PromptQL

Enterprise AI's biggest bottleneck isn't data or models. It's the missing system for creating and evolving shared context. Things like:  • "Active Customer" was redefined in Q2, but only mentioned in a RevOps sync  • Churned customers get reactivated after 90 days, not immediately  • EMEA revenue is reported differently due to a legacy contract That knowledge lives in people's heads, Slack threads, and long email chains. There was never a natural moment or place to capture it. To improve AI, we need a scalable way to capture this context as it's created, before it disappears. Benzinga covered how we're tackling this at PromptQL. Link in the comments. #EnterpriseAI #AccurateAI #DataAnalytics #AIAnalysts #KnowledgeSilos

Are dashboards dead? Are semantic layers obsolete? Do we still need a central data warehouse? AI is forcing data and analytics leaders to rethink the entire architecture. The modern data stack was built to support decision-making. It did so by solving data access, while leaving understanding to humans. But in an era of AI-driven analytics, this architecture is actually slowing decisions down. As AI becomes the primary analyst, leaders need to rethink a few fundamentals: • Treat centralization as optional, not mandatory • Shift semantics from static definitions to living context • Move dashboards from the starting point to the output Full Forbes article linked below👇

The "Modern Data Stack" was built for a world where we had time to wait. We’ve spent a decade perfecting the ETL-to-Dashboard pipeline. It’s great for looking at last month’s performance to plan for the next one, but it’s a massive bottleneck for teams that need to move in real-time. If you’re managing inventory spikes or dynamic pricing in 2026, a "fresh" dashboard from two hours ago is already too late. I wrote about this for Forbes because we are finally at a place where we *can head toward a world of on-demand analysis* that runs at the pace of the business, not the pace of the data pipeline. The traditional architecture has three major friction points: 1. The Warehouse Latency: Forcing every query through a central warehouse creates a lag. When a decision depends on minute-level changes, waiting for an ETL pipeline to clear is a direct cost. 2. Static Semantics: Centralized models prioritize consistency over responsiveness. Business definitions shift faster than you can update a standalone layer, and definitions quickly drift from how the business is actually being run. 3. The Dashboard Dead-end: Dashboards show what happened, but they don't help you investigate why. The actual reasoning usually happens in meetings or Slack threads, completely disconnected from the tools. When AI becomes the primary analyst, the focus shifts toward decision-led systems: • Centralization becomes optional when speed matters --> query operational sources directly • Semantics need to capture context and reasoning, not just SQL definitions • Dashboards become summaries of investigation, not the starting point The way I see it, the shift to on-demand analysis is the "cloud moment" for operations. It removes the provisioning constraints on insights so teams can finally move as fast as the markets they operate in. Full Forbes breakdown in the comments.

Semantic layer promised to be AI's foundation, but it can't keep pace with your evolving business. Discover why wikis are the future of accurate enterprise AI. Read full article → https://lnkd.in/d4MR_W92

Behind every win was a team that showed up everywhere, from San Francisco streets to AWS re:Invent to dinners with AI leaders. We didn't just talk about accurate AI, we built it. And we shipped. Significant innovation. Multiplayer capabilities that let teams collaborate on AI accuracy in ways that weren't possible before. We're building the platform that makes trustworthy, production-grade AI accessible to everyone. 𝐓𝐨 𝐨𝐮𝐫 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬: You challenged us to build better. Thank you. 𝐓𝐨 𝐨𝐮𝐫 𝐚𝐦𝐚𝐳𝐢𝐧𝐠 𝐭𝐞𝐚𝐦: The work was hard, but we had a blast because we believed in what we were building. 𝐓𝐨 𝐨𝐮𝐫 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: Your insights shaped where we're headed. 2025 proved that accurate AI is real. 2026? We're making it inevitable 🚀 Happy New Year from PromptQL. Here's to high hopes and higher impact 🎉

𝗪𝗲’𝗿𝗲 𝗹𝗶𝘃𝗲 𝗮𝘁 𝗤𝗖𝗼𝗻 𝗡𝗲𝘄 𝗬𝗼𝗿𝗸 🎯 Adam Malone (Director, Forward Deployed Engineering) will take the stage tomorrow to talk about how organizations are tackling AI’s “confidently wrong” problem. “𝗣𝗲𝗼𝗽𝗹𝗲, 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 & 𝗽𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆: 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗔𝗜 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻” He’ll dig into what most AI demos conveniently skip: how to deliver real accuracy in production, at scale. ⏰  Dec 17, 2:30 PM ET📍The New York Academy of Medicine If you’re building AI for production, not just demos, this is your stop. #QconNY #LiveNow #AccurateAI #ProductionAI #CustomAI

