Your CHRO is spending $50,000 a year on an HR team that spends half its time on work a model can do in minutes.
We build AI systems that screen resumes without keyword bias, predict attrition before resignations land, and cut onboarding time-to-productivity in half. For mid-market companies with 50-500 employees who need HR to operate as a strategic function, not an administrative one.
HR teams built for 50 people don't scale to 300. The cracks are already showing.
Resume screening that rejects the candidates you actually need
Your recruiter received 400 applications for that senior developer role and spent 12 hours filtering by keywords copied from the job description. She rejected a candidate who built distributed systems because his resume said 'backend architecture' instead of 'microservices.' Keyword matching is pattern matching against your vocabulary, and it systematically filters out non-traditional candidates, career switchers, and anyone who describes their work differently.
Attrition that hits you three months after you could have prevented it
Last quarter, two senior engineers and a product manager resigned in the same month. Your HR team called it unpredictable. It was not. The signals were there for months: declining engagement scores, a manager whose direct reports keep leaving, compensation below the 40th percentile. Nobody connected the data because it lives in four systems and gets reviewed quarterly in a spreadsheet that is already outdated. Replacing a mid-level employee costs 1.5-2x their annual salary.
Onboarding that takes six months to produce a functional employee
Your new hire's first week: three hours filling out forms that could have been pre-populated, a day reading a wiki last updated in 2023, and two weeks waiting for system access because IT provisioning depends on an email chain that stalls every time. Companies with structured onboarding get new hires productive in half the time. Yours is a checklist in Google Docs the hiring manager forgets to share until day three.
Workforce planning based on headcount, not capability
Your leadership team approved 15 new hires based on headcount requests. Nobody asked whether the skills gap could be closed by reskilling existing employees at a fraction of the cost. Nobody mapped which roles will be partially automated within 18 months. Workforce planning without capability visibility means hiring for today's org chart instead of next year's operating model.
Map the dysfunction. Build the system. Measure the shift.
We don't sell HR software licences. We audit your people operations, identify where AI cuts the most manual work, and build systems that integrate with your existing HRMS, ATS, and communication tools. You own everything we build.
HR Process Audit
Days 1-3
We interview HR leads, hiring managers, and team leads across departments. We map every workflow in your hiring pipeline, onboarding sequence, performance review cycle, and exit process. We pull data from your HRMS, ATS, and any spreadsheets your team is quietly maintaining on the side. We measure time spent per process, error and rework rates, and identify where decisions are being made on gut feel instead of data. The output is a clear picture of where AI saves the most time and money in your HR function.
Deliverable: HR process audit with workflow maps, time and cost analysis, data quality assessment, and prioritised AI opportunity list
Model Design & Data Preparation
Week 1-2
For each AI system we are building, we design the model architecture, define input data requirements, and prepare your historical data. For resume screening, this means a scoring model trained on your successful hires, not keyword lists. For attrition prediction, it means pulling data from your HRMS, engagement surveys, compensation benchmarks, and manager feedback into one place. We clean the data, handle gaps, and validate that the inputs are strong enough to produce reliable outputs. You review and approve the design before we build.
Deliverable: Model architecture documents, data pipeline specifications, integration plan with existing systems, and validation criteria
Build, Train & Test
Week 2-4
We build the AI systems, train models on your historical data, and test extensively against known outcomes. For resume screening, we back-test against your last 12 months of hires to verify the model surfaces candidates your team actually selected while also identifying strong candidates your keyword filters rejected. For attrition prediction, we validate against known departures to confirm the model flags risk accurately. Every system shows its reasoning: why it scored a candidate high, why it flagged an employee as a flight risk. No black boxes.
Deliverable: Trained and tested AI models with accuracy benchmarks, explainability reports, and integration with your existing HR tools
Deploy & Handover
Week 4-6
We deploy to production, run parallel testing with your HR team for one week, and conduct hands-on training for every user. Resume screening runs alongside your existing process for the first two weeks so your recruiters can validate outputs and build trust in the system. Attrition dashboards go live with historical context so your HR leads can see how the model would have flagged past departures. We hand over complete documentation, monitoring dashboards, and a clear escalation path for edge cases.
