The evidence behind the score.
How results roll up through your hierarchy, what each official sees, which SDG targets the evidence serves, how the instruments were validated, and what a first cycle asks of you. Reference material for reviewers, procurement teams and statistical offices.
Student-level instrument. State-level evidence. Seven days from cycle close to dashboard.
Results roll up through your administrative hierarchy exactly as it exists today — and every scope gets the same nine views.
Four profilers · one 45-minute session per student.
- Profiler 01Personality
- Profiler 02Career
- Profiler 03Industry
- Profiler 04Learning style
Bottom-up roll-up · 24-hour refresh · your tiers, your codes.
- Student pseudonymised
- Class teacher
- School head teacher
- Block / zone block officer
- District / county district officer
- State / province ministry
- Nation optional
Decision-ready and role-based.
- ScoreFuture Readiness Score · one per scope, with a confidence interval
- Dashboards9 views × 6 scopes
- FlagsTrend · drift · shift · anomaly
- MeasurementCycle N → N+1 — what changed, where
Five roles. Five scopes. Each official sees data framed for their decision.
Class teacherClass
Profiler completion · archetype shape · top-3 careers in class · group composition by learning mode
Pedagogy adjustment · group formation · counsellor referrals
Head teacherSchool
School score vs block average · grade-wise readiness · gender-split heatmap · class-level chase list
Cross-class interventions · counsellor allocation · parent communication priorities
Block officerBlock / zone
Block leaderboard · school-to-school benchmark · industry-cluster heatmap · drift flags
School-level interventions · resource reallocation · cascade priorities
District officerDistrict
District leaderboard · industry-aspiration heatmap · gender equity index · multi-cycle trend
Industry MoUs · district interventions · cross-block resource flows
State or ministryState
State score with confidence interval · district outliers · career-vs-jobs alignment · SDG evidence lines
Policy interventions · budget allocation · inter-state benchmarking
One composite score. Nine views. Multi-cycle pattern detection.
The Future Readiness Score
Four weighted components — three of them read directly on AI-readiness.
- AI resistance 30%
- Industry stability 25%
- Adaptive personality 25%
- Learning agility 20%
Nine views · every scope
Class · school · block · district · state · trust.
Leading indicators for the SDGs your department already reports against.
Every cycle produces census-scale evidence, disaggregated by sex, age and location, that maps onto specific SDG targets — the kind a Voluntary National or Local Review can cite. It does not replace official indicators; it gives you the signal years before they move.
Substantially increase the number of youth with relevant skills — including technical and vocational skills — for employment, decent jobs and entrepreneurship.
District-level demand for trades and technical seats; the skills-gap analysis per cohort; the aspiration–programme match that says which vocational lines to fund.
Career + Industry profilers · Career-vs-jobs view
Eliminate gender disparities and ensure equal access to all levels of education and vocational training for the vulnerable.
A gender equity index per school and block on STEM and vocational aspiration; rural/urban parity on readiness — every cycle.
Gender-split view · all four profilers
Substantially increase the supply of qualified teachers.
Where pedagogy demand and teacher-training delivery are misaligned, block by block — so professional-development slots go where the learners are.
Learning Style profiler · block pedagogy profile
Ensure women’s full participation and equal opportunities for leadership; enhance the use of enabling technology.
Girls’ STEM and technology aspiration tracked per school, every cycle; the outlier schools that achieve parity — and the practices worth replicating.
Gender-split view · Unusual-patterns view
Substantially reduce the proportion of youth not in employment, education or training.
At-risk flags at the secondary transition — five to ten years before a NEET statistic records the outcome. The chase list is the intervention queue for this target.
At-risk view · Chase-list view
Achieve full and productive employment; develop and operationalise a strategy for youth employment.
Aspiration–market fit per district; career-vs-jobs alignment; the evidence base a youth employment strategy is written from.
Career-vs-jobs view · Trend view
Promote inclusive and sustainable industrialisation; enhance research and technological capability.
Sunrise-sector affinity by district; the talent pipeline behind every industrial-policy commitment; district MoU prioritisation.
Industry profiler · sunrise-alignment index
Empower and promote the social and economic inclusion of all, irrespective of sex, origin or other status.
Every view disaggregated by sex and location down to the school; the equity lens is structural, not a special report.
