Career OS is a web platform for training providers and institutes of higher learning. It runs one pipeline from application to verified job outcome: AI scores course fit at enrolment, recalculates each trainee’s employability weekly so coordinators can intervene mid-course, matches graduates to employer roles by skill similarity, and generates SSG and WSG reports from an audit trail rather than a spreadsheet.
Explore the case studyA training provider is measured on one number: how many graduates end up employed. Almost none of the work that produces that number is instrumented.
Enrolment screening, in-course progress, CV collection, employer matching and funding submissions each live in a different tool, inbox or workbook. Coordinators spend their week moving information between them — which means the moments where a placement is actually won or lost pass unnoticed.
A programme coordinator has an employer asking for three shortlisted candidates by Monday, a funding submission due at month end, and eleven graduates from the last cohort who have not replied to three emails asking for proof of employment. The CVs on file are four months old. Somewhere in the current cohort, two trainees stopped submitting assignments in week five — but nothing in the system says so.
Screening applicants by hand is slow and inconsistent between staff. The same profile can be waved into one course and questioned in another — and a trainee placed in the wrong programme is a placement problem that has already happened.
Attendance, assessments and CVs sit in separate systems and spreadsheets. Between intake and graduation there is no single view of who is drifting — so coordinators find out a trainee was at risk only once the placement window has closed.
Post-course employment has to be evidenced, so coordinators chase graduates for payslips and CPF statements over email — often for weeks. The replies are then retyped into funding reports, where one wrong cell becomes an audit finding.
The scale makes the gap expensive.
54%
Roughly one in two mid-career trainees is still searching after graduation.
555k
Up from about 520,000 in 2023 — the pipeline keeps growing.
24k
Employer relationships to track, validate and match against.
42
Reporting accuracy is an existential issue, not a clerical one.
Sources: MOE parliamentary reply (Aug 2024), SkillsFuture Year-in-Review 2024 and MOE parliamentary reply (Sep 2025).
Career OS puts enrolment, tracking, matching and reporting on one record per trainee. Coordinators work from a single dashboard; trainees and employers each get their own portal. Behind it sit four AI services — skill extraction, course recommendation, the Placement Probability Engine and document verification — running on Singapore-hosted infrastructure. You can see the walkthrough above.
Career OS parses each applicant’s CV and history, extracts the skills, and scores course suitability against the pathways on offer. Coordinators see a ranked recommendation with the reasoning — and a redirect suggestion when a stronger pathway exists.
The Placement Probability Engine combines skill readiness, learning performance, engagement and live market demand into one weekly employability score. When it falls below threshold, the coordinator is alerted while there is still course left to fix it.
Graduates are matched to partner roles and weekly-pulled Google Jobs listings by skill similarity. Employment proof is collected and verified through conversational follow-up, then flows straight into SSG and WSG reports with a full audit trail.
Designed with trust at the centre
The impact
The figures below were measured on the first deployment of Career OS with a Singapore training provider, across its data science, software engineering and UX programmes. Placement-rate lift is programme-specific and depends on cohort and market conditions; across the pipeline we model a 15–25% improvement, and validate it per cohort rather than claiming it upfront.
Coordinator time
60%fewer hours
of programme admin time returned to coordinators
Measured on the first deployment with a Singapore training provider, alongside a 90% cut in manual job collation and 50% faster trainee onboarding.
Placement operations
3×faster
trainee-to-employer matching against manual shortlisting
Skill-similarity matching over partner roles and weekly Google Jobs enrichment replaced hand-built shortlists and outdated CV folders.
Compliance & audit
20%less effort
spent preparing funding and audit submissions
Verification records are timestamped at the point of collection, so SSG and WSG reports generate from the audit trail instead of being rebuilt in Excel.
Important: Career OS supports coordinator and compliance decisions. It does not automatically admit, reject or withdraw a trainee, and it does not replace a training provider’s own obligations to its funding bodies.
Analytico AI builds AI systems around your real workflows, funding requirements and measurable outcomes — deployed and supported from Singapore.