AI is the area where we’ve watched the most money get spent on the least useful work. Strategy decks promising “AI transformation.” Pilot projects that never reach production. Chatbots that frustrate customers and require human escalation for every meaningful question. We do AI integration the way we do everything else: starting from a real business problem, with a measurable outcome, and a clear way to roll it back if it underperforms.
Practical, not performative
The clearest signal of a serious AI engagement is how unglamorous the use case sounds. Routing customer-support tickets to the right agent. Extracting structured data from PDF invoices. Generating draft responses to common email patterns. Predicting which customers are likely to churn. These don’t make for impressive press releases, but they save hours per week and pay for themselves inside a quarter. We optimise for those.
Built on Claude, GPT, or your own model
We’re stack-agnostic. Most of our deployments use Anthropic’s Claude or OpenAI’s GPT models because the quality is excellent and the developer ergonomics are strong. But we’ve also built on open-source models hosted on customer infrastructure when data sensitivity requires it. The right model for your project is the one that meets your accuracy, latency, cost, and privacy requirements simultaneously. That choice gets made at the architecture phase, not assumed.
Evaluation and governance, from day one
The thing nobody talks about in AI integration is what happens when the model is wrong — and it will be wrong, sometimes, no matter what model you choose. The systems we build assume this from day one. Confidence thresholds. Human-in-the-loop checkpoints. Audit logs. Evaluation pipelines that flag drift before customers notice. The goal is intelligent infrastructure, not magical thinking.
Internal tooling first, customer-facing second
The most leverage in AI deployment usually sits inside your own organisation, not in customer-facing features. An internal AI assistant that helps your customer-success team find the right answer in five seconds instead of fifteen minutes saves more time than any customer-facing chatbot. We typically recommend starting there — both because the ROI is faster, and because the lessons you learn from internal deployment make any later customer-facing deployment dramatically more successful.