The delivery of government welfare schemes and public policies in developing nations continues toencounter deep-rooted structural inefficiencies, systemic friction, asymmetric information flows, and bureaucraticbottlenecks that collectively undermine intended socio- economic outcomes. Despite the proliferation of hundredsof welfare initiatives across sectors such as healthcare, education, agriculture, financial inclusion, and socialsecurity, a substantial proportion of eligible beneficiaries remain excluded from access. This exclusion is primarilydriven by limited awareness, fragmented information ecosystems, procedural complexity, repetitive documentationrequirements, and digitally inaccessible application mechanisms. Consequently, the gap between policyformulation and last-mile implementation persists as a critical governance challenge. This paper presents acomprehensive review of Sabal (“Bridge of Support”), a dual-platform, AI-driven technological ecosystemarchitected to systematically mitigate these infrastructural and procedural frictions. Sabal reconceptualizes welfaredelivery as an intelligent, data-coordinated pipeline by integrating two synergistic components: an AI-poweredcitizen-facing application portal (Sabal Setu) and an advanced spatial intelligence and analytics dashboard fornon-governmental organizations (Sabal AI). The ecosystem leverages state-of-the-art Optical CharacterRecognition (OCR) powered by Google DeepMind’s Gemini 2.0 Multimodal AI, heuristic and rule-based eligibilitymatching algorithms, real-time demographic analytics, and Geographic Information Systems (GIS) to create afriction-minimized, user-centric service architecture. Through automated document parsing, intelligent schemerecommendation engines, and dynamic geospatial visualization of underserved regions, Sabal transforms staticwelfare portals into adaptive, cognitive systems capable of proactive intervention. By systematically identifyingdemographic bottlenecks and computing a quantifiable Social Return on Investment (ROI) score across geographiczones, the platform empowers NGOs to prioritize interventions based on empirical evidence rather than anecdotalassessment. This enables optimized allocation of field resources, targeted awareness campaigns, and document- specific resolution strategies within high-impact regions. This review elaborates on the theoretical foundationsunderpinning AI-assisted governance, the architectural and methodological design of the Sabal ecosystem, itstechnical implementation across a modern full-stack infrastructure, and its broader sociotechnologicalimplications. By bridging the citizen-state-NGO triad through intelligent automation and spatial analytics, Sabaldemonstrates a scalable and replicable model for modernizing public welfare delivery systems in developingeconomies, advancing the paradigm of data-driven, inclusive, and accountable governanceKeywords:Artificial Intelligence, Spatial Intelligence, E-Governance, Optical Character Recog- nition, GovernmentWelfare Schemes, NGO Deployment, Data-Driven Policy.