The fix for “confidently wrong” AI is coming to New York. Everyone’s tired of AI that lies with total confidence – especially in “talk to your data” systems where accuracy is non-negotiable. Join Adam Malone (Director, Forward Deployed Engineering) at QCon New York on December 17 as he dives into how to scale AI accuracy through continuous learning and real human collaboration. If you're building AI for production, not just demos, then come see: → Why semantic layers aren’t enough for AI → How continual learning unlocks accuracy at scale → What good human-in-the-loop actually looks like in production 📍 The New York Academy of Medicine  ⏰ Dec 17 | 2:30-3:20 PM #QConNewYork #ProductionAI #AccurateAI #EnterpriseAI #AIAnalyst

That's a wrap on last week's AWS re:Invent 2025 – and it was our best one yet. Booth #1733 had an incredible buzz all week. With nonstop live demos of high-trust AI analysts, myth-busting sessions on data readiness, and thoughtful conversations on tackling the "confidently wrong" problem, the energy never let up. To everyone who stopped by for the temporary tattoos (and lasting AI accuracy) – thank you for the incredible conversations. You reminded us why we do this work. Missed the demo? Don't worry – the show may be over, but we're still here. Reach out, and we'll happily walk you through how PromptQL delivers AI accuracy at enterprise scale! 🗓️ Get in touch: https://lnkd.in/ehXAcjp7 #AWSreInvent #DataAnalytics #AI #AIAnalysts #AccurateAI

Fast Company just put PromptQL in the same conversation as Amazon’s 10-year bet on automated reasoning to solve AI hallucinations. Good company to be in. Better problem to be solving. Because the article spells out what we’ve been saying for a while: AI deployments have a confidently wrong problem, and solving it is the key to deploying AI reliably at scale. -- The Technical Problem: Models can’t tell you when they’re wrong. That’s the core issue. AI has the frozen-brain problem humans don’t - we adjust based on feedback; AI doesn’t learn on the job. It stays confidently wrong because it can’t signal when it’s operating outside its knowledge. And that weakness becomes far more dangerous once AI stops writing and starts doing - executing transactions, updating records, making irreversible decisions. Hallucination shifts from “wrong words” to wrong actions. Amazon’s Byron Cook captures that risk perfectly: “If I let it launch rockets, will it launch rockets when we’re not supposed to?” -- How Companies Are Responding: Different organizations are approaching this in different ways: • Amazon uses formal logic to mathematically prove outputs match policy. • EY uses knowledge graphs. • We (PromptQL) translate business processes into a planning language. Different methods, same recognition: the system - not the model - must ensure agents act correctly when the model itself can’t recognize its own gaps. -- The Real Race: The race isn’t to build bigger or “smarter” LLMs. It’s to build better translation layers between messy reality and probabilistic AI. That’s what determines whether your agents hallucinate in production - or actually ship. The companies investing in this now won’t just cut error rates. They’ll define what enterprise AI infrastructure looks like for the next decade. If we want AI to move from chatting to deciding, the infrastructure has to support accuracy at scale. >> Full article link in the comments.

Speaking today at re:Invent: Fixing AI's "Confidently Wrong" problem. Most AI on data today follows a dangerous curve: initial excitement followed by a steep drop in adoption as trust erodes. To scale accuracy in the enterprise, we need systems that can interrogate their own failures and absorb tribal knowledge - just like a human coworker would. That is the focus of my talk this afternoon. I’ll be demonstrating how we are architecting for continuous learning at PromptQL, ensuring AI actually improves with usage rather than degrading. Session Details: Topic: Fixing AI's Confidently Wrong problem in the enterprise Time: Today (Dec 3) | 2:00 PM Location: Venetian Las Vegas | Partner Experience Pavilion Room: Level 2, Hall B, Expo, Theatre 4 If you’re at the event, let’s connect. I’ll be at the talk or at Booth #1733 afterward #reInvent2025