Home > Insights
Knowledge Hub

Insights

Specific write-ups on the decisions that decide whether AI and enterprise software systems survive production — written from client delivery, not from a content calendar.

Engineering notes on AI and software delivery

Short, specific write-ups on the decisions that determine whether an AI system survives contact with production: retrieval versus fine-tuning, latency budgets for voice, where guardrails belong, and how to modernize a system that cannot go down. Every article states the trade-off and the rule we apply, not a list of features.

Published

📚RAG vs Fine-Tuning for Enterprise AI

When retrieval is enough, when fine-tuning actually earns its cost, and how to tell the difference on your own data before spending a budget.

Read the article →

🎤How to Build a Production Voice AI Agent

Latency budgets, barge-in, escalation paths, evaluation sets and telephony integration — the engineering that separates a demo from a phone line customers accept.

Read the article →

In progress

Want one of these sooner? Tell us which problem you are solving and we will prioritise it.

Where these articles come from

Every piece is drawn from delivery work: our case studies across healthcare, manufacturing, financial services, retail, logistics, insurance and telecom, plus our own products — iCare, Callora and TaskEdge.

Frequently asked questions

Because the questions that decide a project are technical: where the data lives, what happens when a model is wrong, how a system behaves under load. We write down what we actually do so you can judge the approach before you talk to us.
Yes, that is the point. Each piece lists the decision rules we use, so you can ask any vendor the same questions and compare answers. Nothing here is a proprietary method.
Talk to an engineer about your project →