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Great post Aron. The entire team has been talking about this for awhile now, but I couldn't agree more. This is WHAT you need to be talking to providers about. If you are not getting the right answers come talk to LevelAi.

Today we introduce Level AI Latitude: seven models, each built for a specific job in customer experience. Trained on more than one billion customer interactions. Running entirely on our own infrastructure. This is our operating standard for enterprise AI. Read more: https://lnkd.in/g6j5wnsQ

The industry keeps asking "which LLM is best for CX?" We think that's the wrong question. We have launched Latitude at Level AI — a suite of seven purpose-built models, each engineered for a single CX job: transcription, redaction, intent detection, summarization, inferred CSAT, automated QA, and Voice of Customer. Here's the thinking. No single model can be optimized for every CX task, because the tasks themselves are measured differently: → A transcription model lives or dies on word-level accuracy through background noise and crosstalk. → A redaction model is only as good as the sensitive data it intercepts before anything leaves your environment. → A QA model earns trust only when a supervisor can trace every score back to concrete evidence in the conversation. Different success criteria. Different latency requirements. Different ways to fail. So we built a specialist for each. The result: on real production CX workloads, Latitude matches frontier LLM accuracy at up to 49x lower serving cost. We've published the full benchmarks and evaluation framework in a whitepaper — link in the comments. Would love to hear how others are thinking about specialist vs. generalist models in production.

If you read back into my post history you'll see me talk about Small Language Models a lot. The Market has finally caught onto the idea that the best path forward is fine tuned purpose build models for each specific task in conjunction with LLMs where it matters. Level AI has been running these small models for years as a pioneer in SLM first architecture. We feel like its time to give the hero of our product a name Meet Latitude - 4X Faster - Up to 49X more cost effect - Just as Accurate as using a Large Language Model - All on Level AI Owned Infrastructure If you're not asking whats under the hood of your CX Platform, it's about time to start

In their latest Perspective piece on Entirely in View, Tobias Ackermann and Van Diamandakis argue that the agentic AI race is focusing on the wrong questions. As the number of agents deployed by enterprises continues to scale alongside tool bloat; as the lack of interoperability of these agents causes employees more work than it reduces, there now comes a call for a centralized operating system that *coordinates* AI agents, rather than simply adding more. Their perspective is that such an operating system running beneath the agents: a shared layer that can connect decisions across the stack, retain what the organization learns, and allow orchestration over what happens next, is the missing piece of the modern tech stack puzzle. Read the full Perspective here: https://lnkd.in/dvNnF4gk

𝗠𝗮𝗸𝗶𝗻𝗴 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 A metric that lives in a dashboard is not operational. A metric that stops a deployment is. The gap between measuring something and acting on it is where most AI programs stall. Making measurement operational means wiring it into the process — not reporting on it after the fact. Check it out: https://lnkd.in/g2iKq7wm

Find out how leading ocean carriers are achieving a 40% reduction in admin costs through AI forecasting, speeding up document processing by 50%, and automating 70% of customer queries. https://okt.to/Nd7o9Y

What if your compliance team could reach a baseline score in hours, not weeks? NISTcompliance․ai brings together secure, Government-hosted AI, shared evidence and automated workflows to simplify RMF and FISMA compliance. Access the Tech Spotlight here: https://ow.ly/gElu30sXrcS

🚀 Built the Guide I Wish Existed for AI Tool Integration "The Self-Hosted AI Agent Playbook" — n8n + local LLM automation, Perplexity + Claude chains, production pipelines flat-fee. → https://lnkd.in/ey4ZtpjY