Three agencies. One shared mission — economic growth, decent work, and skills for Pakistan's 240 million people. Maahir — meaning "skilled one" — is Pakistan's AI-enabled human development platform that could turn economic-growth lending, skills-development projects, and Decent Work programmes into measurable jobs, formalized incomes, and proof of capability. Not coding bootcamps. AI skills applied to the garment floor, the farm, the cooperative, the home-based workshop, and the informal electrician's shop — the real economy these three agencies fund — in 9 languages, on any phone.
USAID Pakistan fuels economic growth, agriculture, and private-sector partnerships. ADB finances skills development, inclusive growth, and TVET strengthening. ILO advances Decent Work, child labor elimination, and youth employment. All three confront the same wall: 72% of Pakistan's workforce is informal, millions of youth are jobless, skills go unproven, and training rarely reaches the farm, the garment floor, or the home-based workshop where most Pakistaners actually work. Maahir takes a different path. We teach AI as a tool that a garment quality checker in Faisalabad, an informal electrician in Lahore, a cotton extension worker in Bahawalpur, a cooperative manager in Sindh, a bangle-maker in Hyderabad, or a home-based stitcher in Karachi could apply to their own work tomorrow — and then prove that skill through a verifiable portfolio. The three agencies fund economic growth, skills, and decent work; Maahir could be the delivery, tracking, and proof layer that turns those investments into jobs, formalized incomes, and trusted credentials — in everyone's language, on every phone.
USAID, ADB, and ILO each run flagship programmes in economic growth, skills, and employment across Pakistan. Here is how each connects to Maahir's delivery capability — and where AI could amplify every dollar.
USAID's flagship EGA portfolio strengthens agri-business value chains, trade, and enterprise. Maahir could deliver applied AI skills to farmers, processors, and SMEs along those chains — advisory, pricing, market access.
USAID invests in education access and quality. Maahir's 9-language AI tutor Roshni could supplement classrooms and reach out-of-school youth phone-based — directly serving USAID learning-outcome targets.
The Pakistan Reading Project built literacy capacity in millions of children. Maahir could extend this with voice-based AI tutoring for pre-literate and early-reader children in Urdu, Sindhi, Pashto, and more.
Youth-development programmes target employability and entrepreneurship. Maahir's skills-to-jobs pipeline — exercises → portfolio → Talent Portal — could give USAID youth cohorts verifiable proof of capability.
USAID brokers private-sector partnerships for jobs and growth. Maahir's Talent Portal + recruiter matching could connect trained beneficiaries directly to USAID partner companies.
USAID engages the Pakistani diaspora for investment and knowledge transfer. Maahir could offer diaspora-funded sponsor-a-batch models and diaspora-mentor matching for local learners.
ADB's Skills Development portfolio finances vocational training reform. Maahir could be the digital delivery rail — replacing or augmenting classroom TVET with phone-based, multi-language instruction.
ADB aligns lending to Vision 2025 pillars — human capital, knowledge economy, private-sector growth. Maahir directly serves the human-capital and knowledge-economy pillars.
ADB prioritizes inclusive growth — women, rural, low-income. Maahir's home-based, 2G-compatible, female-friendly model reaches the populations classroom TVET cannot.
ADB strengthens TEVTA, PVTC, SSD and NAVTTC systems. Maahir could turn those institutes into batch facilitators — digital delivery + monitoring tools, not brick-and-mortar expansion.
ADB's Knowledge Sector initiative funds research and evidence for policy. Maahir's real-time learner data — who learns what, where, outcomes — could feed that evidence base directly.
ADB lending is results-based. Maahir's Command Center produces enrollment, completion, cost-per-beneficiary, and placement data — auditable to ADB M&E frameworks.
The Decent Work Country Programme (DWCP) is ILO's framework for Pakistan. Maahir's workplace safety, labor rights, and fair-pricing modules could deliver DWCP content at scale, phone-based.
ILO combats child labor across sectors. Maahir's Kids Mode — safe, guardian-controlled learning — could offer children removed from labor a path back to education, on a shared family phone.
