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Introduction

While Silicon Valley dominated global AI headlines, a quiet revolution unfolded 7,000 miles away in Manila. Thinking Machines Lab emerged as Southeast Asia’s premier data science company—using artificial intelligence to solve uniquely Filipino challenges: from predicting typhoon damage and optimizing traffic flows to combating misinformation and improving government services.

Founded in 2015 by Stephanie Sy, David Ubian, and Mark Steve Samson, Thinking Machines Lab (often called just “Thinking Machines”) represents the Philippines’ answer to Palantir and Databricks—but focused on emerging market problems with local context and global technical rigor.

Thinking Machines’ breakthrough: applying world-class data science to Philippine-specific challenges that global tech giants overlooked. Their projects include mapping every building in the Philippines using satellite imagery for disaster preparedness, analyzing social media to detect COVID-19 misinformation, optimizing Manila’s notoriously chaotic traffic, and helping the government target social programs to the poorest families.

The company has become the go-to AI partner for Philippine government agencies, major corporations (telecommunications, banking, retail), and international development organizations. With 200+ data scientists, engineers, and domain experts, Thinking Machines generates $20M+ in revenue and has established itself as Southeast Asia’s leading homegrown AI consultancy.

From three founders in a co-working space to the region’s AI powerhouse, Thinking Machines demonstrates that cutting-edge technology can solve local problems—and build valuable companies—outside traditional tech hubs.

This article explores Thinking Machines’ journey from startup to national AI infrastructure, and how they’re using data science to transform the Philippines.

Founding Story & Background

The Philippine Context

To understand Thinking Machines, first understand the Philippines in 2015:

Opportunities:

• 106 million people: 13th most populous country
• Digital adoption: High smartphone penetration, social media usage
• Economic growth: 6-7% GDP growth annually
• Young population: Median age 25 (tech-savvy generation)
• Government modernization: Push to digitize services

Challenges:

• Infrastructure gaps: Traffic congestion, disaster vulnerability, unreliable power
• Data deserts: Limited digitized government data
• Inequality: Wide wealth gap, poverty targeting difficult
• Natural disasters: Typhoons, earthquakes, floods (20+ typhoons annually)
• Misinformation: High social media usage + rampant fake news

Tech Landscape (2015):

• BPO (Business Process Outsourcing) dominated tech sector
• Few product/AI companies
• Talent drain: Top engineers emigrated to U.S./Singapore
• Opportunity: Use data science to solve local problems, retain talent

The Founders’ Journey

Stephanie Sy – Co-Founder & Managing Director

Stephanie’s path to Thinking Machines:

Background:

• Education: Ateneo de Manila University (Economics), University of Oxford (Development Studies)
• Early Career: World Bank, policy research
• Passion: Development economics, poverty alleviation
• Insight: “Data science can transform how we understand and solve poverty, but few are applying it in emerging markets.”

David Ubian – Co-Founder & CTO

David’s journey:

Background:

• Education: Ateneo de Manila (Computer Science)
• Engineering: Software developer, machine learning enthusiast
• Experience: Built recommendation systems, NLP models
• Vision: “World-class data science can be built in Manila, solving local problems.”

Mark Steve Samson – Co-Founder

Mark’s role:

Background:

• Education: University of the Philippines (Computer Science)
• Data Science: Early ML practitioner in Philippines
• Community: Organized data science meetups, workshops

Founding Thinking Machines (2015)

The three founders met in Manila’s small but growing data science community:

The Idea (Early 2015):

“What if we built a data science consultancy focused on Philippine problems—disaster response, traffic, poverty, health—using cutting-edge AI but with local context?”

Market Gap:

• Global firms (McKinsey, BCG): Strategy consulting, light on technical depth
• Tech giants (Google, Facebook): Not focused on Philippine-specific problems
• Local IT firms: Software development, not AI/ML expertise
• Opportunity: World-class data science + local domain expertise

Name: “Thinking Machines” inspired by Alan Turing’s “thinking machines” concept—computers that think.

