AI resume screening software that ranks 200 CVs in minutes and flags the people about to walk out the door.

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 programs, 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 program 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.

HR AI projects at RaftLabs typically run from $55,000 to $180,000 depending on scope. A focused resume screening integration with an existing ATS sits at the lower end. A full system combining churn prediction, workforce planning dashboards, and an HR chatbot sits at the upper end. Every project is scoped and priced in writing before development starts. No cost surprises at invoice time.

When a candidate applies, the system calls them or sends a scheduling link, then conducts a structured interview: the same questions in the same order for every candidate, with natural follow-up on vague answers. A post-call pipeline transcribes the call (Deepgram), scores responses against your rubric (GPT-4o), and writes a ranked summary to the ATS, no recruiter has to listen to a recording. Disclosure that the call is AI-conducted is mandatory and delivered naturally at the start. In practice, drop-off from that disclosure is lower than drop-off from scheduling delays: manual screening typically takes 3-5 business days to first contact, AI screening happens within hours of application, and 11pm screens are common because candidates can do it on their own schedule.

Yes. We sign NDAs before any scoping discussions involving sensitive HRIS data, employee records, or proprietary HR processes. HR projects frequently involve personal data protected under GDPR, HIPAA, or equivalent local laws. Our standard engagement includes a data processing agreement that covers how employee data is handled during the build, and we scope compliance requirements in week 1, not as an afterthought.