ILO's STED methodology forecasts skills needed for trade competitiveness. Maahir could be the delivery channel for STED-identified skills — garment quality, export compliance, textile technology.
ILO's youth-employment focus targets NEET youth (not in education, employment, or training). Maahir's portfolio-first approach could give youth the proof employers demand for that first job.
ILO drives transition from informal to formal economy. Maahir's verifiable skill portfolio could be the credential that lets an informal electrician or home-based worker access a bank loan, a contract, or formal registration.
ILO advances women's empowerment and home-based-worker rights. Maahir's 8 Women Empowerment modules + Saheli toolkit could build home-based income skills with safety monitoring throughout.
Billions committed to growth, skills, and decent work — yet Pakistan's employment gap keeps widening. Here is the reality every USAID, ADB, and ILO project confronts on the ground.
Curricula are disconnected from the real economy. Graduates know theory but cannot use AI, digital tools, or modern workplace systems. Employers report 60%+ of hires need retraining. Programmes produce certificates, not employability.
72% of Pakistan's workforce is informal — electricians, tailors, mechanics, home-based workers, farm labourers. They have skills but no credentials, no bank access, no contracts. Skills programmes rarely reach them, and when they do, the work stays invisible.
4 million youth enter the labour market yearly; most find no work. Degrees without marketable skills, and skills without proof, leave employment indicators flat. USAID youth, ADB TVET, and ILO youth-employment programmes all hit this wall.
An estimated 3.3 million+ children are in child labor in Pakistan — in workshops, brick kilns, agriculture, and home-based production. ILO elimination programmes struggle to offer children a viable path out of work and into learning.
Paper certificates are forged, inflated, or meaningless. Employers cannot verify what a "trained" beneficiary can actually do. Without proof, skills don't convert to jobs — and the "no employer trust" gap swallows every training investment.
Most programmes stop at completion. Did the beneficiary get a job? Earn more? Stay employed? — no one knows. Without post-training tracking, agencies cannot prove employment outcomes or salary uplift to their boards and donors.
With female labour force participation near 8-21% — among the lowest globally — half of Pakistan's human capital sits idle. Cultural norms, mobility barriers, safety, and lack of female-friendly training keep women economically invisible.
Pakistan's export sectors — garments, textiles, leather, sports goods, surgical instruments — lose orders because workers can't meet US/EU quality, compliance, and social standards. STED and trade programmes aim to close this, but training rarely reaches the factory floor.
Every challenge above could map to a specific Maahir feature — and could produce the evidence USAID, ADB, and ILO M&E frameworks demand.
Maahir's applied-AI tracks could align directly to the economy these agencies fund — agriculture, garments/textile, construction, retail, energy, informal trades. A garment worker learns export-quality compliance; an electrician learns to formalize; a cooperative manager learns member accounting. The curriculum is the work itself. Graduates could leave with skills employers need tomorrow, not theory from a decade ago.
An informal electrician, mechanic, or home-based worker could build a verifiable Maahir portfolio — real projects, assessments, a public profile with a verification code. That portfolio could serve as the credential a bank, a microfinance institution, or a registration body needs to move that worker from invisible to bankable. ILO's formalization agenda meets ADB's financial-inclusion lending meets USAID's private-sector growth — one portfolio, one worker, one phone.
Learners could flow through a structured pipeline — exercises → projects → portfolio → Talent Portal → recruiter matching → placement tracking. Graduates could get public profiles visible to employers; recruiters could match on skills, location, and language. Every placement could be logged, so agencies see actual employment outcomes, not attendance counts.
Children removed from labor need a path back to learning — but many cannot attend school, or need bridging first. Maahir's Kids Mode — guardian-controlled, filtered, age-appropriate, voice-based for pre-literates — could offer that bridge on a shared family phone. Six child-friendly modules build digital literacy and curiosity. Safety monitoring throughout flags any distress. Directly serves ILO child-labor elimination goals.