Initial Strategy:

1. Talent: Hire best Filipino data scientists, engineers (many studying abroad, convince to return)

2. Government: Partner with Philippine government agencies (DSWD – social welfare, MMDA – traffic, disaster response)

3. Social Impact: Focus on projects that improve lives (poverty, health, disasters)

4. Thought Leadership: Publish research, open-source tools, build community

5. Sustainability: Consulting revenue funds R&D and social impact projects

Early Projects (2015-2016)

Bootstrapped Start:

• Three founders + 5 data scientists
• Small office in Makati (Manila’s business district)
• Self-funded from savings

First Projects:

1. Poverty Mapping for Government:

• Partner with DSWD (Department of Social Welfare and Development)
• Use data (electricity consumption, mobile phone usage, satellite imagery) to estimate poverty at granular level
• Impact: Target social programs (conditional cash transfers) to neediest families
• Result: Improved targeting, reduced fraud

2. Typhoon Damage Assessment:

• Philippines hit by 20+ typhoons annually
• After disaster, assess damage using satellite imagery + ML
• Traditional: Weeks of manual surveys
• Thinking Machines: 48 hours of automated analysis
• Impact: Faster disaster response, aid distribution

3. Traffic Analysis for Manila:

• Manila has world’s worst traffic congestion
• Analyze MMDA traffic data, GPS traces
• Identify bottlenecks, recommend interventions
• Result: Data-driven traffic management

Traction:

• Government contracts: $100K-500K projects
• Media coverage: “Filipino startup using AI to solve traffic, poverty, disasters”
• Team growth: 10 → 20 employees (2016)

Seed Funding & Growth (2016-2017)

Seed Round (2016):

• Amount: $2 Million (estimated)
• Lead: Kickstart Ventures (Globe Telecom’s VC arm)
• Purpose: Hire team, expand product offerings

Key Hires:

• Data scientists from top universities (UP, Ateneo, MIT, Stanford alumni returning to PH)
• Domain experts (urban planning, public health, disaster response)
• Engineers (infrastructure, deployment)

Product Expansion:

• Geospatial intelligence platform
• NLP for social media analysis
• Predictive analytics for business (telecom, banking)

Founders & Key Team

Leadership Philosophy:

Local Impact, Global Standards:

• Use cutting-edge AI techniques (same as Silicon Valley)
• Apply to local problems (Filipino context)
• Retain top talent (keep them in Philippines, not emigrate)

Social Mission:

• “Data science for good”—projects that improve lives
• Partner with government on critical challenges
• Open-source tools for community

Funding & Investors

Seed Round (2016)

• Amount: $2 Million (estimated)
• Lead: Kickstart Ventures
• Purpose: Team expansion, product development

Series A (2019, estimated)

• Amount: $8 Million (estimated)
• Investors: Wavemaker Partners, angels
• Purpose: Regional expansion, verticals (health, finance, retail)

Total Funding Overview

• Total Raised: $10M+ (estimated)
• Valuation: $100M+ (estimated, 2024)
• Major Investors: Kickstart Ventures, Wavemaker Partners
• Profitability: Likely profitable (consulting model)

Note: Thinking Machines is relatively private about funding details—estimates based on industry reports.

Product & Technology Journey

A. Core Services

1. Government & Social Sector

Poverty Mapping:

• Use proxy indicators (electricity, phones, satellite imagery) to estimate household poverty
• Granular (barangay-level—smallest admin unit)
• Impact: Target $2B+ in social spending more effectively

Disaster Response:

• Post-typhoon damage assessment (satellite imagery + ML)
• Flood risk mapping
• Evacuation center optimization
• Impact: Save lives, faster aid distribution

Health Analytics:

• COVID-19 modeling (predict outbreaks, hospital capacity)
• Malnutrition prediction
• Disease surveillance
• Impact: Public health planning

Urban Planning:

• Traffic flow optimization
• Public transport route planning
• Land use analysis
• Impact: Reduce Manila’s legendary traffic

2. Telecommunications

Major clients: Globe Telecom, PLDT

Network Optimization:

• Cell tower placement (predict demand)
• Churn prediction (identify at-risk customers)
• Customer segmentation

Impact: Improve network coverage, reduce customer churn

3. Banking & Finance

Credit Risk Models:

• Predict loan defaults
• Alternative credit scoring (for unbanked populations)
• Fraud detection

Customer Analytics:

• Lifetime value prediction
• Product recommendations

4. Retail & E-commerce

Demand Forecasting:

• Predict product demand (optimize inventory)
• Dynamic pricing

Store Location Optimization:

• Use geospatial data to recommend new store locations

5. Geospatial Intelligence

Mapdwell Project (2018):

