CleeAI

The dirty secret about enterprise AI? Most of it can't tell your CFO from your coffee machine. Enterprises are deploying LLMs into high-stakes workflows and discovering the same problem: the models are capable, but there's nothing above them controlling which queries reach them, how often, or at what cost. Here's what that looks like in practice: → Every query hits the most expensive model available, whether it needs to or not → Inference costs compound with every user, every workflow, every department that adopts → Your AI generates outputs that look correct but can't be audited or governed → Every business rule change requires weeks of retraining This isn't a model problem. It's an infrastructure problem. LLMs are expensive to run at scale. Without a control plane sitting above them, routing queries intelligently, redirecting simple requests away from frontier models, and governing outputs - you are paying full inference cost for every interaction regardless of complexity. CleeAI's LKM™ is that control plane. It sits above your existing LLM stack, routes queries to the right model at the right cost, enforces accuracy, and governs outputs. Our PoC measured a 69% reduction in inference compute costs. The models don't change. The infrastructure above them does. Your AI should be a business asset, not a compounding expense. #EnterpriseAI #AIInfrastructure #LLM #LKM #InferenceCost

Next week, our CEO Annika Rao-Monari. takes the stage at the London Quant Group's autumn seminar at Cambridge to explore "𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 - 𝗳𝗿𝗼𝗺 𝗦𝘆𝗻𝘁𝗮𝘅 𝘁𝗼 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰𝘀." She'll be speaking alongside an impressive lineup including BlackRock's Chief Risk Officer Ed Fishwick, Bloomberg's Jose Menchero, PhD, CFA & Naima Hammoud, and University of Oxford's Desi R. Ivanova. While the finance world debates whether AI will replace analysts or just assist them, Annika will be discussing something far more fundamental: how we're moving beyond language-based AI to systems that actually understand meaning. The shift from syntax (what words say) to semantics (what they mean) isn't just academic theory - it's the difference between AI that guesses and AI that knows. At CleeAI, we've been building this future with our Large Knowledge Model (LKM) - moving past the limitations of traditional language models to create AI that reasons with structured knowledge rather than predicting text patterns. For the quants, risk managers, and portfolio strategists attending: this isn't about replacing your expertise. It's about amplifying it with AI that understands context, compliance, and the nuanced logic that drives financial decisions. The future of AI in finance isn't just faster calculations - it's deeper understanding. 𝙏𝙝𝙚 𝙚𝙫𝙚𝙣𝙩 𝙥𝙖𝙜𝙚 𝘜𝘙𝘓 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵 𝘣𝘦𝘭𝘰𝘸.👇 #AI #Finance #QuantFinance #MachineLearning #FinTech #Innovation

AI sales agents are becoming a standard talking point in enterprise sales. The reality is more complex than the promise, and more expensive. Most deployments fail the same way: every query routes to the most capable and most costly model available, whether the task warrants it or not. There's no intelligence above the model managing cost, enforcing consistency, or governing what gets returned. Inference costs scale with adoption. Accuracy doesn't. Our evaluation across technology, legal, and financial services found the same pattern consistently: → LLM-only systems struggle with context, consistency, and compliance → Inference costs compound without a routing layer controlling query distribution → A structured control plane above the model enables transparent decisions, lower infrastructure cost, and results your team can actually rely on The future of enterprise AI isn't more capable models. It's the infrastructure layer that makes those models accurate, auditable, and cost-efficient to run at scale. CleeAI's LKM™ delivered 69% inference cost reduction and 87.53% latency reduction in PoC. The models stayed the same. The control plane above them changed everything. #EnterpriseAI #AIInfrastructure #LKM #InferenceCost #AIGovernance

Your sales AI shows 94% accuracy. Your quarterly forecast just missed by £50K. The disconnect isn't in your data—it's in what your AI actually understands about your business. Most enterprise sales systems excel at pattern recognition. They'll tell you a deal looks promising because it matches historical winners: multiple touchpoints ✓, positive sentiment ✓, similar company profile ✓. What they miss: The prospect's parent company just froze hiring. Their IT budget shifted to cybersecurity after a breach. Their decision-maker retires in three weeks. Statistical accuracy without business context is expensive theatre. The solution isn't more data or better algorithms. It's AI that understands your actual business logic—budget cycles, approval hierarchies, competitive dynamics, strategic priorities. When your sales intelligence can reason about why deals happen (not just predict they will), your pipeline becomes genuinely reliable. Your forecasts become planning tools, not quarterly surprises. The competitive advantage belongs to teams that move beyond pattern matching to business understanding. Are you measuring AI accuracy or business relevancy? Apply for beta access: https://bit.ly/41kNBMA 𝘍𝘶𝘭𝘭 𝘢𝘳𝘵𝘪𝘤𝘭𝘦 𝘜𝘙𝘓 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵 𝘣𝘦𝘭𝘰𝘸.👇

