Building AI-Powered Apps: From Concept to Production
Montreal is uniquely positioned for AI app development. Home to Mila (one of the world's top AI research labs), pioneering researchers like Yoshua Bengio, and a thriving ecosystem of AI startups, Montreal developers have access to cutting-edge AI resources.
Step 1: Define Your AI Use Case
Not every app needs AI. Start by asking:
- What repetitive task can AI automate for your users?
- Where can AI add personalization that would be impossible manually?
- Can AI help your users make better decisions faster?
Step 2: Choose Your AI Model
- GPT-4o — General purpose, vision, code ($$, medium latency)
- GPT-4o-mini — Cost-sensitive, high volume ($, fast)
- Claude 3.5 Sonnet — Long documents, nuanced reasoning ($$, medium)
- Gemini Pro — Google ecosystem, multimodal ($$, medium)
- Llama 3 (self-hosted) — Privacy-critical, no API costs
Step 3: Architecture Decisions
Never expose AI API keys in your mobile app. Always proxy through your backend. Key patterns include simple integration, RAG (Retrieval-Augmented Generation), agent frameworks, and fine-tuning.
Step 4: Production Hardening
- Rate limiting — protect against API abuse
- Caching — reduce API calls by 40-60%
- Fallbacks — graceful degradation when AI API is down
- Content moderation — filter inappropriate outputs
- Monitoring — track quality, latency, and costs in real-time
Montreal-Specific Considerations
- Bilingual support: Ensure your AI handles English and French fluently
- Privacy (Law 25): Quebec's privacy law requires transparency about AI data processing
- Tax credits: AI development may qualify for SR&ED and OIDIA R&D tax credits — potentially recovering 30-60% of development costs
Contact LIAWEB for a free AI architecture consultation.
Written by
Alex Rivera
AI & Backend Lead
Ready to Build Your Next Project?
Let's discuss how we can bring your vision to life.
Start a Conversation