Every completed module could earn a certificate with a unique verification code — checkable at a public URL, no forgery possible. Beyond certificates, learners could accumulate real project portfolios — advisories, cost estimates, business plans, quality reports — reviewed by AI + facilitators. An employer could see not a paper claim but actual work. (See the dedicated Proof of Skills section below.)
The Command Center could give agencies a real-time funnel per batch: registered → approved → active → modules completed → at-risk → certified → placed. The auto-nudge engine could follow up with graduates at 1, 3, 6, and 12 months post-completion to capture employment and income data. Per-beneficiary cost calculated automatically. One-click export of donor-ready reports.
Women who cannot leave home could still learn — on a family phone, in their language, with no male instructor. The 8 Women Empowerment modules (Stitching, Home Food, Freelancing, Beauty, Reselling, Teaching, Micro Business, Content Creation) plus the Saheli Toolkit (146 business resources in Urdu) could build real income skills from home. Female facilitators could monitor batches; safety monitoring with distress detection could route any woman at risk to a trained female counselor.
Garment, textile, leather, and surgical-instrument workers could learn to use AI for quality compliance checking against US/EU standards — photograph a finished good, AI flags deviations; check labeling against EU regulations; verify social-compliance requirements (no child labor, fair hours). Pakistan's export sectors could gain the AI-fluent, compliance-ready workforce STED and trade-competitiveness programmes aim to build.
Illustrative use cases showing how Maahir's existing platform features could be applied to USAID, ADB, and ILO programmes. Not coding bootcamps. These are scenarios where a non-IT worker could use AI to do their existing job better, formalize their work, or access a new market — exactly the economic-growth, skills, and decent-work outcomes these agencies are designed to produce.
POSSIBLE SCENARIO: Nasreen, a quality checker at a Faisalabad garment export house shipping to US and EU buyers, could join an ILO STED / ADB skills batch. Through Maahir, she could learn to use AI for quality compliance checking — photograph a finished shirt, and AI could flag a misaligned collar seam, a missing care label, or a dye-lot mismatch against the buyer's spec sheet. She could use AI to verify labeling against EU REACH chemical regulations and US CPSIA standards, and to generate corrective-action reports in English for the buyer. Her factory's rejection rate could drop; her buyer could trust her line; she could become a compliance supervisor. Export readiness, built one worker at a time — the exact outcome STED targets.
POSSIBLE SCENARIO: Imran has been an informal electrician in Lahore for 12 years — skilled, but with no certificate, no bank account access, and no way to prove his capability. Through an ILO formalization / USAID private-sector batch, he could build a Maahir portfolio: completing wiring-safety exercises, uploading photos of his past installations as projects, and passing an AI-reviewed assessment. His public Maahir profile — with verification code — could serve as the credential a microfinance bank needs. He could approach a bank, show his portfolio, and get a ₨200,000 loan to buy proper tools and register his business. From invisible to bankable — the formalization outcome ILO and ADB financial-inclusion lending both seek.
POSSIBLE SCENARIO: Bilal, 22, a matric pass in Faisalabad's textile hub, could join an ADB Skills Development / USAID youth batch. He could learn to use AI for production scheduling — enter an order of 3,000 pieces with a 4-week deadline, machine capacity, and labour shifts, and AI could generate a day-by-day cutting, stitching, and QC plan that balances load and meets the deadline. He could use AI for inventory prediction (when will yarn run out?) and cost estimation (materials + labor + overhead per unit). His SME factory owner could see a smarter scheduler who saves waste and hits deadlines. Bilal moves from loader to production planner — without a university degree.
POSSIBLE SCENARIO: Saima is an agricultural extension worker covering 500 smallholder farmers in Sindh, under a USAID EGA programme. Through Maahir, she could learn to use AI as a force-multiplier. She could ask Roshni (by voice, in Sindhi) for a pest-risk advisory based on the week's weather, then deliver it to all 500 farmers via WhatsApp in one broadcast — translated into each farmer's reading level. She could photograph a farmer's diseased crop, get an instant AI identification and treatment suggestion, and relay it. One extension worker, 500 farmers reached, AI doing the research she could never do alone. Exactly the scale USAID EGA aims for.
