AI agents that run on-device and offline, on the hardware the world already owns.
Fastagger shrinks AI models to run on smartphones, drones, and robots with under 4GB of RAM, with no GPU, no cloud round-trip, and no per-inference cost. Enterprises keep full control over them in the field. Reaching 1M+ devices through Africa’s largest telco, and running fully offline in the field where the cloud can’t.
// PERCEIVE → REASON → LEARN → ACT, entirely on edge
Innovation Showcase Winner: the brightest new player in AI infrastructure.
Fastagger won the inaugural Innovation Showcase at the AI Infra Summit for our Edge AI platform enabling agents to run directly on devices at the edge of the network, without relying on cloud computing. Selected over the field by judges from three of the world’s leading deep-tech investment firms.
Big Tech’s on-device AI reaches barely 1 in 8 smartphones, and the bar keeps rising.
Roughly 600–700 million premium devices can run Apple’s or Google’s on-device AI, leaving close to 90% of the world’s ~5 billion smartphones, and virtually every deployed drone, robot, and industrial edge device, locked out. Google’s newest on-device AI demands 12GB of RAM; the wall is getting higher, not lower.
Meanwhile, cloud inference breaks exactly where the field work happens: no connectivity, no data residency, and a per-query bill that never stops. Fastagger unlocks AI for the other 9 in 10, and gives enterprises control over models running in the field.
Fastagger Embodied AI Runtime
One platform to take a model from lab to fleet: develop → optimize → deploy → observe → update, across thousands of heterogeneous edge devices.
AI Model Optimization
Compression, quantization, and compilation that shrink foundation models to run on <4GB RAM hardware, with watermarking and encryption for model IP protection.
On-Device SDK
Native and React Native SDKs that run inference fully offline on Android, drones, and embedded devices, with sensors, actuators, and state management built in.
Orchestration Platform
Enterprise control plane for the fleet: deployment, telemetry, continuous validation, anomaly detection, federated learning, and billing. Every model, every device.
// Roadmap: Zero-Knowledge Proof of Inference: confidential attestation and cross-model learning, without exposing data.
Products in production. Not demos.
Every product below runs on the Fastagger runtime: proof the infrastructure works at scale, in the field, on constrained hardware.
Fastagger Workforce
Field attendance and patrol verification from any phone, down to SMS on feature phones, streaming to a live operations dashboard. Catches ghost workers in week one; audit-ready from day one.
Auni · Document AI
Document AI for African SMEs: M-Pesa statements, KYC, and loan files, extracted and structured into live business dashboards. A hybrid on-device/Azure architecture optimized for low-end smartphones and low connectivity, live inside Africa’s largest mobile-money super-app.
Vision & Edge Runtime
Full computer vision inference on 4GB Android, fully offline. Deployed for FTTx fiber field audits at a large telecom group. One design-partner slot open for the next vertical.
Scaled where it counts. Piloted where it’s hardest.
One deployment at telco scale, two paid pilots proving the fully-offline runtime in the field. Same platform: hybrid where connectivity allows, fully offline where it doesn’t. The same constraints define US defense, healthcare, and industrial edge deployments.
Document AI for SME intelligence
Auni, inside the M-Pesa Business Super App: 6,000+ businesses signed up, up from 3,500 in the first 90 days, with distribution across 1M+ merchant devices on Africa’s largest telco.
Workforce verification via SMS
SMS-first, GPS-verified check-ins for zero-smartphone environments, live to HQ dashboards, proving the delivery layer for the hardest connectivity tier.
Fully offline CV field audits
Object detection for FTTx fiber audits running fully offline on technicians’ Android phones. Production computer vision under 4GB RAM, zero cloud dependency.
A moat built where the constraints are hardest.
Deep expertise in constrained environments
Built from emerging-market realities (intermittent connectivity, low-RAM hardware, sovereign data requirements), and directly transferable to US defense, healthcare, and industrial edge.
Distribution others haven’t built
1M+ devices reached via telecom partnership, with a 62M-subscriber channel and 7,000-enterprise co-sell network behind it. Competitors optimize models; we own the route to the devices.
Privacy-preserving, resilient runtime
Nothing leaves the device. All inference happens on-edge, so it passes enterprise security review where cloud data residency fails. ZK proof-of-inference and federated learning on the roadmap.
Put AI on the devices you already own.
Enterprise pilot, telco partnership, or platform deep-dive: tell us your hardware environment and target use case. Replies come from the founding team within one business day.