On-device ML · Qualcomm Snapdragon
Production ML and AI systems,
built around real constraints.
Frazil is an ML and AI product engineering consultancy for companies shipping systems where model quality, workflow reliability, platform constraints, and release discipline all matter. We build on-device inference for Qualcomm Snapdragon, regulated ML infrastructure, and AI product workflows.
Shipped for
● Featured case study
Snapdragon Moments — Qualcomm
An Android app that auto-records gameplay and surfaces ML-detected highlights — Victory, Kill, Headshot — as twenty-second clips. Inference runs entirely on the device, with clip processing kept local.
Frazil built the product surface around Qualcomm's core engine: Jetpack Compose UI, engine wrappers, telemetry, memory and battery measurement, and release paths for device placement. The integration kept the model boundary stable while the product experience evolved.
- Kotlin
- Jetpack Compose
- Media3 / ExoPlayer
- On-device ML
- arm64-v8a · API 30+
What we do
Focused consulting practices. One bar: it has to ship.
On-device ML
Real-time inference for edge and device constraints. We handle the product surface, runtime constraints, measurement, and release path around the model.
ML infra for regulated industries
MLOps that survives audit. Pipeline lineage, versioning, drift monitoring, governance, and deployment patterns built in before the review.
AI agents and product workflows
Agentic workflows, retrieval, evals, fine-tuning, observability, and the product logic around them. We scope one concrete workstream at a time.
Platform engineering
CI/CD, IaC, Kubernetes, observability, and release operations for ML-heavy products. The goal is a system your team can keep running.
How we work
Small senior teams. Clear shipped outcomes.
Scoped workstreams, real ownership.
We work inside your process and keep the work tied to a clear outcome, technical owner, and release target.
Senior engineers, direct collaboration.
You brief senior engineers directly, and those same engineers stay close to the code, decisions, and handoff.
Working code beats slides.
We move toward runnable artifacts early, then iterate against the actual product surface, integration points, and release constraints.
Built for handoff.
We pick stacks the next engineer can read, document the important choices, and leave the system in a state your team can operate.
Contact
Have something that has to ship?
Tell us what you're building, the constraints, and what you've already tried. If the work is a fit, we'll scope a short paid pilot around a concrete deliverable.
We usually start with a defined workstream, then adapt the engagement around what the team needs to get it shipped.