POSSIBLE SCENARIO: Hamza, a self-taught developer in Peshawar, could join a USAID youth-development / ADB skills batch focused on the digital economy. Through Maahir, he could complete the Freelancing track, build a 5-project portfolio (real client-style briefs, AI-reviewed), and publish his Talent Portal profile. He could learn to use AI for client proposal writing (enter a job post → AI drafts a tailored proposal in professional English), fair pricing (AI benchmarks rates for his skill level and region), and contract management. The Talent Portal could match him to an international client seeking his exact stack. His first $500 international payment — remittance-worthy work, built from Peshawar, on a phone.
POSSIBLE SCENARIO: Abdul returned from Saudi Arabia after 8 years as a construction worker — experienced, but his Gulf skills don't map cleanly to Pakistan's job market, and he has no local credential. Through an ILO returnee-reintegration / ADB skills batch, he could use Maahir to re-skill: complete a construction-management or facilities-AI track, translate his Gulf experience into a structured portfolio, and get a verifiable certificate that Pakistani employers trust. The Talent Portal could match him to a CPEC-linked contractor or a facilities firm. From returnee-at-risk to employed local worker — the reintegration outcome ILO labour-mobility programmes target.
POSSIBLE SCENARIO: Fatima manages a 120-member women's dairy cooperative in Punjab, supported by a USAID private-sector / ADB inclusive-growth programme. Through Maahir, she could learn to use AI for member tracking (AI-structured member database with production per household), contribution calculation (enter each member's daily milk volume → AI calculates shares, deductions, and monthly totals), and dividend distribution (AI generates a transparent, auditable dividend sheet per member, in Urdu). She could use AI to draft meeting agendas, minutes, and compliance reports for the registrar. Cooperative governance, built on a phone — the inclusive-growth and private-sector partnership both USAID and ADB fund.
POSSIBLE SCENARIO: Rubina makes lac bangles at home in Hyderabad's cottage industry — skilled, but dependent on a middleman who sets the price. Through an ILO home-based-worker / USAID EGA batch, she could learn to use AI for market research (AI summarizes where similar bangles sell online, at what price, and which designs trend), product pricing (enter her materials, time, and design complexity → AI suggests a fair retail price with a profit margin), and customer outreach (AI drafts WhatsApp and Instagram product descriptions in Urdu and English). She could bypass the middleman, sell directly, and earn 3x per set. Cottage-industry digitization, from the home workshop — exactly the home-based-worker empowerment ILO champions.
POSSIBLE SCENARIO: Waleed, 19, lives in a merged tribal district (ex-FATA) where formal jobs are scarce. Through a USAID youth / ADB inclusive-growth batch delivered in Pashto, he could complete Maahir's Micro Business and Freelancing tracks. He could use AI to generate a business idea based on his region's needs, draft a simple business plan, set up a WhatsApp-based storefront for local delivery of goods, and use AI for customer messaging and basic bookkeeping. Within weeks, he could be running a small online business — no office, no capital, no migration needed. Youth employment in the hardest-to-reach places, on a shared phone.
POSSIBLE SCENARIO: Shazia, a home-based worker in Karachi, does stitching, cooking, and beauty services — but earns irregularly and has no proof of skill. Through an ILO women's-empowerment / USAID / ADB gender batch, she could complete Maahir's Women Empowerment modules (Stitching, Home Food, Beauty) and build a certified portfolio — photos of her work, customer testimonials, AI-reviewed skill assessments, and verifiable certificates. Her Maahir profile could be shared with local salons, catering clients, and garment subcontractors. She could set her own prices, take direct orders via WhatsApp, and earn independently — with safety monitoring throughout. The home-based-worker formalization and women's economic empowerment that all three agencies fund.
Illustrative exercise concepts that COULD be created and aligned to the economic-growth, skills, and decent-work programmes USAID, ADB, and ILO fund. Each could be deployed in any batch, in any of 9 languages.
