Workstream 01
Edge runtimes
Deploy business logic globally on Cloudflare Workers, Vercel Edge, Deno Deploy, or Fastly Compute.
CAPABILITIES
Edge computing
Inference where your users are. We deploy compute, ML, and storage to the edge when latency, privacy, and resilience goals justify the added architecture tier.
Edge runtimes
On-device ML
Hybrid cloud + edge architecture
Capability focus areas
Edge platformsOn-device MLMobileSync and storage
What we do
Workstream 02
On-device ML
Core ML, TFLite, and ONNX Runtime optimizations including quantization and model size reduction.
Workstream 03
Hybrid cloud + edge architecture
Design split-responsibility systems balancing edge responsiveness with centralized reliability.
Workstream 04
Edge AI inference
Run lightweight classifiers, recommenders, and embedding models close to users.
Workstream 05
IoT and embedded delivery
Device protocols, OTA workflows, and resilient sync patterns for distributed environments.
Workstream 06
Edge observability and controls
Regional telemetry, rollback switches, and fallback strategies that keep edge rollouts safe under live traffic.
How we work
01
Trade-off analysis
Evaluate latency, data gravity, compliance, and cost to define what should run at edge vs origin.
02
Pilot implementation
Build a scoped edge slice with observability and fallback behavior to validate impact quickly.
03
Scale and harden
Expand edge workloads with rollout controls, caching strategy, and reliability guardrails.
04
Operational handoff
Deliver runbooks and deployment practices for sustained multi-region operation.
Tech we use
Edge platforms
Cloudflare WorkersDurable ObjectsVercel EdgeFastlyLambda@Edge
On-device ML
Core MLTFLiteONNXPyTorch MobileWebGPUWASM
Mobile
KotlinSwiftFlutterReact Native
Sync and storage
SQLiteRealmCRDTsY.js
Featured case study
Proof in production
We moved personalization scoring to Cloudflare Workers and reduced p95 latency from 320ms to 24ms.
Read case studyQuestions we get
How do we choose edge vs centralized compute?
We run an explicit trade-off analysis in week one based on latency sensitivity, compliance, and operational cost.
Can you run LLMs at the edge?
Small models are viable at edge and on-device; frontier models typically remain centralized.
Can migration happen incrementally?
Yes. We usually begin with high-latency hotspots and expand once impact and safety are validated.
Latency-sensitive product?
We can help you move the right workloads to edge infrastructure with controlled rollout risk.
Move to the edge