Our thoughts
AI Adoption ROI: The Metrics Most Firms Get Wrong
Only 18% of professional services firms track AI adoption ROI. Here's how the real return gets measured, and what unmanaged AI risk costs the other 82%.
RAG Development Services: What Actually Gets Built
More than 1,200 court cases now involve AI-fabricated citations. Most of what gets sold as RAG development doesn't fix the problem behind that number. Here's what actually does.
How to Choose a Custom AI Agent Development Company
Kirkland & Ellis is spending $500 million to build its own AI platform. Most firms can't do that, here's how to pick the company that builds it for you instead.
AI Agents for Enterprise: What Actually Works Inside Document-Heavy Firms
Most "AI agents for enterprise" are rebranded chatbots. Here's what genuine agents do inside law firms, RIAs, and investment banks, and how to tell the difference.
AI Adoption Readiness for Professional Services Firms
56% of professional services firms have adopted AI. Only 24% reach production. Here's the gap in between, and how to close it.
How to Choose an AI Adoption Consulting Partner
Most AI consulting partners look the same in a pitch deck. Here's what actually separates the 20% that deliver from the 80% that don't.
Why Your Company Needs a Custom AI Operating System
Most enterprises run hundreds of disconnected SaaS tools, and bolting AI features onto each one doesn't fix that. Here's why more companies are building a custom AI operating system instead, one layer that connects what you already have, built around how your business actually runs.
Your Business Card Should Live in Your Wallet. And It's Free: Wallet Cards Club
Paper cards get lost, go stale, and end up in the trash. Your contact information should live somewhere people actually look. WalletCards lets you create a free digital business card for Apple Wallet and Google Wallet in under two minutes. No app, no subscription, no expiration.
Why 88% of Companies Use AI and Only 6% See Results From It
88% of organizations use AI. Only 6% see real results. The gap isn't a technology problem, it's a workflow problem. Here's what the data from McKinsey, Deloitte, and Stanford shows about why most AI implementations stall before they produce anything measurable.
Most Enterprise Teams Already Have AI. They Just Don't Have Anything to Show for It
Most enterprise teams already have AI tools. The problem is that none of those tools know how their operation actually works. This is why most AI rollouts produce reports, not results.
Why Enterprise AI Adoption Fails in 2026 (And What the 8.6% Who Made It to Production Did Differently)
79% of enterprises face AI adoption challenges in 2026. Most stall before production. Here's what's actually blocking scale — and what separates the companies that ship from the ones that pilot forever.
Vibe Coding Technical Debt: What Happens to Your Codebase at Month 6
Vibe coding ships fast. But 63% of devs spend more time debugging AI-generated code than writing it. Here's what the debt looks like — and when to bring in professionals.
How Much Does It Cost to Build an AI Product? A Real Breakdown for 2026
Most cost breakdowns for AI products are useless without context. A $50K project and a $500K project can both be called "an AI product", and both numbers can be accurate. Here's what actually determines where your build lands, and where companies typically lose budget before they know it.
Why Most AI Products Fail After Launch, And What Production-Ready Actually Means
80% of AI projects fail after the demo. Here's what separates products that survive production from prototypes that don't — and the specific decisions that make the difference.
How We Built an AI Sprint Planning Tool That Replaced Standups
How Imaginary Space built Flor.work, an AI sprint planning agent that generates sprint plans in seconds, assigns tasks by skill and capacity, and keeps engineering teams in sync through Slack. 40% less meeting time. 3x faster planning. Here's what we built, what we cut, and what we'd do differently.
How to Build an AI Product That Pulls From Multiple Data Sources (Without Breaking in Production)
Most AI products that aggregate external data don't fail because of the model. They fail because of the architecture decisions nobody made in week one. Here's what getting it right actually looks like.
Open Source LLM vs. API: How to Make the Build-vs-Buy Decision for Your AI Product
Open source LLMs now match frontier APIs on most benchmarks. Here's the framework for deciding which approach is right for your AI product and when the math changes.
AI Meeting Automation: Why Most Workflows Still Need a Human in the Middle
I built an agent last weekend that runs my entire post-meeting workflow without any input from me. No prompt. No copy-paste. No checking if it actually did the thing.
