AI Consulting, Auditing and Solutions
LinkedIn's customer-service team deployed a knowledge-graph-augmented retrieval system for about six months, and the reported result was a 28.6% reduction in median per-issue resolution time. The same paper reported a 77.6% improvement over its baseline in mean reciprocal rank, plus a 0.32 BLEU improvement, showing that KG-RAG can move beyond a lab concept into an […]
You're probably sitting on the same problem right now. The agent works in demos, the team trusts the prompt, and someone has already connected it to a CRM, a ticketing tool, or a browser workflow that can do damage. That's where agent guardrails stop being a nice-to-have and become the difference between a useful agent […]
A domain specific LLM isn't automatically a better business tool because it has been fine-tuned on proprietary data. That advice is incomplete, and it sends founders toward expensive training work before they can answer a more important question: how will you prove the system is reliable on the tasks that matter? The strongest implementations treat […]
Most advice about multi agent architecture starts in the wrong place. It treats more agents as an automatic upgrade, when the central question is whether your workflow can survive the coordination overhead without turning small mistakes into expensive ones. In production, the divide is simple: parallelizable work benefits, sequential work often suffers, and the architecture […]
You're probably living one of two versions of the same problem right now. Either your team has already built a chatbot and watched it answer a policy question with confidence and no clue, or you're still deciding whether it's worth connecting your private documents to an LLM at all. That gap is exactly where a […]
You've got the demo. The notebook scores look strong. Procurement still hasn't signed off, the data team is cleaning edge cases by hand, and every meeting ends with the same sentence, “We just need a little more time.” That's the normal failure mode for custom AI model development. The model was never the hard part. […]
Most advice on generative AI workflows starts in the wrong place. It starts with the prompt, as if better wording alone will turn a model into a reliable business system. That's backwards. The question is which steps the model should touch, which steps it should never own, and how every remaining step gets audited when […]
You've probably seen this movie already. A founder demos a custom LLM agent that answers questions, pulls from internal docs, and looks sharp in a board meeting. Then real users show up, the tool calls get messy, the model starts wandering, and the first week of production turns into a hunt for silent failures and […]
Your AI proof of concept worked, and that's exactly why the conversation changed. The product team wants speed, the security team wants boundaries, and legal won't bless a public API for sensitive data unless the controls are airtight. That's the point where the best private LLM stops being a model-picking exercise and becomes an architecture […]
Your team already shipped a generative AI pilot. People were excited for two weeks. Then the complaints started. The legal team says the model misses the meaning of routine clauses. Operations says the outputs look polished but drift from the exact format they need. Compliance says they can't trust answers on edge cases. Product wants […]