• Map every building in Philippines using satellite imagery + deep learning
• 20 million+ buildings identified
• Use Cases: Disaster preparedness, census, infrastructure planning
• Technology: Convolutional neural networks (CNNs) on satellite images

Impact: Most comprehensive building footprint dataset in Philippines

B. Technology Stack

Machine Learning:

• Frameworks: TensorFlow, PyTorch, scikit-learn
• Techniques: Deep learning, NLP, computer vision, geospatial ML

Infrastructure:

• Cloud: AWS, Google Cloud Platform
• Big Data: Spark, distributed computing
• Geospatial: QGIS, PostGIS, Google Earth Engine

Languages:

• Python (primary)
• R (statistical analysis)
• JavaScript (web apps, visualizations)

C. Open Source & Community

Philosophy: Share knowledge, build ecosystem

Open Source Projects:

• Geomancer: Geospatial feature engineering library
• Geoai Retail: Store location optimization toolkit
• Various utilities on GitHub

Blog & Research:

• Thinking Machines Blog: Technical deep dives, case studies
• Conference talks (NeurIPS, ICML, local conferences)
• Academic collaborations (UP, Ateneo)

Data Science Meetups:

• Host regular meetups in Manila
• Training workshops for students, professionals
• Impact: Grow Philippine data science community

Company Timeline Chart

📅 COMPANY MILESTONES

2015 ── Founded by Stephanie Sy, David Ubian, Mark Steve Samson | Bootstrapped | First government contracts
│
2016 ── Seed funding ($2M, Kickstart Ventures) | Typhoon damage assessment project | 20 employees
│
2017 ── Poverty mapping for DSWD | Traffic analytics for MMDA | 40 employees
│
2018 ── Mapdwell project (mapped 20M+ buildings) | 60 employees | $5M+ revenue
│
2019 ── Series A ($8M est.) | Telecom clients (Globe, PLDT) | Regional expansion | 100 employees
│
2020 ── COVID-19 response (modeling, misinformation detection) | 120 employees
│
2021 ── Banking/finance vertical | 150 employees | $15M revenue
│
2024 ── 200+ employees | $20M+ revenue | Southeast Asia’s leading AI consultancy

Key Metrics & KPIs

Competitor Comparison

📊 Thinking Machines vs Philippine/SEA Tech Competitors

Winner: Thinking Machines (Philippine AI/Data Science)

Thinking Machines leads in:

1. Technical Excellence: World-class data science (on par with Silicon Valley)
2. Local Expertise: Deep understanding of Philippine context
3. Government Partnership: Trusted by national/local government
4. Social Impact: Mission-driven, poverty/disaster focus
5. Talent Development: Retaining top Filipino data scientists

Where Others Win:

• Global consultancies: Brand, scale, C-suite access
• Local IT firms: Broader IT services, more mature
• Regional startups: VC-backed, consumer products

Thinking Machines is category leader in Philippine AI/data science consulting.

Business Model & Revenue Streams

Consulting Services Model

Primary Revenue: Project-based consulting

Pricing:

• Small Projects: $50K-200K (3-6 months)
• Medium Projects: $200K-500K (6-12 months)
• Large Projects: $500K-2M+ (multi-year)

Client Mix:

• Government (40%): Social programs, disaster response, urban planning
• Telecommunications (30%): Network optimization, customer analytics
• Banking/Finance (20%): Credit risk, fraud detection
• Retail/Other (10%): Demand forecasting, geospatial

Unit Economics

Per Project (average):

Revenue: $300K

Costs:

• Talent: $150K (data scientists, engineers)
• Infrastructure: $20K (cloud, tools)
• Sales/Marketing: $30K
• Overhead: $50K

Gross Margin: 30-40% (consulting typical)

Growth Strategy

Vertical Expansion: Deepen expertise in government, telecom, banking

Regional: Expand to other Southeast Asian markets (Indonesia, Vietnam)

Product-ize: Build SaaS products from consulting IP (e.g., geospatial platform)

Achievements & Awards

Industry Recognition

• Philippine Startup of the Year: Various awards from local ecosystem
• ASEAN ICT Awards: Recognition for social impact
• Government Partner: Trusted by Philippine government agencies

Social Impact

• Poverty Mapping: Improved targeting of $2B+ social spending
• Disaster Response: Faster post-typhoon damage assessment (save lives)
• 20M Buildings Mapped: Most comprehensive Philippine building dataset
• COVID-19: Modeling and misinformation detection during pandemic

Community Building

• Data Science Ecosystem: Hosted 100+ meetups, trained 1,000+ students/professionals
• Open Source: 10+ tools released to community
• Talent Retention: Kept top Filipino data scientists in country (vs. emigration)

Valuation & Financial Overview

💰 FINANCIAL OVERVIEW

Note: Private company—estimates based on industry reports, project sizes, team size.

