Integration & Infrastructure
APIs, data pipelines, and connecting AI to legacy systems.
Articles in this direction
5 articles
LLM Integration into Existing CRM and ERP Software in Canada: Architecture, Data Residency and Cost
How to integrate an LLM with the CRM or ERP you already run in Canada: three architecture patterns, where prompts are processed, PIPEDA and Law 25 duties, cost drivers and a six-week pilot.
How to Integrate LLMs into Your Business Software in 2026
A practical 2026 guide to LLM integration: which models and APIs to use, what they cost per month, Python patterns that hold up in production, and the steps from first call to live feature.
Building AI Data Pipelines: From Raw Data to Actionable Business Insights
Modern AI data pipelines transform fragmented, inconsistent raw data into governed, queryable assets — the foundation that makes every downstream AI use case actually work in production.
LLM Integration Patterns for Enterprise Architecture
The six LLM integration patterns enterprises use in 2026, where an LLM gateway fits, which provider options keep data in Canada, and the governance a regulated organization needs.
You Don't Need to Replace Your Legacy Systems to Deploy AI
The biggest misconception in enterprise AI: that you need modern infrastructure before AI can work. The reality is that AI can be integrated with legacy systems through pragmatic bridge architectures.
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