Cloud cost pressure
Sending routine queries to cloud drives avoidable cost
Edge-first AI infrastructure
Eliminating the GPU tax in AI.
Routes every query to the lowest-cost compute layer — edge first, cloud only when necessary.
70%Up to 70% of queries handled at the edge
50-80%Up to 50–80% reduction in cloud inference cost
500M+data-constrained smartphone users
Why It Matters
AI adoption will be decided by unit economics.
AI fails at scale when cost per query becomes unsustainable. This system fixes that.
Bandwidth constraints
Low-cost devices and weak networks limit cloud-first AI
Local intelligence
Routine tasks run on-device. Advanced models used selectively
How It Works
Efficient AI Orchestration
Each query is routed to the most efficient compute path
Powered by a proprietary orchestration engine. Patent pending.
Query
AI Engine
Edge — 70%
Cloud CPU — 20%
LLM — 10%
Result
Target routing distribution based on early workload assumptions; validation planned through pilot deployments.
Latency
Local-first responses
GPU Load
Reduced cloud usage
Data Control
Routine tasks stay local
Market
Built for practical AI access.
Addressable today
Data-constrained users underserved by cloud AI
Potential by 2030
India, SE Asia, Africa, Latin America
Company
AI cost optimization infrastructure.
AI for All is a digital-native business building software infrastructure for lower-cost AI inference across edge devices and cloud systems.
Business and product
The company is developing an orchestration layer that evaluates each AI query and routes it to the lowest-cost compute path: on-device execution first, cloud CPU when suitable, and larger LLMs only when needed. The goal is to make AI access economically practical for bandwidth- and cost-constrained users, institutions, and emerging-market deployments.
Business modelUsage-based API, institutional subscriptions, and enterprise partnerships
Product stageMVP architecture built; edge-cloud integration in testing
FounderGaurav Kumar Singh
Contactgaurav@aiforall.ltd
Public review details
Digital-native model
Core value is delivered through AI routing software, APIs, and infrastructure tooling rather than consulting services.
Target customers
NGOs, public-sector pilots, SMBs, schools, telecom/OEM partners, and developers building AI for constrained environments.
Public links
Founder profile, LinkedIn, blog, demo request, sitemap, business email, and address are listed on this domain.
Business Model & Milestones
Revenue Model
API & Orchestration Layer
Usage-based pricing tied to compute efficiency (cost advantage vs cloud-only)
SMB & Institutional Tools
Subscription AI tools (SMBs, schools, NGOs)
Enterprise & Partnerships
Enterprise deployments (OEM, telecom, government)
Early Commercial Signals
Target pilots and future paid deployments
Future Expansion
Developer SDK and ecosystem (scale)
Milestones
2026 (Year 1)
- MVP deployment (edge + cloud)
- Initial pilot deployments (NGO / government)
- Cost advantage validation
- First paid contract
2027 (Year 2)
- Pilot conversion → production scale
- Regional partnerships
- API & developer platform launch
- Unit economics proven
Customer Segments
Target beachhead: NGO & government customers in emerging markets
OEM & Infra (Future)
- Device makers
- Telecoms
- Infra providers
- Platform integration
SMBs & Institutions (Future)
- Small businesses
- Schools, NGOs
- Local orgs
- Daily AI operations
Developers (Future)
- API access
- SDKs
- Build on platform
Progress
Current stage
DoneEdge layer live (local testing)
DonePatent filed - complete specification, App No. 202511059693
DoneCore orchestration implemented
In progressCloud pipeline live, optimizing
In progressEdge–cloud integration testing
To startAndroid APK (build + testing)
NextFirst NGO/government pilot
Founder
Operator-led execution.
AI for All applies operating discipline to AI access—reducing waste, improving efficiency, and making unit economics work at scale.
Gaurav Kumar Singh
Founder
Most AI systems are built for the top 1%. The rest face cost and access barriers.
My background in industrial operations and supply chains is built on one principle:
systems scale only when unit economics work.
This approach applies that discipline to AI infrastructure—
making intelligence efficient, scalable, and accessible.
20+ years in operations and supply chains—now applied to AI.
Launch a pilot
For partnerships, pilot deployments, and investor discussions.
Raising pre-seed to launch first pilots and validate unit economics.