All views · six scopes
Develop effective, accountable and transparent institutions at all levels.
Evidence-based, auditable allocation — which districts got which seats and why; every dashboard action logged.
Leaderboard view · audit log
Increase significantly the availability of high-quality, timely and reliable data disaggregated by sex, age and geographic location.
The data goal itself: twice-yearly, census-scale, disaggregated by sex, age and location to the school — comparable across cycles and districts.
The evidence series
Validated by leading economies. Settled methodology.
Validated psychometric instruments · longitudinal cohort tracking · district-level aggregation · alignment to the national skills strategy. The diagnostic follows the playbook the best school systems already run — and adds census scale and a twice-yearly rhythm.
Education & Career Guidance · since 2014
Ministry-deployed guidance counsellors in every secondary, junior college, polytechnic and technical institute. Self-assessment from upper primary.
Career Education Act 2015 · Free Semester 2016
All 3,186 middle schools, by law. 170+ hours of career exploration per semester; career teachers mandatory.
Transition Year · nationally adopted
A year-long career exploration in upper secondary, referenced as a benchmark by South Korea, Australia and Estonia.
Career Readiness · PISA 2022 validation
690,000 students across 81 economies. The indicators the diagnostic measures are predictive of adult employment outcomes.
How this differs from achievement surveys, household surveys and accreditationReference
Achievement and household surveys measure what students know. Accreditation measures institutional capability. None of them measures who students are — and that is what determines a region’s workforce destiny.
| Future Readiness Diagnostic | National achievement surveyse.g. NAS, NAEP, PISA-style | Household learning surveyse.g. ASER, MICS-style | Ed-tech adoption metrics | Institutional accreditation | |
|---|---|---|---|---|---|
| What | Personality · career · industry · learning style — attitudes and aspirations | Academic achievement (language, maths, science) | Basic reading and arithmetic | Platform adoption and delivery metrics | Institutional quality, faculty, infrastructure |
| Scope | Census — every student in every school in scope | Sample-based | Sample, household-based | Enrolled platform users | Institutions |
| Ages | Secondary (typically 13–18); configurable | Selected grades | Children 5–16 | Higher-education students | Institution-level |
| When | Twice a year | Every three years | Annual | Continuous usage | Periodic accreditation cycles |
| Output | Policy-lever evidence · nine views at every scope · SDG evidence lines | Test scores · district achievement | Literacy and numeracy levels | Adoption metrics · delivery infrastructure | Accreditation grades · rankings |
| Use | Workforce-policy activation across departments | Accountability · outcome benchmarking | Public awareness · basic-skills tracking | Delivery — not an assessment | Quality assurance |
Technical foundation — for reviewersReference
International theory anchors, local exemplars, published reliability. Bilingual at construct, adaptive at item level.
| Personality | Career | Industry | Learning style | |
|---|---|---|---|---|
| Theoretical base | Four-axis type model (E/I · S/N · T/F · J/P) with a wellbeing dimension. Ages 13–18. | Holland RIASEC plus the O*NET occupational taxonomy — 800+ careers. | The 16 O*NET career clusters, localised with regional exemplars. | Four-mode learning preferences plus classroom-design dimensions, mapped to pedagogy inventories. |
| Item bank | 96 items · adaptive routing | 84 items · top-K career inference · skills-gap inference | 64 items · forced-choice, Likert and scenario items | 48 items · situational and scenario-based |
| Language validity | Independent validation per language; review channel for your curriculum body on translated constructs. | Career titles keep the global mapping; local anchors added per region. | Cluster names translated; industry examples drawn from the local economy. | Scenarios use bilingual classroom contexts for ecological validity. |
| Output · student | 32-archetype assignment + wellbeing score | Top-5 careers + fit score + AI-impact rating | 16-cluster strength scores, normalised | Four-mode score + group-vs-solo preference |
| Output · cohort | Archetype distribution + at-risk flag, per scope | Aspiration heatmap + supply-gap detection + AI-exposure share | Industry heatmap + technical-seat alignment + sunrise-alignment index | Block-level pedagogy demand profile |
| Validation | Pilot n = 3,200+ across three states · Cronbach α 0.78–0.84 | Pilot n = 4,100+ · construct validity via diagnostic classification models | Pilot n = 2,800+ · test–retest reliability 0.81 (four-week interval) | Pilot n = 2,200+ · item-response calibrated · Cronbach α 0.76 |
Frameworks · OECD Career Readiness · national holistic-assessment policies · item-response scoring with bootstrap confidence intervals · no personal identifiers at the instrument layer, by design.
