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-query cost. Proven in production where connectivity fails, hardware is old, and data cannot legally leave the device.
// 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 merchants: statements, KYC and loan files structured into live dashboards. Hybrid on-device architecture tuned for low-end handsets and intermittent connectivity.
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.
Three classes of constraint, solved in production.
We didn’t set out to sell to telcos. We set out to run AI where nothing else does: sub-4GB hardware, no connectivity, and data that cannot leave the device. Those are the same constraints that define US defense, industrial and healthcare edge.
Document AI for SME intelligence
Document AI running on 4GB Android handsets. 6,000+ businesses in production, up from 3,500 in the first 90 days. Distributed through a merchant super-app (paid pilot with a tier-1 mobile operator).
Workforce verification via SMS
GPS-verified check-ins over SMS for field teams with no smartphones at all, streaming live to HQ. Paid pilot with a global private security firm.
Fully offline CV field audits
Object detection running fully offline on field technicians’ Android phones. Production computer vision under 4GB RAM, zero cloud calls. Technical pilot with a large infrastructure operator.
A moat built where the constraints are hardest.
Deep expertise in constrained environments
The constraints that define a disconnected field deployment anywhere are the constraints that define US defense, industrial and healthcare edge. We already ship under them: sub-4GB hardware, no connectivity, sovereign data.
A channel most infra startups never get
Embedded inside a mobile operator’s merchant app, which took us from zero to 6,000 businesses in five months with no direct sales motion. Competitors optimize models in a vacuum; we ship into a live install base.
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.