When businesses want AI that knows their specific content — their products, their policies, their processes — two technical approaches come up repeatedly: Retrieval-Augmented Generation (RAG) and fine-tuning. Both achieve the goal of making AI more relevant to your specific business context, but through fundamentally different mechanisms, at different costs, and with different strengths. This … Read more
Prompts are the instructions that make AI systems work. As businesses build more AI-powered workflows, prompts accumulate: system prompts for customer service bots, extraction prompts for document processing pipelines, generation prompts for content workflows, classification prompts for triage systems. Without version control, these prompts are scattered across codebases, Notion pages, and team members’ memories. When … Read more
Temperature and top-p are the two most commonly referenced AI model parameters after model selection itself, and they are among the most commonly misunderstood. Many business users either ignore them entirely (accepting defaults) or adjust them based on vague intuitions about what they mean. Understanding what these parameters actually control — and how to set … Read more
ReAct is a prompting framework that combines reasoning and acting in an interleaved loop — the model thinks about what to do, takes an action (using a tool), observes the result, thinks about what the result means, takes the next action, and continues until the task is complete. This pattern was formalised in a 2022 … Read more
As businesses mature their AI usage, they typically accumulate multiple models across multiple providers: GPT-4o for complex reasoning, Claude Haiku for fast classification, Mistral for cost-sensitive batch work, a fine-tuned model for a specific domain. Without an orchestration layer, managing this stack is chaotic: different API keys scattered across codebases, inconsistent error handling, no unified … Read more
Most business databases contain far more insight than the reports and dashboards that are regularly produced from them. AI tools that can query your databases in plain English — asking questions like “which customers haven’t ordered in 90 days?” or “which products have declining sales in the North region?” — unlock this latent intelligence for … Read more
Phone calls remain one of the highest-value customer touchpoints in many businesses — and one of the most labour-intensive to handle at scale. AI-powered calling tools have matured to the point where they can handle inbound calls with natural conversation, qualify outbound leads through multi-turn dialogue, book appointments, answer product questions, and escalate to humans … Read more
AI generates structured insights — classifications, extractions, scores, summaries — that are only valuable if they end up where your business can act on them. When AI outputs flow into your data warehouse automatically, they become part of your reporting, your analytics, and your decision-making. When they stay in a chat interface or a note, … Read more
Shopify is the commerce platform for hundreds of thousands of small businesses, and it has become one of the richest environments for AI automation. Product listings, abandoned cart emails, customer support responses, post-purchase sequences, inventory alerts — the recurring operational tasks of running a Shopify store are exactly the kind of repetitive, structured work that … Read more
FinOps — Financial Operations — is the practice of bringing financial accountability to cloud infrastructure spending: making costs visible, assigning them to the teams and products that generate them, and creating a culture where engineering, product, and finance collaborate on optimising spend. The same practices that transformed cloud cost management for AWS and Azure are … Read more