What kind of AI projects do you take on?
We focus on five areas: on-device AI, agentic systems, model optimization, private AI architecture, and practical AI adoption. Each engagement starts with a concrete production constraint or operating goal.
On-Device AI Production ML AI Adoption
AI that ships
We build production AI systems: on-device inference, agentic workflows, model optimization, private AI architecture, and adoption strategy.
01 Prototype to production in weeks
CloudEdge Privacy & speed by design
ModelRuntime Optimize for target hardware
What We Do
Five focused ways to move AI into production, from on-device inference and agentic workflows to model optimization, private architecture, and adoption strategy.
On-Device AI
Run AI models directly on phones, IoT devices, and embedded hardware. We design for local inference, offline operation, and performance measured on the devices you support.
- On-device inference
- Data stays local
- Offline operation
- Device-specific profiling
Agentic Systems
Build AI agents for real business processes. We keep predictable steps in code, use language models where judgment is needed, and add approval gates for consequential actions.
- Process automation
- Tool integrations
- Evaluation suites
- Human approval gates
Model Optimization
Make AI models faster and less resource-intensive on the hardware that matters. We benchmark first, then reduce latency, memory use, and compute while tracking quality.
- Latency profiling
- Memory & compute reduction
- Hardware-specific tuning
- Quality benchmarks
Private AI Solutions
Define where sensitive data is stored, processed, and allowed to leave. We design local, on-premises, and tightly controlled cloud architectures around those boundaries.
- Data-flow mapping
- Local & on-premises inference
- Access controls
- Threat modeling
AI Adoption Strategy
Assess where AI can improve real work, choose a first project worth validating, and prepare the team that will own it in production.
- Opportunity assessment
- Pilot selection
- Team enablement
- Operating model
- 5
- Focused AI service areas
- Eval-first
- Evidence before launch
- Human gate
- Approval for consequential actions
- Target-fit
- Models measured on deployment hardware
From the Lab
All posts →On-Device LLMs on Android in 2026: ExecuTorch, llama.cpp, and MNN Compared
Native Android on-device LLMs without React Native. ExecuTorch JNI, llama.cpp-android, and MNN side by side — benchmarks, Kotlin integration patterns, and which framework to pick for your use case.
On-Device AI Inference Trends in 2026: What's Actually Shipping
Five trends making on-device AI the default in 2026: NPU stability on Snapdragon 8 Elite and Dimensity 9400, sub-1B LLMs at 12–15 tok/s, vision-camera pipelines, on-device RAG, and framework convergence. What's already in production.
Computer Vision in React Native: A Practical Guide with ExecuTorch v0.8.0
Classification, detection, segmentation, OCR, and vision-language models — all running on-device in your React Native app. A complete guide to CV with react-native-executorch v0.8.0.
Common Questions
What is on-device AI and when should I use it?
On-device AI runs machine learning models directly on the user's device instead of sending data to the cloud. Use it when you need real-time speed, data privacy, offline capability, or lower cloud costs.
On-Device AI in 2026: Sub-20ms Inference Is Here →How long does it take to go from prototype to production?
Timelines depend on the system boundary, deployment target, data access, and evidence required before launch. After discovery, we propose milestones with explicit success criteria rather than a generic delivery estimate.