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Fondo is an all-in-one accounting platform for startups. Get your books closed, taxes filed, and cash back from the IRS.

  1. 🎧 START pod: Liam Karlsson & William Gyltman, Co-Founders of Rankad.ai "Turn AI visibility into revenue. On autopilot."

    MAR 31

    🎧 START pod: Liam Karlsson & William Gyltman, Co-Founders of Rankad.ai "Turn AI visibility into revenue. On autopilot."

    Liam Karlsson had no clue why his SEO clients were losing traffic while rankings held Then his 57-year-old mom asked ChatGPT for new running shoes Nike answer. Bought the shoe. Was super happy.  Google was never part of that customer journey... That was the seed of Rankad.ai He pitched Co-Founder William Gyltman. They went all in Track and grow brand visibility across ChatGPT, Perplexity, Gemini, and Copilot.  Enter your domain, 30 seconds later you're in the app. AI agent optimizes your site directly Liam Karlsson & William Gyltman, Co-Founders, Rankad.ai at The Residency Demo Day  🎙️ Fondo START pod w/ David J. Phillips (full ep in comments) 00:25 “We’re helping companies earn more money in AI search” 02:34 AI search is moving fast: new models, algorithms, protocols 03:05 A new source of income beyond Google traffic 03:31 AI search is growing fast, but has not outrun Google yet 05:34 Enter your domain and get inside the app in about 30 seconds 05:52 Visibility, competitor, and company data inside one platform 06:07 AI gives optimization tasks based on scan data 06:15 The agent executes tasks on your site 09:41 SEO rankings held, but traffic still disappeared 09:47 Liam’s mom used ChatGPT to buy running shoes 10:00 “That was like the seed of Rankad”

    12 min
  2. 🎧 START pod: Matthew Chen, Founder & CEO, Laurence "Autonomous performance marketing"

    MAR 27

    🎧 START pod: Matthew Chen, Founder & CEO, Laurence "Autonomous performance marketing"

    Amazon sellers do not have a data problem. They have a decision problem. They already have the clicksThe conversionsThe impressionsThe keyword history What they do not have is a system that knows what to do with it. So brands pay agencies $5,000 to $50,000 a month - and still lose money on ads Matthew Chen built Laurence to change that. A quantitative system for Amazon advertising.Built for continuous decision-making under profit constraints.Using existing ad copy, reinforcement learning, and custom models to run ads on autopilot. When confidence is high, it actsWhen data is sparse, it borrows signal from similar keywords The result: about 40% better performance for customers. Amazon is the wedge.Autonomous performance marketing is the bigger vision. 🎧 START pod: Matthew Chen, Founder & CEO, Laurence "Autonomous performance marketing" 00:23 What Laurence does02:12 How the company found the wedge02:47 The customer quote that changed the company03:15 The flaw in the standard Amazon ad model05:25 The move from Amazon to the wider internet06:12 How Laurence uses transformers today

    8 min
  3. 🎧 START pod: Milind Sagaram, Co-Founder & CEO, Articulate "Speeding Up Construction with AI"

    MAR 27

    🎧 START pod: Milind Sagaram, Co-Founder & CEO, Articulate "Speeding Up Construction with AI"

    Construction doesn't fail on the jobsite. It fails in the drawings. The jobsite just reveals it Project managers spend half their time scanning plans page by page for conflicts between disciplines. Plumbing through steel beams. Electrical into HVAC They still miss most of it. Millions in rework when caught in the field Milind Sagaram built Articulate to catch these issues before construction starts AI reads the PDFs. Finds clashes across architectural, structural, and MEP sheets. Generates draft issue reports automatically The surprise: construction teams aren't resistant. They want it more than anyone expected 🎙️ Milind Sagaram, Co-Founder & CEO, Articulate / Helonic.com on Fondo START pod  00:18 AI for finding drawing issues before construction starts02:24 The old workflow: manual plan review02:45 The consequence: rework and delays03:00 Small issues, massive downstream cost03:27 Copilot, not replacement04:04 Why AI belongs in construction04:39 What surprised him about selling into the industry05:59 Who Articulate sells to

    7 min
  4. 🎧 START pod: Tejas Bhakta, Founder & CEO , Morph "Subagents and tools that improve coding agents"

    MAR 27

    🎧 START pod: Tejas Bhakta, Founder & CEO , Morph "Subagents and tools that improve coding agents"

    Agents don’t need bigger models. They need better tools. Morph trains coding subagents.Not for humans. For frontier models. Fast Apply edits at 10,000 tokens/sec. WarpGrep handles code and log search. Both keep the main model’s context clean Because when context gets too large, performance drops. Now Morph is pushing coding subagents even faster. One newer model runs at 33,000 tokens/sec: https://docs.morphllm.com/sdk/components/compact 🎙️ Tejas Bhakta, Founder & CEO, Morph 01:30 Fast Apply + WarpGrep02:26 Context fills up around 100k02:38 Keep the main model context clean03:31 “You can’t scale human attention 100x”07:28 Founders are missing how to value human attention08:30 New model at 33,000 tokens/sec10:08 Better, faster, and cheaper than the frontier

    20 min
  5. 🎧 START pod: Pamir Ehsas, CEO & Co-Founder, Arcline "AI-native legal services for startups"

    MAR 25

    🎧 START pod: Pamir Ehsas, CEO & Co-Founder, Arcline "AI-native legal services for startups"

