When we think of making progress on the intelligence front in AI, we typically think of producing models that have higher “peak intelligence”. But in the Customer Support space, and many other similar applications, it is not higher peak intelligence that we most need in order to build generally able AI systems anymore. Instead, “intelligence density” is how we can efficiently drive immediate and meaningful progress in real-world, latency-constrained AI applications, today.| /research
Building reliable large language model (LLM) inference is still an emerging discipline. Although the field has matured considerably in recent years, we are far from the level of dependability seen in industry-standard services such as Amazon…| /research
We step through the optimisation process required to make an Open Source reasoning model fast enough to use as a component of an interactive user application.| /research
On May 21st, we launched Insights, an AI-powered suite of products that delivers real-time visibility into your entire customer experience. As part of Insights, we built ‘Suggestions’ to tackle help improve knowledge center documentation and Fin’s… The post A Causal Inference Approach to Measuring the Impact of Improved RAG Content appeared first on /research.| /research
Using customer service conversations as a source of truth is a very tempting idea but the sheer volume of the conversations may flood AI Specialists. By experimenting with various architectures, we have found a solution that…| /research
Conventional wisdom says speed matters in software and that fast is always better. But in testing our AI agent, we found that slowing down might actually make it feel smarter.| /research