Top Investors / Backers

1. Kickstart Ventures – Globe Telecom’s VC arm
2. Wavemaker Partners – Southeast Asia VC
3. Angels: Philippine tech/business leaders

Market Strategy & Expansion

Philippine Dominance

Strategy: Be the go-to AI partner in Philippines

Moat:

• Government relationships (trusted partner)
• Domain expertise (poverty, disasters, traffic—uniquely Philippine)
• Talent concentration (best data scientists)
• Brand (synonymous with “data science” in Philippines)

Regional Expansion (Future)

Target Markets: Indonesia, Vietnam, Thailand (similar challenges)

Approach: Replicate model—partner with governments, tackle local problems

Product Strategy (Future)

Opportunity: Product-ize consulting IP

Examples:

• Geospatial Platform: SaaS for building footprint data
• Poverty Mapping API: Governments/NGOs subscribe
• Traffic Optimization: Software for cities

Challenge: Consulting profitable—need conviction to invest in product R&D

Challenges & Controversies

Consulting Model Limitations

Challenge: Revenue tied to headcount (not scalable)

Response: Explore product opportunities, but consulting remains core

Government Dependency

Risk: 40% revenue from government—political/budget changes impact

Mitigation: Diversify into corporate clients (telecom, banking)

Talent Competition

Challenge: Global tech giants (Google, Facebook) recruit Filipino talent

Response: Mission-driven culture (“build for Philippines”), competitive comp, growth opportunities

Regional Competition

Challenge: Singapore-based startups better funded, broader market access

Response: Lean into Philippine expertise (hard to replicate)

No Major Controversies

Thinking Machines avoided scandals—strong ethics, social mission, government trust.

Corporate Social Responsibility (CSR)

Social Impact Mission

Core Purpose: Use data science to improve lives in Philippines

Focus Areas:

• Poverty Alleviation: Help government target social programs
• Disaster Response: Save lives through better preparedness
• Health: Public health analytics (COVID-19, malnutrition)
• Urban Planning: Reduce traffic, improve livability

Community Building

Data Science Ecosystem:

• Host meetups, workshops
• Train students, professionals
• Open-source tools
• Impact: Grow Philippine data science community, retain talent

Ethical AI

Principles:

• Transparency in government projects
• Privacy protection (aggregate data, no PII exposure)
• Bias mitigation in algorithms

Key Personalities & Mentors

Notable Products / Projects

Media & Social Media Presence

Recent News & Updates (2024-2026)

Regional Expansion (2024)

Indonesia Pilot: Testing replication of Philippines model in Jakarta

Geospatial Platform (2025)

Product Launch: SaaS platform for building footprint data (Southeast Asia)

Talent Milestone (2024)

200+ Employees: Largest data science team in Philippines

Government Partnership Expansion (2025)

National ID Integration: Analytics for Philippine ID system rollout

Lesser-Known Facts

1. Philippines-First: One of few successful tech product/consulting companies built in Philippines (most are BPO).

   
   

2. Talent Retention: Convinced dozens of Filipino data scientists to return from U.S./Singapore—stay local, work on impactful problems.

   
   

3. 20M Buildings Mapped: Most comprehensive building footprint dataset in Philippines—used by government, NGOs, researchers.

   
   

4. COVID-19 Response: Built misinformation detection models (social media) during pandemic—combat fake news.

   
   

5. Government Trust: Trusted partner for sensitive projects (poverty data, disaster response)—rare for private company.

   
   

6. Open Source: Released 10+ tools to community—unusual for consulting firm (typically IP-protective).

   
   

7. Meetup Organizers: Hosted 100+ data science meetups—built Philippine AI community from scratch.

   
   

8. Social Mission: 40% revenue from government/social sector—mission-driven, not just profit.

   
   

9. Disaster Response: After every major typhoon, Thinking Machines volunteers time to assess damage—social responsibility.

