Latency
Built by someone who has worked on sub-50ms p99 serving paths and performance-sensitive ML systems.
We built Octomil for teams that need AI to survive contact with production.
Octomil started from a simple observation: most teams do not fail because they lack a model. They fail because shipping, routing, monitoring, and operating that model across real devices is still too hard. We are building the control plane for on-device AI.
Why Sean started Octomil
Before starting Octomil, Sean Bangalore worked on large-scale machine learning systems at Apple and Amazon. His work focused on the parts of ML that become painful in production: latency, rollout safety, infrastructure efficiency, observability, experimentation, and operating services under real traffic.
At Apple, he worked on runtime LLM integration, caching, deployment systems, and performance improvements that pushed critical paths below 50ms p99 while reducing infrastructure cost. At Amazon, he led and built optimization and recommendation systems that served high-volume ad traffic, improved execution speed by an order of magnitude, and supported revenue-critical workflows.
That experience shaped the thesis behind Octomil. The gap in the market is not another research framework. It is the operating layer that helps teams move from cloud-only prototypes to reliable, cost-efficient, on-device AI in production.
Rollout safety
Shaped by experience with configuration systems, feature flags, staged delivery, and production change management.
Cost control
Designed with the realities of infrastructure efficiency, hardware targeting, and cost-aware inference in mind.
What Octomil is built to solve
Teams need more than model access. They need a system for deploying models to real devices, routing traffic between local and cloud paths, rolling changes out safely, monitoring fleet behavior, and keeping quality high while costs stay under control. That is the problem Octomil exists to solve.
What we believe
On-device first
Inference should run on-device when it can, and fall back to cloud only when it should.
Operations matter
Deployment and operations matter as much as model quality.
Control before abstraction
Teams need visibility, rollback, and control before they need more abstraction.
Core product focus
Enterprise features should support the core product, not define its identity.
Sean Bangalore is the founder of Octomil. He previously worked on machine learning infrastructure and production systems at Apple and Amazon, with a focus on latency, deployment, experimentation, observability, and cost-efficient scale.
We're hiring
We're looking for engineers who've built ML infrastructure, mobile SDKs, or distributed systems at scale. If you've seen inference costs eat margins and want to solve the problem at the infrastructure layer, we'd like to talk.
Get in touch
Office
Octomil
447 Broadway 2nd Floor #1202
New York, NY 10013
If you are moving AI from prototype to production, we would love to help.
Octomil is for teams that want a practical path to deploying, routing, and operating AI across phones, browsers, laptops, and edge hardware.