AI agents for business are the current version of a familiar story: a demo that works beautifully in ten minutes, followed by six months of trying to make it work reliably enough to leave unattended. The gap between those two states is where almost all the budget goes, and almost none of the marketing material...
AI Integration
Articles, guides and tutorials on integrating AI into software products, covering APIs, language models and practical AI implementation strategies.
The fine-tuning vs RAG question usually arrives as a statement: “we need to fine-tune a model on our data.” It is one of the most expensive sentences in enterprise AI, and it is usually wrong. Not always, but usually. The request nearly always means one of two things: the model does not know about our business, or the...
The case for moving off OpenAI got considerably stronger during 2026. Open-weight models reached a level where the quality gap on ordinary production work became narrow, published pricing undercut the frontier providers, and the weights themselves became downloadable, which turns a supplier relationship into an option....
Self-hosting Kimi K3 became technically possible on 27 July 2026, when Moonshot AI published the weights for a 2.8-trillion-parameter model alongside production inference support. A great many organisations read that news and concluded they could now run frontier-class reasoning on their own hardware and stop paying...
The Kimi K3 API arrived with an unusual combination behind it: frontier-adjacent benchmark results, aggressive pricing, and downloadable weights. Moonshot AI published those weights on 27 July 2026, which makes K3 the largest openly available model released so far and the first time a model at this scale has been...
An OpenAI API integration looks trivial in a prototype and turns out to be an engineering project in production. The proof of concept takes an afternoon: install the client library, paste a key, send a prompt, get a useful answer back. Then someone asks what happens when the request times out, who pays when a customer...
Determining the true AI integration cost is a major financial step for UK businesses looking to deploy Large Language Models (LLMs) in 2026. Integrating AI into software applications automates customer service pipelines, increases productivity, and unlocks conversational data insights. However, budgeting for these...
Reducing LLM latency is one of the most critical challenges for engineers building responsive AI applications. While Large Language Models (LLMs) keep growing in capability, their token-by-token generation can create frustrating bottlenecks for end users, and long wait times lead directly to lower engagement and...
Building low-latency audio pipelines with the OpenAI Realtime API lets developers launch human-like conversational voice agents in production. Traditionally, building a voice interface meant chaining three separate model layers: automatic speech recognition (ASR), a text-based LLM logic layer, and text-to-speech (TTS)...
Choosing between the Claude Opus 4.8 vs OpenAI GPT-5 developer APIs is one of the first critical decisions for teams building enterprise AI applications in 2026. As organisations integrate Large Language Models (LLMs) into production codebases, the model provider you pick dictates your platform’s capabilities, latency...