Learner uploads a mock garment photo + spec sheet; AI flags seam, label, and dye deviations against EU REACH/CPSIA standards. Builds export-compliance thinking for factory-floor workers.
Learner enters product, destination, and quantity; AI drafts a commercial invoice, packing list, and certificate-of-origin outline. Teaches real export paperwork for SMEs.
Learner uses AI to generate a 7-day pest + irrigation + weather advisory in Sindhi, then formats it as a WhatsApp broadcast list. Extension-worker skill at scale.
Learner uploads project photos, completes a safety assessment, and assembles a verifiable profile. AI reviews completeness for a microfinance-ready portfolio.
Learner enters order size, deadline, machine capacity; AI generates a cutting-stitching-QC schedule with load balancing. Builds SME production-planning skill.
Learner enters each member's monthly contribution; AI calculates shares, deductions, and a transparent dividend sheet in Urdu. Builds governance + numeracy.
Learner enters materials, time, design; AI suggests a fair retail price, drafts a product description, and identifies 3 online selling channels. Empowers home-based makers.
Learner pastes a mock job post; AI drafts a tailored proposal, benchmarks a fair price, and flags contract red flags. Builds digital-economy earning skill.
Learner reviews a mock factory scenario; AI presents child-labor risk indicators aligned to ILO conventions and drafts a reporting + remediation plan. Builds compliance-officer thinking.
Learner enters working hours, wages, safety conditions for a mock workplace; AI checks them against ILO DWCP standards and flags violations. Builds labor-rights literacy.
These are a sample. Maahir's 3,300+ exercises span all tracks and could be customized per agency, sector, region, and language.
Every USAID, ADB, and ILO skills programme confronts the same wall: employers don't trust the certificate. Billions are spent training beneficiaries — and then a forged piece of paper, or a meaningless attendance certificate, fails to convert training into a job. Here is how Maahir's portfolio + assessment system could solve the "no employer trust" gap.
Traditional training delivers a certificate of attendance. It says "this person sat in a room for 6 weeks." It does not say what they can actually do. Employers know this. They have hired "certified" welders who cannot weld, "trained" garment workers who cannot meet spec, and "IT-literate" graduates who freeze at a real keyboard. So they discount all certificates. The result: USAID, ADB, and ILO invest in training, beneficiaries complete it, and then... nothing. No job. No income uplift. No proof. The programme reports "1,000 trained" — but "0 placed" is the number that matters.
Maahir is built around a different principle: don't tell — show. Every learner could accumulate a real project portfolio alongside their certificates. Here is how the proof chain could work:
Applied, real-world exercises — not quizzes. A garment worker checks export compliance; an electrician diagrams a safe install.
Exercises accumulate into deliverables — a quality report, a wiring plan, a business plan, a production schedule.
AI + facilitator review every project. Personality, aptitude, and technical assessments benchmark the learner.
All projects + scores form a public portfolio — actual work, not a paper claim.
Each certificate carries a unique verification code — checkable at a public URL. No forgery.
Portfolio + certificate publish to a recruiter-visible profile. Employer sees the work before the interview.
The difference: an employer isn't asked to trust a piece of paper. They can click a link, see 5 real projects, read AI + facilitator reviews, verify the certificate code, and watch a skill-demonstration video. That is how a USAID/ADB/ILO-trained beneficiary could walk into an interview with proof — and walk out with a job. The "no employer trust" gap, closed by design.
USAID reports to Congress. ADB reports to its Board. ILO reports to its constituents. All three are asked the same question: "Did your training produce jobs?" Without proof of skills, the answer is stuck at "we trained X people." With Maahir's portfolio system, the answer could be: "we trained X, Y have verifiable portfolios, Z were placed, and their average income rose by N%." That is the results chain that turns a skills programme into an economic-growth outcome — and it starts with proof.
From an informal apprentice with no schooling to a university graduate seeking first employment — Maahir could serve every level these three agencies' programmes touch.
