Built This Week

This week we sat down with Jim Olsen, co-founder and CTO of ModelOp, to explore what it takes to get AI into production inside Fortune 500 companies. Building a model is only part of the job. Knowing where it runs, what it costs, who owns it, and whether it meets company policies is where things get complicated. Jim walks us through a live demo of ModelOp, showing how it discovers unregistered enterprise AI, brings it into a central inventory, and uses agents to fill out documentation and flag compliance gaps. His team is turning work that can take months into a process that takes minutes, with humans still in the loop. We also covered why AI spending needs to be measured by business use case, whether an AI-powered pet feeder solves a real problem, and how Jim runs engineering from an off-grid home at 10,200 feet in the Colorado mountains. We cover: • What ModelOp does and why all its clients are Fortune 500 companies • Why enterprise AI projects get stuck between prototypes and production • Tracking token usage and costs by business use case • Shadow AI beyond employees using chatbots • A live demo of discovering and registering enterprise AI • Using AI agents to complete forms and identify compliance gaps • Why model inventories matter when providers change or models retire • The real-world problems that led Jim to co-found ModelOp • Petlibro’s AI-powered feeder and whether everything needs AI • Running an engineering team with solar power and Starlink from an off-grid home If you are building with AI, managing enterprise AI deployments, or trying to understand how governance can accelerate delivery, this episode is for you. Find Jim and ModelOp: Website: https://www.modelop.com New episodes every Friday at BuiltThisWeek.com TIMESTAMPS [00:00] Intro [00:41] Meet Jim Olsen, co-founder and CTO of ModelOp [01:49] What ModelOp does: enterprise AI governance explained [03:15] Tracking AI costs by business use case [04:50] Demo: token usage and cost dashboards [05:41] Automating governance and compliance workflows [06:08] Discovering shadow AI across enterprise platforms [08:49] Bringing a healthcare chatbot into the inventory [09:56] AI agents automate documentation and forms [11:20] Checking policies and flagging compliance gaps [12:05] From months of work to minutes [12:27] The real-world problems behind ModelOp [14:04] Tracking model dependencies and managing changes [15:23] Where to find ModelOp [15:43] News: Petlibro’s AI-powered pet feeder [16:11] Does everything really need AI? [17:20] Automatic feeders, robots, and everyday chores [18:23] Jim’s off-grid setup in the Colorado mountains [19:09] Solar panels, batteries, and working remotely [20:12] Wrap-up #AI #ModelOp #AIGovernance #EnterpriseAI #ShadowAI #AIAgents #Fortune500 #BuildWithAI #BuiltThisWeek

This week we sat down with Rob Williams, co-founder and CTO of Read AI, to dig into what 5 million monthly active users actually looks like at the product and infrastructure level. Most people know Read AI as a meeting note taker. Rob winces every time he hears that. What it really does is capture every conversation across your organization and turn it into intelligence — daily briefings, smart routing, and organizational context that moves itself to where it needs to be.We also covered AI companion apps and the unit economics behind them, the AI adoption divide inside enterprise companies, and Opus 5.5 dropping this week. We cover: What Read AI actually is beyond the meeting note taker label The daily briefing feature — hot and hydrated context before every meeting How smart routing sends meeting data automatically to the right team Why some companies set data retention to 7 days and why that makes sense Navigating the AI adoption divide inside enterprise organizations Rob's vision for information moving itself in three years Why meetings are the biggest investment most companies make and nobody measures Hot Girl Hotline — an AI relationship advice app and why the unit economics work AI as a sounding board vs a solver and why both have value Opus 5.5 dropping and what cheaper intelligence means for builders Token economics of running 5 million monthly active users If you are building with AI, managing a team, or want to understand where enterprise AI is actually heading, this episode is for you. Find Rob and Read AI: Website: readai.com Email: [email protected] LinkedIn: [LINKEDIN LINK — to be added] New episodes every Friday at BuiltThisWeek.com TIMESTAMPS [00:00] Intro [01:06] Meet Rob — co-founder and CTO of Read AI [01:42] What Read AI actually is beyond meeting notes [02:12] Meetings as the biggest investment companies make [02:33] The daily briefing feature — live demo [03:47] Smart routing — data flows where it needs to go [04:57] Unusual use cases and the AI adoption divide inside companies [06:19] 7-day data retention and what it says about fast-moving companies [06:40] 5 million monthly active users and how they got there [07:00] Token economics at scale [10:00] The future — information that moves itself [15:34] What supersedes old context — the data freshness problem [15:55] News — Hot Girl Hotline, AI relationship advice [17:09] Is AI as a companion dangerous or valuable [18:14] AI memory and the sounding board use case [19:37] Unit economics of AI companion products [20:13] Sam uses AI for big personal decisions [21:32] Where to find Rob [21:50] Wrap up #AI #ReadAI #Meetings #Enterprise #FutureOfWork #BuildWithAI #BuiltThisWeek #Productivity #Anthropic #AICompanion