A statewide baseline in 12 weeks. Then a rhythm you own.
Setup & pilot
MoU signed, roster synced, app deployed. One district, around 50 schools, the first 5,000 students.
District
Full pilot-district rollout. Block officers brief their schools; class teachers run sessions inside the timetable.
Multi-district
Five representative districts — rural and urban, with the gender spread of the state.
Baseline report
Statewide baseline and score, district action plans, SDG evidence lines, cabinet brief — in time for the budget cycle.
- 30-day window
- 5–7 days QA
- Dashboards at every scope
- Briefings cascade
- Interventions designed
- Next cycle measures the change
- Annual evidence series
Three things start a first cycle.
Everything else — devices, training, processing, dashboards, the baseline report — is covered by the programme. A first cycle is analytics only; coaching and placement are proposed after the baseline is validated.
Department sign-off
An MoU covering data sharing, privacy and pilot-district selection.
Pilot district nomination
One district, representative across rural/urban and gender ratios.
Read-only roster access
Your existing school and student rosters, so we sync on day one — no new identity records.
- CSR or philanthropic partners — zero cost to the exchequer; the model behind our first statewide baseline.
- Development-partner programmes — skills, employability and SDG-localisation funding lines.
- Direct procurement or tender — per-student pricing that scales with census size.
- Language-first — student picks at login; every language validated first
- Roster-native — read-only sync, pseudonymised, no paper
- Action lists — at-risk rankings and chase lists per block
- Audit-grade access — scope-enforced, every action logged
- Built for scale — 5M+ students, 100K+ schools, offline-capable
- Your data, your jurisdiction — in-region hosting, full export any time
What departments ask before a first cycle.
Does this work outside India?
Yes. The diagnostic is built for any administrative hierarchy — nation, state or province, district or county, block or zone, school, class. The instruments rest on international frameworks (the O*NET occupational taxonomy, the OECD Career Readiness indicators) with local anchors added per region, and the roll-up reuses your existing school and district codes.
Which languages are supported?
The student picks a language at login. Every language is independently validated before a cycle runs — English and Hindi are live today, and new languages are validated during setup with a review channel for your curriculum body on translated constructs.
How does this help us report on the SDGs?
Each cycle produces census-scale evidence disaggregated by sex, age and location down to the school, mapped to specific targets under Goals 4, 5, 8, 9, 10, 16 and 17. It does not replace official SDG indicators; it gives your Voluntary National or Local Review a leading, district-level evidence line — and gives the department years of warning before the official indicator moves.
What does “AI-ready” actually mean here?
Three measured things. Whether a cohort’s aspirations point at occupations with high AI exposure (every aspired career carries an AI-impact rating). Whether students show the capacities automation augments rather than replaces — adaptability, learning agility, interpersonal and hands-on orientation. And whether their industry affinity matches the sunrise sectors your region is bidding for. The quadrant view puts all three on one chart per district.
What data do you need from us?
Read-only access to your existing school and student rosters, so the diagnostic maps to your structure on day one. Students are pseudonymised at the instrument layer; no new identity records are created and no personal identifiers are held against responses. The evidence series belongs to the department, with full export at any time and in-country or in-region hosting aligned with your data-protection law.
Is this coaching or counselling?
No. A first cycle is analytics only: evidence and targeted action lists for the officials who own each lever. Coaching, predictive analytics and placement are proposed only once the baseline is validated.
Can we start with one district?
Yes. The standard path is a 12-week first baseline: one district in weeks 1–2, then five representative districts, then a statewide report. Many departments begin with a single district and scale after the first dashboards.
How is it funded?
Three paths: CSR or philanthropic partners at zero cost to the exchequer, development-partner programmes, or direct procurement. Per-student pricing scales with census size, and tender-ready documentation is available on request.
See your region’s students the way a workforce planner needs to.
A 45-minute briefing with the education team. We open a live diagnostic dashboard, walk through the 12-week baseline plan, and outline what a first cycle in your region would look like.