Voice-first delivery for pre-literate and informal-sector workers — electricians, mechanics, farm labourers, home-based workers. Roshni could teach in the learner's mother tongue, by voice, no reading required. Directly serves ILO informal-economy formalization and USAID private-sector goals.
Kids Mode: guardian-controlled, filtered, safe. Six child-friendly modules. Voice for pre-literate children. Foundational literacy + digital safety in any of 9 languages. Serves USAID Pakistan Reading Project + ILO child-labor-to-learning transitions.
Foundation track: digital literacy, AI fundamentals, internet safety, communication. Character modules introduce ethics, integrity, time management. The bridging stage toward secondary, TVET, or informal-trade readiness.
Full Foundation + early specialization. Students pick an applied track — garments, agriculture, construction, retail. Soft-skills deepening: workplace behavior, negotiation. Assessments begin — the ADB TVET and USAID youth-employment bridge.
Maahir could run as the digital delivery layer for TVET institutes — exercises on phone, proof submitted, AI + instructor review. Institutes become batch facilitators. Certificate verification builds employer trust. Directly serves ADB Skills Development and TVET strengthening.
Maahir as a parallel AI lab. Students register as interns, complete advanced tracks, build a 3–5 project portfolio. Talent Portal connects graduates to USAID private-sector partners — closing the youth-employment loop.
Not every beneficiary is a student. Maahir could serve adults: garment workers, electricians, extension workers, cooperative managers, home-based stitchers, returnee migrants. Flexible pace, no deadlines, mother-tongue delivery. Batch system groups adults by trade and district.
Degrees teach theory. Employers demand character. These are the skills that decide whether a learner stays employed, earns more, and contributes — what universities don't teach but employers demand. Maahir's 47 Character modules build the human qualities that economic-growth and decent-work programmes ultimately fund.
Scenarios on honesty, anti-corruption, ethical AI use. Foundational to every public-service, finance, and self-employment career these agencies support.
Punctuality, dress, email/WhatsApp etiquette, meeting conduct, hierarchy respect — the unwritten rules that decide who gets hired and promoted.
Precision in quality checking, data entry, document review. The trait that separates a hired garment worker from a rejected one.
Planning, prioritization, beating procrastination. Critical for informal workers and freelancers juggling orders, family, and deadlines.
Self-awareness, empathy, reading emotions. Vital for cooperative managers, extension workers, and first-generation employees.
Clear writing, active listening, customer-facing speech. Practiced with AI role-play partners in the learner's own language.
De-escalation, negotiation, mediation. Directly serves workplace retention and cooperative-governance goals.
Hygiene, dress codes, workplace presentation. The unspoken barrier keeping rural and home-based workers from service and export-facing jobs.
PPE, machine safety, fire hazards, chemical handling. Especially critical for garment, construction, and informal-sector workers — an ILO Decent Work core.
Coping with failure, stress, setback. The single most important trait for returnee migrants and informal workers facing economic shocks.
47 Character modules in total — each exercise-based, language-localized, and tracked. These are the skills that turn a learner into a Maahir — a skilled, whole person.
The ILO's Decent Work Country Programme rests on four pillars: jobs, rights, social protection, and social dialogue. Maahir's character and skills modules could embed Decent Work principles into every learner's journey — not as a separate workshop, but as the fabric of the training itself.
Modules on machine safety, fire hazards, chemical handling (REACH/cotton chemicals), ergonomics for garment and home-based workers. Safety scenarios specific to each trade — the DWCP occupational-safety-and-health pillar.
Modules on calculating fair rates for freelance, home-based, and informal work. AI benchmarks market prices so a bangle-maker or stitcher isn't underpaid by a middleman. Directly serves DWCP adequate-earnings pillar.
Workplace harassment, online harassment, and gender-based-violence awareness modules. 100+ safety scenarios. Distress detection routes at-risk learners (especially women) to a female counselor. DWCP rights-at-work pillar.
Modules on minimum wage, working hours, overtime, leave, contract literacy, and the right to organize — in plain Urdu/Sindhi/Pashto. Empowers informal and home-based workers to know and claim their rights under Pakistan's labor law.