Deliverable: Production-deployed AI systems, training sessions for HR team, complete documentation, monitoring dashboards, and model performance tracking
HR Process Audit
Days 1-3
We interview HR leads, hiring managers, and team leads across departments. We map every workflow in your hiring pipeline, onboarding sequence, performance review cycle, and exit process. We pull data from your HRMS, ATS, and any spreadsheets your team is quietly maintaining on the side. We measure time spent per process, error and rework rates, and identify where decisions are being made on gut feel instead of data. The output is a clear picture of where AI saves the most time and money in your HR function.
Deliverable: HR process audit with workflow maps, time and cost analysis, data quality assessment, and prioritised AI opportunity list
Model Design & Data Preparation
Week 1-2
For each AI system we are building, we design the model architecture, define input data requirements, and prepare your historical data. For resume screening, this means a scoring model trained on your successful hires, not keyword lists. For attrition prediction, it means pulling data from your HRMS, engagement surveys, compensation benchmarks, and manager feedback into one place. We clean the data, handle gaps, and validate that the inputs are strong enough to produce reliable outputs. You review and approve the design before we build.
Deliverable: Model architecture documents, data pipeline specifications, integration plan with existing systems, and validation criteria
Build, Train & Test
Week 2-4
We build the AI systems, train models on your historical data, and test extensively against known outcomes. For resume screening, we back-test against your last 12 months of hires to verify the model surfaces candidates your team actually selected while also identifying strong candidates your keyword filters rejected. For attrition prediction, we validate against known departures to confirm the model flags risk accurately. Every system shows its reasoning: why it scored a candidate high, why it flagged an employee as a flight risk. No black boxes.
Deliverable: Trained and tested AI models with accuracy benchmarks, explainability reports, and integration with your existing HR tools
Deploy & Handover
Week 4-6
We deploy to production, run parallel testing with your HR team for one week, and conduct hands-on training for every user. Resume screening runs alongside your existing process for the first two weeks so your recruiters can validate outputs and build trust in the system. Attrition dashboards go live with historical context so your HR leads can see how the model would have flagged past departures. We hand over complete documentation, monitoring dashboards, and a clear escalation path for edge cases.
Deliverable: Production-deployed AI systems, training sessions for HR team, complete documentation, monitoring dashboards, and model performance tracking
AI systems that work inside your existing HR stack. Not another dashboard nobody opens.
Audit & Design (Week 1-2)
- HR process audit with end-to-end workflow maps across hiring, onboarding, retention, and workforce planning
- Data quality assessment and preparation plan for AI model training
- Model architecture and integration design for each AI system in scope
- Prioritised roadmap with expected ROI for each AI application
Build & Test (Week 2-4)
- AI-powered resume screening system that scores candidates on capability fit, not keyword matches
- Employee attrition prediction model with risk scores, contributing factors, and recommended interventions
- Automated onboarding workflow engine integrated with your HRMS and IT provisioning systems
- Back-testing and validation reports showing model accuracy against your historical data
Deploy & Handover (Week 4-6)
- Production deployment with parallel testing alongside your existing HR processes
- Hands-on training for HR team, hiring managers, and leadership (recorded)
- Complete documentation: model logic, data inputs, decision explanations, and maintenance guides
- Monitoring dashboards tracking model accuracy, screening efficiency, and attrition risk alerts
We build AI for HR decisions. These are different engagements.
We scope tightly so timelines stay honest and results stay measurable. Each of these is available as a separate engagement.
End-to-end HR workflow automation (payroll, leave, compliance)
If your need is automating administrative HR workflows like payroll processing, leave management, or compliance reporting rather than AI-driven decision support, that is a business automation engagement with a different scope and toolset.
