Conversations with insurers, banks, law firms, the public sector and many more changed how we think about getting AI projects off the ground.
What a Year of AI Conversations Taught Us
Over the past year, we have spoken with companies at very different stages of using AI. Three observations stayed with us: similar businesses start from very different places, nobody is short of ideas, and showing something early changes the conversation.
Document processing done right
Most people think document processing is simply uploading a PDF to an LLM. Step the same file through a single model call and through a staged pipeline, and watch where the cost, the reliability and the audit trail actually come from.
Local Language Models are back on top
Companies with strict data privacy requirements used to be shut out of capable general-purpose AI. Open-weight models that run on your own infrastructure have nearly caught up, and here is what each capability tier asks for in hardware.
How to Invest in AI Without Regretting It Later
Every new model makes some of today’s AI work unnecessary. Here is how to build systems whose business value survives — and grows — when the technology improves, instead of having to start over.
Faster, or Different?
When should AI make your existing process faster, and when should it redesign that process entirely? A practical guide to telling the difference, worked through the example of incoming customer requests.