   
   

10. Local Context Expertise: Understand uniquely Philippine challenges (informal settlements, typhoon frequency, traffic chaos) that global firms miss.

    
    

11. Profitable Consulting: Likely profitable (consulting model)—rare for startups (usually burn VC cash).

    
    

12. Academic Collaboration: Partner with University of the Philippines, Ateneo—research + talent pipeline.

    
    

13. Mapdwell Recognition: Building mapping project won international awards—showcased at AI conferences globally.

    
    

14. Regional Model: Blueprint for how to build AI company in emerging markets—local problems, global standards.

    
    

15. Future Product Ambitions: Exploring transition from consulting to SaaS products—geospatial platform, poverty mapping API.

    
    

FAQs

What is Thinking Machines Lab?

Thinking Machines Lab (Thinking Machines Data Science) is the Philippines’ leading artificial intelligence and data science company. Founded in 2015 by Stephanie Sy, David Ubian, and Mark Steve Samson, Thinking Machines applies machine learning to solve local challenges: poverty mapping, disaster response, traffic optimization, and public health analytics. With 200+ employees and $20M+ revenue, the company serves government agencies, telecommunications companies, banks, and retailers across Southeast Asia.

Who founded Thinking Machines?

Thinking Machines was founded in 2015 by three Filipino entrepreneurs:

• Stephanie Sy (Managing Director): Oxford Development Studies, World Bank policy research background
• David Ubian (CTO): Ateneo Computer Science, machine learning engineer
• Mark Steve Samson (Co-Founder): University of the Philippines CS, data science community organizer

The trio combined technical excellence with social mission to build Southeast Asia’s premier AI consultancy.

What does Thinking Machines do?

Thinking Machines provides AI and data science consulting:

Government: Poverty mapping, disaster response, traffic optimization, COVID-19 modeling
Telecommunications: Network optimization, customer analytics, churn prediction
Banking: Credit risk models, fraud detection, alternative credit scoring
Retail: Demand forecasting, store location optimization
Geospatial Intelligence: Building footprint mapping, urban planning

Approach: Apply world-class machine learning to solve local Philippine challenges with deep domain expertise.

How does Thinking Machines map buildings?

Thinking Machines uses deep learning on satellite imagery to map buildings:

1. Satellite Images: High-resolution imagery from commercial satellites
2. CNN Models: Convolutional neural networks detect building footprints
3. Automated Pipeline: Process millions of images across Philippines
4. Validation: Human review + ground truth data
5. Dataset: 20 million+ buildings identified

Mapdwell Project (2018): Most comprehensive building dataset in Philippines—used for disaster preparedness, census, infrastructure planning.

Why is Thinking Machines important for the Philippines?

Thinking Machines is critical for Philippine development:

Poverty Alleviation: Improved targeting of $2B+ social spending (conditional cash transfers)
Disaster Response: Faster post-typhoon damage assessment—save lives
Traffic Management: Data-driven optimization of Manila’s congestion
Talent Retention: Keeps top Filipino data scientists in-country (vs. emigration)
Ecosystem Building: Trained 1,000+ students/professionals in data science
National Infrastructure: Built foundational datasets (20M buildings mapped)

Impact: Demonstrates that world-class AI can be built locally, solving local problems.

How does poverty mapping work?

Thinking Machines’ poverty mapping uses proxy indicators + machine learning:

Traditional Method:

• Door-to-door surveys (expensive, slow, outdated quickly)

Thinking Machines Approach:

1. Data Collection: Electricity consumption, mobile phone usage, satellite imagery, census data
2. Machine Learning: Train models to predict poverty from proxies
3. Granular Estimates: Barangay-level (smallest admin unit) poverty scores
4. Validation: Compare to survey data, adjust models
5. Updates: Refresh quarterly (vs. annual surveys)

Impact: Government targets social programs (4Ps conditional cash transfers) more effectively—reduce leakage, reach neediest families.

What is the Mapdwell project?

Mapdwell is Thinking Machines’ initiative to map every building in the Philippines:

Technology: Deep learning (CNNs) on satellite imagery
Scale: 20 million+ buildings identified
Coverage: Entire Philippines (300,000 km²)
Use Cases:

• Disaster preparedness (which buildings vulnerable?)
• Census planning (population estimates)
• Infrastructure planning (electricity, roads)
• Research (urbanization patterns)

Impact: Most comprehensive building footprint dataset in Philippines—open for government/NGO use.

