Global travelers moving through an airport

INDUSTRIES → GLOBAL WORKFORCE, STAFFING & MOBILITY

Connect Talent to Opportunity Across Borders.

Skills, qualifications, language, authorization and employer demand — cross-border matching is a normalization problem wrapped in a fairness obligation. DISHA makes matching evidence-based, transparent and fair for workers, agencies and employers.

How do we connect people, skills, employers and mobility pathways across borders while making matching more evidence-based, transparent and fair?

Illustrative industry view — synthetic scenarios and examples until connected to your validated production data.

All industries →

Pain-Point → DISHA Solution Map

Select a pain point: root cause, DISHA intervention, workflow, data inputs and the measurable outcome open beneath.

Root cause

Skills, qualifications, language, location, work authorization, occupation requirements and employer demand are hard to compare

DISHA intervention

Intelligence layer that maps and normalizes requirements

How it works

Normalization → eligibility → match explanation

Data inputs

Occupation requirements, qualification frameworks, demand signals

Measurable outcome

More accurate and efficient cross-border matching

Industry Methodology

Matching spans borders and lives — the methodology normalizes the work and protects the human.

Labour demandOccupation/role requirementsSkill & qualification normalizationTalent discoveryEligibility & readinessMatchFair recruitment workflowMobility/placementOnboardingOutcome

The universal loop — the same in every industry

DiscoverModelDiagnosePrioritizeInterveneMeasureLearn

The universal loop runs continuously — every outcome improves the match model.

Global Talent Mobility Exchange

Pick the occupation and destination market — eligibility, gaps, bridging pathway and fair-recruitment checks re-compute on synthetic data.

Occupation

Destination market

Match explanation — Registered nurse → Destination A

Eligibility (illustrative)

62%

Gaps

Language B2 certificate, local licence conversion

Bridging pathway

8-week language bridge + licence-mapping workshop

Fair-recruitment checks

✓ No fees charged to the worker
✓ Verified employer + contract transparency
✓ Consent gates at every data step

Illustrative scenario — synthetic data. Eligibility logic here is illustrative ONLY and is never legal immigration advice; the human decides with verified documents.

A mobility advisor guiding a candidate through her options

WHY DISHA HERE

Cross-Border Matching That's Evidence-Based, Transparent and Fair

DISHA's value in Global Workforce is trust at scale: normalized skills and qualifications, transparent match explanations, compliant recruitment workflows and fair-recruitment controls — with the worker's consent at the centre.

See Career Mobility

Integration With Your Mobility Stack

DISHA overlays your existing systems — reference architecture, not migration.

HRIS & staffing platformsCredential verification servicesATS & sourcing toolsCompliance & authorization records (governed)Learning providersIdentity & access (consented)

Systems of record / operational systems

APIs, events or data pipelines

DISHA data & knowledge layer

Intelligence/AI

Existing workflow or DISHA UI

Human decision

Outcome feedback

Adoption Options

Integrate only the modules you need — every option runs on the same governed data layer.

Overlay

Read governed data; produce intelligence without changing core systems — typical first use: capability mapping / workforce planning

Embedded

Surface DISHA outputs inside existing applications — typical first use: ATS, HRIS, WFM, project or service workflow

Module-by-module

Activate selected intelligence modules — typical first use: skills, readiness, learning, mobility, analytics

Full intelligence layer

Connect lifecycle, data and AI across the workforce system — typical first use: enterprise workforce transformation

Full platform

DISHA becomes the selected workforce operating layer — strategic transformation

Role-Based Value

Global workforce CEO

Decision: Is our matching engine trustworthy at scale?

Data: Match quality, compliance, fairness signals

DISHA: Normalized matching + governance

Outcome: Match quality with zero compliance breaches

Staffing agency

Decision: Can we place faster without cutting corners?

Data: Candidate evidence, eligibility, demand

DISHA: Discovery + eligibility workflows

Outcome: Placement velocity with evidence

Recruiter

Decision: Why is this candidate a match — really?

Data: Match explanations, evidence, gaps

DISHA: Transparent match explanations

Outcome: Explainable matches accepted by clients

Employer workforce planning

Decision: Can global talent fill our critical roles?