Your compliance team has been too polite to tell you this. That "95% accurate" AI tool they're using? It's making their job harder — and it's costing more than you think. McKinsey's March 2025 analysis: Financial services firms face $35.2 million in penalties per AI compliance failure. That's the governance cost. But there's an infrastructure cost running underneath it every day. LLMs operating without a control plane route every compliance query — simple or complex, routine or critical — to the same expensive frontier model. There's no intelligence above the model deciding what actually needs full inference and what doesn't. The result is accuracy you can't audit and inference bills that don't reflect the value being delivered. What compliance environments need isn't a more accurate model. It's a control plane that enforces accuracy, routes queries intelligently to reduce inference cost, and produces outputs that can be audited end to end. CleeAI's LKM™ sits above your LLM stack and does exactly that — validated at 69% inference cost reduction and ≥99% routing accuracy. The model doesn't change. The infrastructure governing it does. If your compliance team is spending more time correcting AI outputs than using them, the stack is missing a layer. #EnterpriseAI #AIGovernance #Compliance #LKM #InferenceCost

Running LLMs at scale is expensive. Most enterprises don't realise how expensive until the invoice arrives. Every query. Every user. Every department that adopts. The inference cost compounds, and without anything above the model managing it, you're paying full price for every single interaction regardless of whether it needed a frontier model or not. Here's what that looks like in practice: → A simple query that could cost fractions of a cent gets routed to a $0.06/1K token frontier model → Multiply that across thousands of daily interactions → Add latency that slows every workflow it touches → Then try to audit any of it The enterprises winning with AI aren't running bigger models. They're running smarter infrastructure above them. CleeAI's LKM™ sits above your existing LLM stack as a control plane — routing queries to the right model at the right cost, enforcing accuracy, and governing every output. The numbers from our PoC: → 69% reduction in inference compute costs → 87.53% reduction in query latency → 70.81% correctness on Humanity's Last Exam vs ~36% for frontier models alone → ≥99% routing accuracy Same models. Smarter infrastructure above them. #AIInfrastructure #LKM #InferenceCost #EnterpriseAI #LLM Read the full breakdown: https://bit.ly/4m5MdpM

Your AI infrastructure bill is growing faster than the value it's delivering. That's not a model problem. It's a stack problem. Most enterprises are running LLMs the same way, every query hitting the most expensive model available, no intelligence above it deciding what actually needs full inference, no layer enforcing accuracy or governing outputs. Prompt engineering was the first attempt to fix this. It doesn't scale. Handcrafted prompts break under real enterprise conditions — rule changes, edge cases, compliance requirements, organizational complexity. What scales is infrastructure. A control plane above your LLM stack that: → Routes queries intelligently, directing simple tasks away from expensive frontier models → Enforces accuracy before outputs reach your users → Governs every response against your compliance and policy requirements → Does all of this without replacing the models you already run CleeAI's LKM™ is that layer. In PoC it delivered 69% inference cost reduction and 87.53% latency reduction - not by changing the models, but by controlling what reaches them and what comes back. The infrastructure era of AI is here. Prompts were never going to get you there. #AIInfrastructure #LKM #EnterpriseAI #InferenceCost #LLM Read how to build an AI stack that won’t break in production: https://bit.ly/43422EL

Most AI projects don't fail because the model was wrong. They fail because there was nothing above the model making it reliable. No routing intelligence. No accuracy enforcement. No governance layer. No cost control. Just raw LLM capability exposed directly to enterprise workflows, and an inference bill that grows with every query whether the output was useful or not. The enterprises that are winning with AI have stopped asking "which model is best?" and started asking "what sits above our models?" That question matters because: → Without intelligent routing, every query pays full inference cost regardless of complexity → Without accuracy enforcement, hallucinations reach your users → Without a governance layer, outputs can't be audited → Without cost control, AI spend scales with adoption but value doesn't CleeAI's LKM™ is the control plane that sits above your existing stack and solves all four. It doesn't replace your models. It makes them accurate, auditable, and cost-efficient to run at scale. 69% inference cost reduction. 87.53% latency reduction. Not by changing the models. By building the right infrastructure above them. #AIInfrastructure #LKM #EnterpriseAI #InferenceCost #AIGovernance

The market is obsessed with better models. Bigger context windows. Faster inference. Higher benchmark scores. Every few weeks a new frontier model claims the top spot. Meanwhile enterprises are quietly dealing with a different problem entirely — one that a better model won't fix. Their AI spend is compounding. Every department that adopts adds to the inference bill. Every query hits the most expensive model available. Every workflow that scales multiplies the cost. And nobody has visibility into whether the outputs being generated are accurate, auditable, or worth what they cost. This isn't a model problem. It's an infrastructure problem. The companies winning with AI right now aren't waiting for a better model. They're building the layer above their existing models - the control plane that routes queries intelligently, enforces accuracy, governs outputs, and controls inference cost before it becomes a boardroom conversation. CleeAI's LKM™ is that layer. 69% inference cost reduction. 87.53% latency reduction. Same models. The right infrastructure above them. If you're still thinking in models, you're solving the wrong problem. #AIInfrastructure #LKM #EnterpriseAI #InferenceCost #FrontierAI If you’re still thinking in prompts and prototypes, you’re already behind. Read the full post here: https://bit.ly/3YBGI8r