He Ran a Rocket Launch Company. Now He Is Building Autocomplete for Hardware Design. This week we sat down with Sohrab Haghighat, CEO and co-founder of Hestus, to demo the AI layer that sits on top of your existing CAD software and autocompletes your next design move. Rob spent years as an aerospace engineer, ran a rocket launch company, and built Hestus to eliminate the manual clicks that slow hardware engineers down and introduce errors into designs.We also covered Anthropic 5.5, GPT-6, and a new Grok model all dropping in the same week — and why intelligence getting cheaper is good news for everyone building right now. We cover: What Hestus is and how AI autocomplete works inside CAD software Live demo — AI suggesting the next design step before you click Why Hestus plugs into your existing CAD tool instead of replacing it How Hestus learns your preferences within a design session Who the target customer is — mechanical, aerospace, and robotics engineers The gap between a flashy 3D model and one that is actually ready for manufacturing Rob's long term vision — doing for hardware what Claude Code did for software Anthropic 5.5, GPT-6, and Grok all dropping the same week — intelligence is getting cheaper Why LLM improvements do not directly translate to improvements in hardware AI If you are building hardware, working in engineering, or want to understand where AI is going beyond software, this episode is for you. Find Rob and Hestus: Website: hestus.co LinkedIn: linkedin.com/in/sohrabhaghighat Twitter: x.com/s_haghigh New episodes every Friday at BuiltThisWeek.com TIMESTAMPS [00:00] Intro [01:12] Meet Rob — CEO and co-founder of Hestus [01:33] Aerospace engineer, rocket launch company, and the pain that led to Hestus [02:29] Live demo — AI autocomplete inside CAD software [03:50] How Hestus sits on top of your existing CAD tool [04:16] Meeting engineers where they are — no new workflow [05:02] How Hestus learns your preferences within a session [06:32] Available on Autodesk Fusion now, SolidWorks coming soon [07:20] Who is using Hestus — mechanical, aerospace, robotics engineers [08:30] Bottom-up approach — sketch to 3D to manufacturing readiness [10:25] Long term vision — Claude Code for hardware design [11:15] The gap between a flashy model and one ready for manufacturing [12:00] News — Anthropic 5.5, GPT-6, and Grok released same week [13:18] Jordan tests the new Opus — cheaper and competitive with Fable [14:41] Intelligence is getting cheaper — what that means for builders [15:06] Why LLM improvements do not directly improve Hestus [16:08] Where to find Rob and Hestus [16:31] Wrap up #AI #CAD #HardwareDesign #Engineering #Hestus #BuildWithAI #BuiltThisWeek #Anthropic #OpenAI #Aerospace