Data: Global supply, eligibility pipelines

DISHA: Demand + normalization

Outcome: Critical roles filled via verified mobility

Mobility / relocation team

Decision: Is this assignment ready — for the person and their family?

Data: Readiness beyond skills: timing, language, support

DISHA: Readiness mapping + workflow

Outcome: Assignment success with human context

Public employment service

Decision: Do pathways serve workers fairly?

Data: Pathway coverage, fairness indicators

DISHA: Pathways + fair-recruitment controls

Outcome: Fair, transparent placement outcomes

Skills / training provider

Decision: Which programs close real eligibility gaps?

Data: Gap-linked program demand

DISHA: Bridging pathways + evidence

Outcome: Program completions that create eligibility

Worker / candidate

Decision: What am I eligible for — and what would make me eligible?

Data: My verified evidence, my gaps, my options

DISHA: Genome + pathways + consent gates

Outcome: My next step, chosen with full information

Tangible Business Outcomes & Measurement

Every outcome is a measured KPI with a baseline, intervention, measurement period, data source, owner and target. Illustrative figures below are placeholders for YOUR data.

KPI	Baseline → Target (illustrative)	Period · Source · Owner
Normalized cross-border match rate	18% → 42% (illustrative)	Rolling quarter · Match engine · Owner: Agency
Verified-credential share of placements	35% → 90% (illustrative)	Per placement · Genome · Owner: Verification
Bridging-pathway completion to eligibility	40% → 68% (illustrative)	Rolling year · Providers · Owner: Programs
Fair-recruitment disclosure coverage	— → 100% (illustrative)	Per placement · Workflow records · Owner: Compliance

Illustrative scenario shown in the product demo — real figures come from your connected, validated data with published measurement definitions.

Travelers with backpacks walking through a bright terminal

THE HUMAN LAYER

Mobility Is a Life Decision

Assignments move families, not just skills. Workforce intelligence keeps readiness honest — capability, language, timing — and the human decides with evidence in hand.

AI & Agent Architecture

Industry intelligence agent

Global skill trends, occupation demand by market

Inputs: Market data, mobility corpora (licensed)

Demand outlooks · Human: strategy approves

Workforce analyst agent

Talent pool analytics with evidence quality

Inputs: Genome, staffing data (governed)

Population insights · Human: analyst validates

Skills & capability agent

Normalized skills with verifiable evidence

Inputs: Credential records, work artifacts (consented)

Capability map · Human: candidate confirms

Readiness agent

Eligibility and readiness for target occupations

Inputs: Capability, qualifications, destination requirements

Eligibility map · Human: advisor reviews

Workflow agent

Coordinates compliant recruitment and mobility workflows

Inputs: Workflow configs, consent gates

Orchestrated steps · Human: approvers act

Executive briefing agent

Decision-ready summaries for agencies and employers

Inputs: Aggregate insights

Briefing pack · Human: leaders decide

Governance agent

Provenance, consent, fair-recruitment and policy constraints

Inputs: Audit logs, policies

Compliance trail · Human: governance sign-off

Governance, Privacy & Responsible AI

Role-based access and least privilege

Tenant/data isolation

Encryption in transit and at rest

Purpose limitation and data minimization

Evidence and provenance on every output

Human oversight for consequential decisions

Correction/appeal mechanisms where relevant

Configurable retention and deletion

Clear distinction between observation, inference, forecast, scenario and recommendation

Worker consent and fair-recruitment disclosures at every step

CONCEPT FILM

Talent Moves With Evidence — Global Workforce & Mobility

90–120s · reserved film slot

0–15s: A candidate's evidence meets a border15–45s: Normalization, eligibility, gaps surface45–80s: Bridging pathways and consent gates80–110s: Transparent match, compliant workflow110–120s: A life moves — fairly

Connect talent to opportunity across borders.

Where This Connects

Canonical pillars — the concepts beneath

Solutions most used in this industry

Research & Resources

External references for context — clearly attributed to their sources; not evidence of findings DISHA has achieved in your organisation.

ILO — Global Estimates on International Migrants in the Labour Force ↗OECD — International Migration Outlook 2025 ↗ILO — Promoting Fair and Ethical Recruitment in a Digital World ↗

Connect Talent to Opportunity Across Borders

Run the industry simulation, explore integration architecture, build a discovery brief — or talk to an expert about a technical workshop and API architecture review.

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