How did Thinking Machines help during COVID-19?

Thinking Machines supported Philippine COVID-19 response:

Outbreak Modeling: Predicted case trajectories, hospital capacity needs
Misinformation Detection: Social media analysis to identify fake news about vaccines, treatments
Government Analytics: Dashboard for Department of Health (track testing, cases)
Resource Allocation: Optimize distribution of PPE, ventilators

Impact: Evidence-based pandemic response—saved lives through better data.

Does Thinking Machines do social impact work?

Yes—social impact is core mission:

Government Projects (40% revenue): Poverty, disasters, health, traffic—improve lives
Pro Bono: Volunteer time for disaster response (typhoon damage assessment)
Open Source: Released tools to community (geospatial libraries, tutorials)
Education: Host meetups, train students, grow data science ecosystem
Ethical AI: Transparency, privacy protection, bias mitigation

Philosophy: “Data science for good”—use AI to solve problems that matter.

What is the future of Thinking Machines?

Thinking Machines’ roadmap:

Near-Term (2024-2026):

• Regional Expansion: Replicate model in Indonesia, Vietnam
• Product Development: SaaS platform for geospatial intelligence
• Verticals: Deepen healthcare, finance expertise

Long-Term:

• Southeast Asia Leader: Expand beyond Philippines
• Product Company: Transition from consulting to scalable SaaS
• IPO/Exit: Potential acquisition by regional tech giant or IPO

Vision: Become Southeast Asia’s Palantir—using AI for government, social good, and commercial applications.

Conclusion

From three founders in a Makati co-working space to 200+ employees and $20M+ revenue, Thinking Machines Lab’s journey demonstrates that world-class AI companies can thrive outside Silicon Valley—especially when solving uniquely local problems with global technical standards.

Key Takeaways:

✅ Local Impact: AI applied to Philippine challenges (poverty, disasters, traffic)
✅ Technical Excellence: World-class data science (on par with Silicon Valley firms)
✅ Government Partnership: Trusted by Philippine agencies (50+ projects)
✅ Talent Retention: Keeping top Filipino data scientists in-country
✅ Social Mission: “Data science for good”—improving lives, not just profit
✅ Ecosystem Building: Trained 1,000+, open-sourced tools, grew community

What’s Next for Thinking Machines?

The coming years will determine if Thinking Machines scales from Philippine leader to Southeast Asian giant:

Opportunities:

• Regional Expansion: Indonesia, Vietnam (similar challenges, larger markets)
• Product Strategy: SaaS platforms (geospatial, poverty mapping APIs)
• Verticals: Deepen healthcare, finance, government tech
• Acquisition: Strategic buyer (Google, Grab, regional tech conglomerate)
• IPO: Public offering on Philippine or regional exchange

Challenges:

• Consulting Limitations: Revenue tied to headcount (not scalable like SaaS)
• Talent Competition: Global tech giants recruiting Filipino data scientists
• Government Dependency: 40% revenue from government (political/budget risk)
• Regional Competition: Singapore-based startups better funded
• Product Transition: Requires investment, different skill set from consulting

For emerging market entrepreneurs, Thinking Machines offers a powerful blueprint: Solve local problems with world-class technology, retain local talent, build trusted relationships with government, and focus on social impact alongside profit.

As Stephanie Sy says: “Data science can transform lives in the Philippines. We don’t need to move to Silicon Valley to do cutting-edge work—we can build it here, for our country.”

With 200+ data scientists, 20 million buildings mapped, poverty mapping improving $2B+ in social spending, and disaster response saving lives after typhoons, Thinking Machines has established itself as the Philippines’ AI infrastructure—and a model for how technology companies can drive social change.

The question is whether they can scale regionally and transition from consulting to product—or if they remain a successful but niche Philippine consultancy.

By 2027, we’ll know if Thinking Machines became Southeast Asia’s Palantir—or if they stayed focused on deep, impactful work in their home market.

One thing is certain: Thinking Machines proved that world-class AI can be built anywhere, solving problems that matter most to local communities.

And for the Philippine government agencies, disaster responders, and millions of families benefiting from better-targeted social programs, Thinking Machines is already indispensable.

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