This week we sat down with Jennifer Dulski, CEO and founder of Rising Team, to demo the AI leadership platform that acts as a personal trainer for your effectiveness at work. Jen spent 10 years at Yahoo, then Google, then Facebook, ran Change.org for five years, and sold her first AI company to Google in 2009. That company now powers the review insights inside Google Maps. She also teaches at Stanford GSB.Rising Team's AI coach RT does not give generic leadership advice. It knows your team. It knows that Tom likes direct feedback and Debbie needs encouragement first. And it helps you practice the conversation before you have it.We also got into why Apple Intelligence took too long, why AI memory still feels creepy to some people, and why the best AI features are the ones you never notice. We cover: What Rising Team is and how the AI coach RT works Why 70 percent of AI transformations fail because of people not technology How RT learns your team individually and personalizes every conversation Why every executive gets a coach and every frontline manager gets nothing Why the engagement survey is dying and what is replacing it in real time The privacy guardrails built into RT and EU AI Act compliance Jen's first AI company in 2009 — DealMap, sold to Google, now inside Google Maps Why Apple Intelligence took so long and whether it matters anymore The AI memory debate — helpful or creepy Why the best AI features are the ones you never have to turn on If you are building with AI, managing a team, or want to understand where workplace AI is heading, this episode is for you. Find Jen and Rising Team: Website: risingteam.com LinkedIn: linkedin.com/in/jdulski New episodes every Friday at BuiltThisWeek.com TIMESTAMPS [00:00] Intro [01:11] Meet Jen — CEO and founder of Rising Team [01:44] From high school teacher to Yahoo, Google, Facebook, and Stanford GSB [02:27] What Rising Team is at the highest level [02:47] Why coaching never scaled past the executive level [03:40] How Rising Team started in 2020 [04:03] DealMap — her first AI company, sold to Google, now inside Google Maps [05:18] How Rising Team evolved from team building software to AI coach [05:55] How RT knows your team individually [06:31] Live demo — RT in action [07:00] Why 70 percent of AI transformations fail because of people [07:24] The science of high performing teams has not changed [08:12] What a session with RT actually looks like [10:00] Real-time anonymized insights for executives [14:21] Why the engagement survey is dying [14:48] Real insights from enterprise customers [15:36] Privacy guardrails and EU AI Act compliance [16:43] News — Apple Intelligence, Siri, and why it took too long [18:28] The AI memory debate [20:06] The best AI features are the ones you never notice [21:00] Where to find Jen and Rising Team [21:42] Wrap up #AI #Leadership #RisingTeam #FutureOfWork #BuildWithAI #BuiltThisWeek #TeamManagement #AICoach #Apple #HR

This week we sat down with David Petrou, CEO of Continua, to demo Wheely — an agent development operating system built for developers running multiple AI coding agents who are hitting the wall of mental overhead. David spent 18 years at Google, was on the founding teams of Google Glass and Google Goggles, and left when ChatGPT launched. We also covered the foldable iPhone, Android vs Apple for AI agents, Fable 5.1, Anthropic vs OpenAI on developer experience, and whether new programming languages designed for agents are coming. We cover: What Wheely is and why David calls it an agent development operating system 18 years at Google, Google Glass, Google Goggles, and why he left to start a company The pivot from social AI to building the OS layer for multi-agent coding Why token costs dropping does not solve the human attention problem How Wheely decomposes problems, allocates agents, and knows when to interrupt you Why Git and GitHub were designed for humans and need to be rethought for agents How agent coding can modernize legacy code and rebuild bloated React frontends Who Wheely is actually built for and why the middle space is the opportunity Why new programming languages designed specifically for agents could unlock major efficiency gains Apple vs Android for AI — best hardware vs best OS for agent use cases How Android can disintermediate apps by running them simultaneously off screen Fable 5.1, Gemini 3.8 Flash, and the honest developer take on Anthropic vs OpenAI The coevolution of models, harnesses, and programming languages Whether the foldable iPhone matters if agent coding eliminates the need for extra screen real estate If you are building with AI agents, working in dev tools, or want to understand where the agent coding stack is heading, this episode is for you. Find David and Wheely: Website: wheelie.dev Twitter: x.com/dpetrou New episodes every Friday at BuiltThisWeek.com TIMESTAMPS [00:00] Intro [01:07] Meet David — CEO of Continua and creator of Wheely [01:56] Google Glass, Google Goggles, 18 years at Google [02:40] Why he left Big Tech when ChatGPT launched [05:18] The previous product — social AI for group chats [06:36] When coding agents changed everything [08:03] How building for agents internally became the real opportunity [08:56] Who Wheely is built for [09:46] What happens when you run 4 agents at once [10:17] Human attention is the scarcest resource in agent coding [11:16] How Wheely works — decompose, allocate, optimize, interrupt only when needed [13:05] The space between Claude Code and Lovable [14:05] Modernizing legacy code and greenfield agent projects [15:18] Why React is bloated and agents can rebuild frontends better [16:11] News — Apple foldable iPhone coming Q3 [17:29] Apple unified memory vs Android open OS for AI agents [18:27] Android disintermediating apps with off-screen agent execution [20:24] Will the foldable iPhone matter if agents take over development [20:48] Fable 5.1 launch — honest developer take [22:10] Anthropic vs OpenAI on developer experience and harness access [23:17] New programming languages designed for agents [25:19] The coevolution of models, harnesses, and programming languages [25:40] Where to find David and Wheely [25:55] Wrap up #AI #AIAgents #Wheely #DevTools #GoogleGlass #BuildWithAI #BuiltThisWeek #Anthropic #Fable #Apple #iPhone