Awareness modules on child-labor risks in supply chains (garment, leather, brick kilns, agriculture). For adult learners: recognizing and reporting child labor. For children: a path to Kids Mode learning instead. ILO's core mandate.
Modules on cooperative governance, collective bargaining basics, and worker organization — especially for informal and home-based workers who lack any collective voice. Builds the social-dialogue pillar of DWCP.
A proposed step-by-step path from signed MOU to first placement. Each step would be supported by built-in Maahir tooling. Applicable to USAID, ADB, or ILO — individually or jointly.
Agency programme officers (or implementing partners — government departments, NGOs, industry associations) could create batches in the Command Center — by programme ("USAID EGA Sindh Cohort", "ADB TVET Faisalabad", "ILO DWCP Garment Workers"), by sector, by region, or by outcome target. Each batch would get capacity limits, a timeline, a language default, and a module-set. Zero coding required.
Each batch could generate unique invite codes or a join link (e.g. /join/usaid-ega-sindh, /join/adb-tvet-faisalabad, /join/ilo-dwcp-garment). Codes distributed via implementing partners, TVET institutes, industry associations, or SMS. A co-branded landing page could carry the agency + Government of Pakistan + Maahir branding. Codes could be single-use, region-locked, and expiry-dated.
Local facilitators (TVET instructors, NGO staff, industry mentors, cooperative officers, extension workers) could receive teacher accounts. A short "How to Run a Maahir Batch" onboarding. Facilitators would not need to teach content — Roshni does that. They would monitor progress, review project submissions, motivate, and escalate distress signals.
Beneficiaries could register via phone: name, age, language, gender, location, accessibility needs, guardian info (for minors), current employment status, and baseline income (for salary-uplift tracking). Kids Mode would require parental consent. Roshni could greet them in their mother tongue and place them on the right path. The whole flow could work on a basic Android phone over 2G.
As learners complete exercises and projects, their portfolio builds automatically. Each project is reviewed by AI + facilitator. Certificates carry verification codes. By completion, every beneficiary has a public, verifiable proof of capability — not just an attendance certificate. This is what closes the employer-trust gap.
Graduates could receive a public Talent Portal profile — modules, projects, assessments, verifiable certificates — visible to recruiters and agency-linked industry partners. For informal-sector workers, the portfolio could serve as a bank-loan or business-registration credential. Skills could convert to jobs, loans, and formal status; placement and income data could flow back as impact evidence.
The Command Center could give the agency a real-time funnel per batch: registered → active → completed → certified → placed. The auto-nudge engine could follow up at 1, 3, 6, and 12 months post-completion for income data. One-click export of donor-ready reports — formatted for USAID, ADB, and ILO M&E frameworks and SDG reporting. Every claim backed by logged, auditable data.
Successful batches could become templates. Clone a proven USAID EGA Sindh cohort into a new Punjab one, or an ADB TVET Faisalabad batch into a Karachi one, in minutes. Scale from 100 to 100,000 beneficiaries with zero infrastructure investment — only facilitation cost scales. This is how a single pilot could reach national impact across all three agencies' mandates.
SDG 1 · No Poverty · SDG 4 · Quality Education · SDG 5 · Gender Equality · SDG 8 · Decent Work & Economic Growth · SDG 10 · Reduced Inequalities · SDG 17 · Partnerships
From a garment worker checking export compliance in Faisalabad to an informal electrician formalizing his skills in Lahore, from a cooperative manager in Sindh to a bangle-maker finding new markets in Hyderabad, from a returnee migrant re-skilling to a home-based stitcher earning independently — Maahir is ready to be the delivery, tracking, and proof platform for USAID, ADB, and ILO programmes in Pakistan. AI that enables non-IT work, for the real economy, with proof employers trust, in everyone's language. Let us set up a pilot batch within two weeks of your green light.
Developed by S4S · Aligned with SDGs 1, 4, 5, 8, 10, 17 · Operating in Pakistan · 9 Languages · 🌍 Ready for USAID, ADB & ILO