Business AutomationBroader AI strategy across departments beyond HR
If you want to understand where AI creates value across your entire organisation, beyond HR, the diagnostic maps every AI opportunity in your business and tells you which ones to pursue first. HR may be one of several high-impact areas.
AI Strategy & DiagnosticOngoing model retraining, monitoring, and enhancement
AI models need periodic retraining as your workforce data evolves. After handover, ongoing model management, accuracy monitoring, and enhancement is covered under a separate retainer.
AI Operations & Managed SupportIs this right for you?
Right for you if
- You are a mid-market company (50-500 employees, $2M+ revenue) where hiring volume has outgrown your HR team's ability to screen effectively, and you are either rejecting good candidates or wasting interview slots on bad ones.
- You have experienced unexpected attrition in the last 12 months and want a system that identifies flight risk before the resignation letter arrives, not after.
- Your onboarding process takes longer than 30 days to get a new hire productive, and you know the bottleneck is process, not people.
- You have an HRMS or ATS with at least 12 months of historical data that AI models can learn from.
Not right if
- You have fewer than 50 employees or hire fewer than 20 people per year. The ROI on AI-powered screening does not justify the investment at that scale. Start with process automation instead.
- You do not have an HRMS or any structured employee data. AI models need historical data to train on. We can help you set up the data foundation, but that is a different engagement.
- You are looking for a plug-and-play HR software product. We build custom AI systems tailored to your data and processes, not software licences.
What this looks like in practice.
Problem
A 400-person BPO was hiring 60-80 customer service agents per month. Their two-person recruitment team spent 14 hours per batch screening 500+ resumes using keyword filters in their ATS. They were shortlisting based on exact phrase matches, rejecting candidates with relevant experience but different vocabulary. Time-to-fill averaged 28 days.
What we did
Built a resume screening model trained on 18 months of hiring and performance data. The model scored candidates on capability signals (communication patterns, tenure stability, domain exposure) rather than keyword matches. Integrated directly with their existing ATS so recruiters saw ranked candidates with explanations instead of a raw applicant list.
Outcome
Screening time dropped from 14 hours to 45 minutes per batch. Quality of shortlist improved: offer-to-join ratio increased from 55% to 78%. Time-to-fill reduced from 28 days to 16 days. Two recruiters now handle the same volume that previously required four.
Problem
A 180-person SaaS company lost 11 engineers in a single quarter, including three tech leads. Exit interviews cited 'better opportunities' but leadership suspected deeper systemic issues. They had no early warning system and no way to identify which teams or roles were at highest risk.
What we did
Built an attrition prediction model integrating data from their HRMS, quarterly engagement surveys, compensation benchmarks, promotion history, and manager span-of-control metrics. The model generated individual risk scores updated monthly, with contributing factors explained for each flagged employee. Designed an intervention dashboard for HR leads showing risk by team, role, and tenure band.
Outcome
Model identified 85% of subsequent voluntary departures at least 60 days before resignation. HR proactively intervened on 14 high-risk employees in the first quarter post-deployment, retaining 9 of them. Estimated savings of $55,000 in avoided replacement costs in the first six months.
Problem
A 250-person consulting firm was onboarding 8-10 new hires per month. The onboarding process involved 23 manual steps across HR, IT, finance, and the practice lead. Average time-to-productivity was 12 weeks. New hires consistently reported feeling lost during their first month, and 18% left within the first 90 days.
What we did
Mapped and automated the entire onboarding workflow: pre-joining document collection and verification, IT provisioning triggered automatically on offer acceptance, role-specific learning paths generated based on the hire's experience profile, and a 30-60-90 day milestone tracker with automated check-ins. Layered in an AI assistant that answered common new-hire questions using the firm's internal knowledge base.
Outcome
Time-to-productivity reduced from 12 weeks to 6 weeks. 90-day attrition dropped from 18% to 5%. HR time spent on onboarding administration reduced by 70%. New hire satisfaction scores in the first-month survey improved from 3.2 to 4.4 out of 5.