Tibbi

Tibbi

Tibbi

AI-powered Whatsapp-based platform delivering accessible primary care to bridge healthcare disparities through regionally trained models.

Lahore, Pakistan

PAK

Hybrid of for-profit and nonprofit

In Pakistan, over 60% of the population lacks access to consistent primary healthcare — especially in rural areas where there may be just one doctor per 15,000 people. This creates a dangerous gap where treatable illnesses like fevers, infections, and dehydration escalate into emergencies. Compounding this, nearly half of Pakistanis are illiterate, and existing health platforms (e.g., WebMD) are English-only, bandwidth-heavy, and culturally disconnected. Having been trained on Western data, they are not familiar with the specific regional dialects and medical idioms of Pakistan. Women are especially affected — often unable to travel alone, they rely on Lady Health Workers for care. Yet these Lady Health Workers are overstretched and still reliant on paper-based systems. As a result, families often self-medicate blindly or delay seeking treatment until it’s too late. Globally, the WHO reports that 4.5 billion people face similar primary care barriers in LMICs, driven by understaffed systems, digital exclusion, and language inaccessibility. This, coupled with a measly allocation of the national budget towards the healthcare sector of 1.2%, creates an environment where the less fortunate are burdened with medical concerns year round with no visible improvement in sight.

Tibbi is a two-faceted solution: it is an AI-based WhatsApp chatbot for patients, and an offline triage dashboard for Pakistan’s Lady Health Workers (LHWs). Users — many of whom are low-literacy and rural — can speak or type symptoms in Urdu, Punjabi, Pashto, or Sindhi. Tibbi’s backend uses multilingual large language models, trained on local symptom descriptions, slang, and illness patterns, to return simple, safe self-care advice, flag medical red flags, and refer users to nearby clinics via open-source map data. For those without personal devices, Tibbi runs in “Health House Mode” on the phones of 100,000+ government-trained Lady Health Workers, turning their homes into walk-in triage centers. This version syncs with national paper-to-digital recordkeeping workflows and lets Lady Health Workers document cases, refer high-risk patients, and receive real-time prompts during home visits. Clinics receive digital QR code referral slips and basic triage summaries to reduce patient delays. Tibbi even includes keyword-based SMS fallback for areas without smartphones. All features are optimized for low bandwidth, run on $30 Android phones, and are co-designed with Lady Health Workers to match their field constraints.

Tibbi serves two key groups: rural, low-literacy communities with limited access to care, and Pakistan’s Lady Health Workers, who are often their only healthcare link. The Lady Health Worker Program is a national public health initiative that deploys over 100,000 trained women into underserved regions — each responsible for about 1,000 people. These workers serve as the critical bridge between households and hospitals, conducting door-to-door checkups, providing maternal and child care, and managing basic illness triage. But their tools remain paper-based and disconnected, limiting how much they can do. Tibbi helps both sides. For patients, it provides a WhatsApp-based chatbot that works in Urdu, Punjabi, Sindhi, or Pashto, giving instant self-care advice, red flag alerts, and directions to the nearest clinic. For those without phones, Tibbi runs in “Health House Mode” on the Lady Health Workers’ devices — transforming her home into a triage point. Lady Health Workers can use Tibbi to log visits, receive smart prompts during interviews, and refer patients digitally. By supporting both the underserved and the workers serving them, Tibbi upgrades a nationwide system that already reaches millions — turning it from paper-based outreach into real-time, AI-assisted primary care.