On-Device AI
Privacy-first AI that runs entirely on your hardware. Local inference with WebGL, Ollama. Zero data leaves your infrastructure.
- ✓ Production-ready implementation
- ✓ Strong software engineering foundations
- ✓ Scalable and maintainable solutions
- ✓ Expert guidance throughout
Why Choose This Service
Production-ready solutions with proven results
Complete Privacy
Data never leaves your device or infrastructure. No cloud APIs, no data transmission, no privacy concerns.
Local Inference
Run models via WebGL in browsers or Ollama on servers. Full AI capabilities without external dependencies.
Flexible Deployment
Browser-based for end-user devices, on-premise servers for enterprise, or edge devices for IoT. Your choice.
Cost Effective
No per-token API costs. Once deployed, inference is essentially free. Scales without scaling bills.
Works Offline
Full functionality without internet connection. Critical for healthcare, field operations, and secure environments.
Your Models
Deploy fine-tuned models you own. No vendor lock-in. Full control over model updates and versions.
Our Implementation Process
From concept to production in 8-12 weeks
Requirements Analysis
1 weekUnderstand your privacy requirements, hardware constraints, and performance needs. Define what AI capabilities you need locally.
Model Selection & Optimization
1-2 weeksChoose appropriate models (Whisper, Gemma, Llama, etc.) and optimize for your target hardware. Quantization and pruning as needed.
Integration Development
2-3 weeksBuild the local inference pipeline integrated with your application. WebGL for browsers, Ollama for servers, or custom runtimes.
Testing & Deployment
1-2 weeksValidate on target hardware, optimize performance, and deploy. Training for your team on maintaining the system.
Compare AI Solutions
Choose the right approach for your specific needs
| Feature | RAG & GraphRAG | LLM Fine-tuning | AI Agents |
|---|---|---|---|
| Best For | Dynamic knowledge, Q&A | Domain-specific tasks | Complex workflows |
| Setup Time | 2-4 weeks | 4-8 weeks | 3-6 weeks |
| Cost | $$ | $$$ | $$ |
| Accuracy | High with good data | Very high | Variable |
| Maintenance | Low | Medium | High |
| Use When | Need latest information | Need consistent behavior | Need autonomy |
RAG & GraphRAG
- Best For
- Dynamic knowledge, Q&A
- Setup Time
- 2-4 weeks
- Cost
- $$
- Accuracy
- High with good data
- Maintenance
- Low
- Use When
- Need latest information
LLM Fine-tuning
- Best For
- Domain-specific tasks
- Setup Time
- 4-8 weeks
- Cost
- $$$
- Accuracy
- Very high
- Maintenance
- Medium
- Use When
- Need consistent behavior
AI Agents
- Best For
- Complex workflows
- Setup Time
- 3-6 weeks
- Cost
- $$
- Accuracy
- Variable
- Maintenance
- High
- Use When
- Need autonomy
Frequently Asked Questions
What models can run on-device? ▼
Speech-to-text (Whisper), small-to-medium LLMs (Gemma, Llama, Phi), embedding models, and specialized models. The limit depends on your hardware, but modern devices can run surprisingly capable models.
How does browser-based AI work? ▼
We use WebGL and WebGPU to run models directly in the browser using the device's GPU. No installation required, no data leaves the browser. Works on laptops, tablets, and even phones.
What about performance vs cloud APIs? ▼
Local models are typically smaller and may be less capable than GPT-4 for general tasks. However, for domain-specific applications with fine-tuned models, local can match or exceed cloud performance while maintaining privacy.
Is on-device AI HIPAA compliant? ▼
Yes. When data never leaves the device, there's no data transmission to secure. This simplifies HIPAA compliance significantly. Our dental documentation system serves 200+ practices with full GDPR compliance.
What hardware do we need? ▼
For browser-based: any modern device with a GPU (most laptops/desktops from 2018+). For server deployment: depends on model size and throughput needs. We help you spec appropriate hardware.
Can we update models after deployment? ▼
Yes. Models can be updated by deploying new versions. For browser-based solutions, users get updates automatically. For on-premise, you control the update schedule.
What about internet-connected features? ▼
On-device AI can work alongside cloud features. You might run sensitive processing locally while using cloud for non-sensitive operations. We help you design the right hybrid architecture.
How do you handle model size limitations? ▼
Through quantization (4-bit, 8-bit), pruning, and model distillation. A 7B parameter model can run on a laptop GPU. We optimize for your specific hardware and performance requirements.
Still have questions? We're here to help. Contact us for more information.
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Let's discuss how we can help with your on-device ai implementation.