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Guides on AI integration — connecting LLMs, ML models, and AI agents into existing business systems, ERPs, and workflows.
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Model routing sends different AI tasks to different model tiers based on complexity and cost. It's the single most impactful cost-reduction pattern in production AI systems. Most outsourced builds skip it because it's harder to architect than wiring everything to one model. The result: systems that cost 10-20x more than they need to.
The choice between fine-tuning a smaller model and engineering better prompts for a larger one determines whether your AI system costs $500/month or $15,000/month in production. Most outsourced development teams default to the expensive model with basic prompts because fine-tuning requires production ML expertise they don't have.
Claude Opus costs $15 per million input tokens. Haiku costs $0.25. For extraction, classification, and parsing tasks, Haiku matches Opus on accuracy. If your development team used Opus for everything, they didn't make an engineering decision — they skipped one.
Most AI systems in production cost 5-10x more than they need to — not because AI is expensive, but because the development team architected the system wrong. They used one expensive model for every task instead of routing tasks to the right model. That's not an AI cost problem. That's an engineering problem.
Standard marketing automation platforms handle campaign sequences and basic lead scoring. Custom AI is needed when the scoring model requires data from outside the platform, triggers need to fire from internal system events, or personalisation logic exceeds what the platform can express.
Adding AI to existing production software follows four distinct integration patterns — each with different engineering requirements, costs, and risks. Here's how to choose the right pattern and avoid the common failure modes.
