR processes before they outgrow their HR teams. Resume screening takes days when it should take hours. Performance data lives in a system nobody uses because the reports take too long to generate. Employee churn is discovered when someone resigns rather than predicted when the signals first appear. Onboarding is inconsistent because it depends on who is doing it.
We build AI systems for HR teams: resume screening and ranking, employee churn prediction, workforce planning tools, HR chatbots, and performance analytics. Every system is scoped against your data, your HR workflows, and a measurable outcome -- fewer hours on manual work, better retention decisions, or faster hiring cycles.

  • Resume screening AI that ranks candidates against role requirements in minutes, not days

  • Churn prediction models that surface at-risk employees before they start looking elsewhere

  • Workforce planning tools that give HR leaders data for headcount decisions, not gut feel

  • HR chatbots that handle benefits questions, policy lookups, and onboarding guides automatically

RaftLabs builds AI systems for HR teams including resume screening and candidate ranking AI, employee churn prediction models trained on your HR and engagement data, workforce planning and headcount forecasting tools, HR chatbots for benefits questions and policy lookups, performance analytics dashboards, learning recommendation systems, and onboarding automation. HR AI engagements are scoped at a fixed price after a discovery phase that assesses your current HR data, HRIS systems, and the specific hiring, retention, or operational problem being solved.

Vodafone
Aldi
Nike
Microsoft
Heineken
Cisco
Calorgas
Energia Rewards
GE
Bank of America
T-Mobile
Valero
Techstars
East Ventures

HR processes that do not scale are not just an HR problem

When screening takes too long, the best candidates accept other offers. When churn risk is invisible, retention becomes reactive and expensive. When workforce planning is manual, headcount decisions are late and based on incomplete information.

HR AI does not replace your HR team. It removes the manual work that currently prevents your HR team from doing the higher-value work that requires their judgment.

What we build

Resume screening and candidate ranking

AI that reads incoming applications and scores them against defined role criteria. Skills extraction from CVs, scoring against required and preferred qualifications, relevance ranking, and duplicate detection across multiple applications from the same candidate. Output delivered as a ranked list with scoring factors visible so recruiters see why each candidate placed where they did. Integration with your ATS to update application records automatically. Reduces hours of manual screening to minutes. Recruiter time focuses on candidates worth talking to, not on filtering the ones that clearly do not qualify.

Employee churn prediction

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 scores, manager changes, time-since-promotion, and performance trends. Output is a risk score per employee with the top contributing factors surfaced for HR review. HR and manager workflows triggered when risk scores cross defined thresholds: check-in prompts, development conversation cues, or compensation review triggers. The difference between proactive retention and reactive exit interviews.

Workforce planning and forecasting

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 and self-service

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 analytics

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.

Learning recommendation systems

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.

Where is manual HR work costing your team the most time?

Tell us your hiring volume, current attrition rate, and which HR processes are the most manual. We will identify where AI delivers the fastest return.

Talk to our HR AI team

Frequently asked questions

AI resume screening works by applying a scoring model to incoming applications based on criteria defined from the role requirements. The model extracts structured signals from CVs -- skills mentioned, years of experience in relevant areas, education background, previous role titles and company types -- and scores each application against the defined criteria. The output is a ranked list of candidates with the scoring factors surfaced, so the recruiter sees why each candidate ranked where they did. The bias question is important and requires honest treatment. If the training data used to build the model reflects historical hiring patterns that contained bias -- for example, if a company historically hired predominantly from certain universities -- a model trained on that data will reproduce the bias. We address this by: not using historical hiring decisions as training data for candidate scoring, defining scoring criteria explicitly with the hiring team before the model is built, making scoring factors transparent and auditable so recruiters can see and override the logic, and testing scoring distributions across demographic groups before deployment. AI screening should reduce the time spent on screening, not replace recruiter judgment on final candidate selection.

Employee churn prediction models work best with a combination of HR system data and engagement signal data. HR system data includes: tenure, role, level, compensation relative to band, time since last promotion, manager history, and team stability (how many direct manager changes in the last 24 months). Engagement signal data includes: engagement survey scores over time, performance review ratings, participation in development programmes, and absenteeism trends. The model learns which combinations of these signals correlate with employees who left within a defined future window. For the model to be useful, you typically need at least 12-24 months of historical data and a sufficient number of past departures (at least 50-100) to train a reliable signal from. If your data is thin, we design a data collection programme and build the model when sufficient data has accumulated rather than building on insufficient data and producing unreliable predictions.

Workforce planning AI supports headcount decisions by providing data that makes the planning process less dependent on intuition and more grounded in current evidence. A workforce planning tool answers questions like: given current attrition rates and hiring speed, what headcount will we have in each department in six months if we take no action? What roles have the longest time-to-hire and should be opened earliest in the planning cycle? If we lose our top three performers in engineering, what does that do to project delivery capacity? What is the cost per hire by role type and department, and how does it compare across hiring channels? Workforce planning tools connect HR data, finance data, and operational data to provide decision support for HR leaders and department heads in the planning process. They do not make headcount decisions. They make the data available to support the humans making them.

Yes. We build AI layers on top of your existing HRIS rather than replacing it. Most HR AI systems we build connect to HRIS platforms via API (Workday, BambooHR, Personio, HiBob, Greenhouse, Lever, and similar) or via data export for platforms with limited API access. The AI system reads data from the HRIS, applies its models, and either presents output through a separate interface or writes results back to the HRIS (for example, adding a candidate rank score to an ATS application record). We assess your specific HRIS and ATS integrations during scoping and confirm what data is accessible before design begins. If your HRIS data quality is a limiting factor, we address that in the design as well -- AI built on inconsistent HR data will produce inconsistent outputs.