    Pamir Ehsas spent years as outside counsel serving startups.  He saw the same problem on repeat Simple legal work took weeks. Pricing was opaque. Lawyers kept starting from scratch instead of using AI So he built Arcline.  AI generates the first draft. Elite lawyers from the best firms, and schools (Harvard, Oxford etc.) do the final revision Up to 80% of the work gone. Same-day turnaround (Try getting that from a traditional law firm)  50+ venture-backed startups already onboard when this ep was recorded Most legal AI tries to replace the lawyer. Pamir replaced the busywork 02:09 “Fondo, but for legal” / AI-native legal for startups03:12 Sold AI to law firms first; then pivoted to end users04:52 Why Arcline uses experienced lawyers, not junior lawyers05:43 Faster, higher-quality work — including complex matters07:00 Big Law was slow; incentives were broken07:36 Why going direct creates the data loop to improve AI08:34 AI does the grunt work; lawyers create trust11:08 What startups actually come to Arcline for12:26 The long-term vision: legal advice at your fingertips

    15 min
  6. 🎧 START pod: Naman Ambavi, Founder & CEO, Oximy "See and control all AI activity across your enterprise"

    MAR 25

    🎧 START pod: Naman Ambavi, Founder & CEO, Oximy "See and control all AI activity across your enterprise"

    An employee used his corporate Gemini account to generate fake receipts for reimbursements Not because he was lying. He just didn't have the real ones That's when the CISO realized they needed Oximy Ask an enterprise how many AI tools they use. They say 10. The real number is probably 40+ Most of the risk isn't malicious. People just want to get things done faster. So customer lists end up on free tools with no DPA The first instinct is to block everything. But people bypass restrictions anyway The real question: how do you say yes to AI without losing control? That's Oximy. A control layer for enterprise AI adoption. Track usage, manage spend, enforce governance 🎙️ Naman Ambavi, Founder & CEO, Oximy on Fondo START pod 00:19 — What Oximy helps enterprises understand and manage02:24 — From India to the Bay, and the thesis behind Oximy03:29 — Track adoption, cost, and risk controls03:52 — “They say they’re using not more than 10… the number goes roughly over 40”04:22 — Compliance, retraining risk, and why oversight matters05:10 — Most misuse starts with harmless intent06:23 — The gap between Silicon Valley and corporate America07:35 — How to say yes to AI without losing control10:25 — Why the pain shows up most clearly at 1,000+ employees12:59 — Why banning AI is the first instinct — and why it doesn’t last

    17 min
  7. 🎧 START pod: Raffi Isanians, Founder & CEO, Mage Legal "Automatic AI M&A Legal Diligence"

    MAR 24

    🎧 START pod: Raffi Isanians, Founder & CEO, Mage Legal "Automatic AI M&A Legal Diligence"

    Attorneys are trained to spot issues. That’s literally what law school teaches. Show them your product, and the first thing they’ll say is: “the margin is off on this.” Every hour they spend learning software is an hour they’re not billing. Raffi Isanians knows that because he lived it.Kirkland. Gunderson. Years inside private equity and venture work. That’s why Mage Legal has a simple standard: if a lawyer opens the product with no instructions and can’t figure it out, "we’re failing" Comprehensive AI coverage across the entire data room:red and yellow flags, diligence memos, disclosure schedules, redline comparison. 1,500 documents in tens of minutes.  All async. 05:15 - Puts a TOS into ChatGPT 3.5, comes back 85-90% there 06:20 - Every hour learning a tool is an hour not billed 08:37 - The product worked, nobody wanted it 09:00 - YC partner on the two-year window 09:53 - AI is an F1 engine in a world of bicycles 13:00 - Clients are pushing AI adoption, not the lawyers 15:19 - The goal is zero behavior change 17:00 - 1,500 documents, diligence memos, tens of minutes 18:22 - One good associate now does the work of six 30:56 - Engineers simplify, lawyers complicate 33:00 - 11PM, picture of his daughter, back to work 40:34 - Simple enough to use with zero instructions

    43 min
  8. 🎧 START pod: Lucas Ngoo, Co-founder & CEO, Cortex AI "The Real World Is the Next Training Ground for Embodied AI"

    MAR 19

    🎧 START pod: Lucas Ngoo, Co-founder & CEO, Cortex AI "The Real World Is the Next Training Ground for Embodied AI"

    The internet was the training set for intelligence Nobody has built the equivalent for the physical world Previously, Lucas Ngoo co-founded Carousell, scaled it past $1B  Now at Cortex AI he's collecting the data robotics labs need to train foundation models Cameras, VR headsets, glasses on factory workers, retail workers, everyday people. Recording real-world manipulation work (Maybe tens of millions, even hundreds of millions of hours) Not building the robot. Not training the model. Collecting what goes in 2026: scale data in a big way 2027+: start rolling robots out, 90% teleoperation, 10% autonomy While humans step in, the system keeps learning That's the flywheel toward full autonomy 01:38 Cortex as the data layer for general-purpose robotics02:19 Why text data doesn’t transfer cleanly to the real world02:51 Recording day-to-day human manipulation work03:23 “Tens of millions” to “hundreds of millions of hours” of data06:37 Cortex’s role: “We are really the data piece”07:28 When humanoids become part of everyday life08:00 2026 as the year data scales08:13 90% teleoperation, 10% autonomy08:37 Data flywheel toward full autonomy08:55 Synthetic data + real-world data reinforcing each other

    11 min
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Fondo is an all-in-one accounting platform for startups. Get your books closed, taxes filed, and cash back from the IRS.

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  • Creator
    Fondo
  • Years Active
    2025 - 2026
  • Episodes
    47
  • Rating
    Clean
  • Copyright
    © 2026 Fondo
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