LLM.co
Private and custom large language models — the build, the boundaries and the bill. Fine-tuning versus retrieval, running models in your own environment, evaluation you can actually trust, data governance, and the questions to ask before a vendor answers them for you. Each episode takes one decision a team is facing — whether your problem needs a custom model at all, how to evaluate output without fooling yourself, what "private" has to mean contractually — and works it through concretely. Written for engineering and data leaders putting a model into production. Five or six minutes, one idea, no demos. Topics include fine-tuning versus retrie...
Fixed-Scope, Managed Appliance, or Co-Build: Picking the Right Private LLM Contract
With AI pilot failure rates hovering near eighty percent and tens of billions in enterprise spending producing little measurable return, the contract model behind a private LLM deployment deserves far more scrutiny than most buyers give it. This episode of LLM.co examines the three structures dominating the private LLM market today — drawing on the detailed breakdown of private LLM contract models on the LLM.co blog — and explains how each one allocates risk, ownership, and exit rights in ways that can make or break an enterprise AI program.
The episode covers:
Why contract structure is the...EU AI Act Conformity for Self-Hosted LLMs: What Your Evidence File Must Prove
The EU AI Act's December 2027 deadline for high-risk AI systems looks distant on a roadmap — but with notified body queues already growing and harmonised standards still being finalised, the organisations that will be ready are the ones assembling their evidence files now. This episode of LLM.co walks through the full conformity assessment requirements for self-hosted LLMs, cutting through the policy noise to focus on what a market surveillance authority would actually demand to see.
The episode covers the full arc of Article 43 conformity for enterprise LLM deployments — from the legal triggers that push a system into Anne...
Why DeepSeek's China Data Storage Policy Is an Enterprise Red Flag
DeepSeek has earned real attention for its speed and accessibility, but its privacy policy contains a detail that enterprise teams cannot afford to overlook. This episode of LLM.co examines the data storage risks behind DeepSeek's China policy and explains why where an AI platform stores your data matters just as much as what it can do with it.
The conversation covers the full arc of the problem — from the legal mechanics of Chinese data sovereignty to the practical compliance exposure facing organizations in regulated industries:
The core policy clause: DeepSeek explicitly states that user data ma...How Enterprises Are Using Local LLMs for Fraud Detection
Global fraud losses are on track to surpass half a trillion dollars, and the rigid, rules-based detection engines that financial institutions have trusted for years simply can't keep up. This episode of LLM.co explores how enterprises are deploying local LLMs to fight financial fraud — examining why on-premises AI is becoming the competitive edge for compliance-conscious organizations facing increasingly sophisticated threats like synthetic identity schemes, deepfake voice fraud, and sleeper-bot attacks.
The episode unpacks the full case for local large language models in enterprise fraud detection, covering:
Why legacy rule engines are failing: Hard-coded logic can't ad...Debugging Hallucinations in Open Source Models
Hallucinations — confident, fluent, and factually wrong AI outputs — are among the most damaging trust failures in production systems. This episode of LLM.co draws on the full guide to debugging hallucinations in open source models to walk through why these failures happen and, more importantly, how to trace them back to their root causes rather than chasing individual bad outputs.
Language models don't retrieve facts — they predict likely text. Fluency and accuracy are not the same thing, and users routinely mistake a confident tone for a verified answer. Teams working with open source models have real leverage here...
Why AI Projects Fail Organizationally Before They Fail Technically
Technical performance is rarely what kills an AI initiative. This episode of LLM.co examines the organizational and structural breakdowns that derail AI projects long before a model ever has a chance to prove itself — drawing on the full analysis of why AI projects fail organizationally before they fail technically. From how goals get defined to how tools get adopted, the conversation surfaces the patterns that repeat across teams and industries.
The episode walks through the most common organizational failure modes, covering:
Vague goals that invite misalignment — when a project's objective is broad enough for every stak...CAPEX vs OPEX in Open Source AI: Why the Hybrid Wins
When an AI project moves from demo to daily infrastructure, the conversation shifts fast — from model benchmarks to balance sheets. This episode of LLM.co tackles one of the most consequential decisions in any open source AI deployment: how you pay for it. Drawing on the in-depth guide to CAPEX vs. OPEX in open source AI, the episode cuts through the accounting jargon and explains how the choice between owning infrastructure and renting it shapes not just your first invoice, but your team structure, data control, and total costs for years ahead.
Here's what this episode covers:
...