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In this episode of the AI Agents Podcast, host Demetri Panici sits down with Jotform Founder & CEO Aytekin Tank to discuss the newly emerging ChatGPT App Store and why it could become one of the biggest shifts in software distribution.

Aytekin explains how MCP (Model Context Protocol), originally introduced by Anthropic, helped shape the idea of connected AI apps — and how OpenAI’s ChatGPT App Store is bringing that vision to life. Instead of switching between dozens of SaaS tools, users can now interact with apps directly inside ChatGPT using natural language.

They also explore real-world examples including Jotform, Canva, Adobe Photoshop, Apple Music, and TripAdvisor integrations — showing how AI agents can connect tools, automate workflows, and reduce the need for traditional interfaces.

The conversation also covers why chat interfaces may replace traditional SaaS dashboards, how AI memory improves personalization, and why companies that build AI integrations now could benefit from massive distribution.

In this episode, we're seeing how AI agents and app stores are converging — and why the next generation of software may live entirely inside AI.

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⏰ TIMESTAMPS:

00:00 – MCP, Anthropic, and the rise of AI app ecosystems
01:07 – Introduction to the ChatGPT App Store
03:22 – Why AI apps feel like the early Apple App Store
05:28 – Jotform inside ChatGPT: building forms with AI
10:36 – Moving from traditional UI to chat interfaces
16:46 – Why AI apps are more powerful with context & memory
24:15 – Canva, Photoshop, and AI creative workflows
29:30 – TripAdvisor and AI travel planning apps
32:45 – Why ChatGPT apps are still early but growing fast
35:21 – The future of SaaS: apps inside AI agents

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In this episode of the AI Agents Podcast, host Demetri Panici sits down with Katie Fortunato, Co-Founder and EVP of Innovation & Platform Strategy at Hire Innovations AI, to discuss how AI is reshaping hiring, innovation, and the future of work.

Katie explains how her team built an AI innovation incubator to safely test new ideas without risking their core enterprise business. She also shares how AI is enabling faster product launches, leaner teams, and entirely new business models — including solopreneurs and fractional work.

The conversation dives into hiring trends, AI-driven product development, vibe coding, tech stack consolidation, and why companies that embrace experimentation will move faster than those waiting on the sidelines.

They also discuss how AI is changing talent acquisition, why healthcare and AI engineering roles are growing, and how founders can build faster using modern AI tools.

If you're interested in AI innovation, hiring trends, solopreneurs, and building products faster with AI, this episode is worth watching.

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⏰ TIMESTAMPS:

00:00 – Building an AI innovation incubator safely
01:09 – Katie Fortunato intro & Hire Innovations overview
03:08 – A day in innovation and product strategy
05:43 – Why legacy companies need AI sandboxes
07:10 – Three ways companies innovate with AI
09:35 – Launching Job Stream using vibe coding
11:22 – Non-technical founders building with AI
13:38 – Claude, Lovable, and rapid product development
16:11 – AI is consolidating the tech stack
18:30 – Finding product-market fit with AI
20:13 – Understanding hiring cycles and timing launches
23:00 – Automate yourself before AI automates you
24:39 – Healthcare, AI engineers, and job growth
27:03 – Solopreneurs and lean AI-powered teams
29:08 – Fractional work and gig economy growth
31:12 – Designing your dream life, not just your job
33:54 – Advice for founders using AI to free time

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In this episode of the AI Agents Podcast, host Demetri Panici sits down with Jotform founder Aytekin Tank to break down Jotform AI, the company’s new in product AI assistant built to help users create forms faster, easier, and with far less friction. Aytekin explains how Jotform AI can answer questions, take actions inside the product, and proactively suggest better next steps while users build.

They also get into how MCP powers the system behind the scenes, why Jotform AI uses multiple specialized agents instead of one general agent, and how voice interaction changes the onboarding experience for non technical users. If you want a practical look at how AI agents are being embedded directly into SaaS products, this episode is worth watching.

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⏰ TIMESTAMPS:
00:00 – Jotform AI uses multiple agents behind the scenes
01:00 – What Jotform AI is and how it works inside the product
02:21 – How Jotform AI improves onboarding and boosts success rates
05:03 – Live voice demo: building a pizza party signup form
08:32 – How Jotform AI is built on Jotform Agents
14:20 – Why users succeed faster with AI guided form building
24:13 – MCP, specialized agents, and how Jotform AI works technically
28:02 – Will users work inside products or through external AI agents?
33:13 – What’s next for Jotform AI and proactive suggestions
37:26 – Final thoughts on how AI changes product learning curves

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In this episode, we sit down with the founder and CEO of Sift AI to unpack a problem most founders don’t even realize they have: they’re solving the wrong problems.

From customer discovery and validation to the rise of “vibe founding,” we explore why asking for help with fundraising might actually mean you’re not ready yet—and how AI is both helping and hurting founders in that process.

We also dive into how tools like ChatGPT fall short when it comes to iteration, context, and real business building—and what a more structured, founder-first AI platform could look like.

If you're building (or thinking about building) something, this episode will challenge how you think about strategy, validation, and using AI the right way.

⏰ TIMESTAMPS:
00:00 – Most founders don’t know what they need help with
00:23 – The real reason you’re struggling with fundraising
01:34 – From startups to AI: the founder’s journey
03:09 – Why founders still lack real support
05:22 – The problem with ChatGPT for building businesses
07:16 – Founders don’t know what to ask AI
09:04 – ChatGPT’s bias and “people pleasing” issue
10:01 – The rise of “vibe founding”
14:14 – What “structure as a service” actually means
26:40 – The future of solopreneurs and job security

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In this episode of the AI Agents Podcast, host Demetri Panici sits down with James Luan from Zilliz to talk about how AI is already changing the day to day work of engineers. James explains why coding agents are already taking over parts of his workflow, how vector databases became a core building block for modern AI systems, and why retrieval still matters even in a world obsessed with bigger models.

They also get into the real mechanics behind RAG, hallucinations, MCP, long term memory for agents, and the challenges of building production grade AI systems that can search, reason, and scale reliably. If you want a practical conversation about where agent infrastructure is going and what engineers should actually pay attention to, this episode is worth watching.

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⏰ TIMESTAMPS:
00:00 – AI is already taking parts of engineering work
01:03 – James Luan’s background and first AI moments
07:07 – Why Zilliz was built and how vector databases fit in
16:58 – Long term memory, agent search, and reasoning workflows
21:37 – MCP, tooling limits, and real world production issues
31:02 – Are coding agents already replacing parts of engineering?
35:52 – AI for travel planning, presentations, and parallel work
38:57 – NotebookLM, Gamma, and James’s favorite AI tools
39:45 – Where to find James and Zilliz

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In this episode of the AI Agents Podcast, host Demetri Panici sits down with Dror Asaf, co-founder and CTO of Coval, to talk about what it actually takes to bring agentic AI into enterprise operations. They get into why supply chain and operations are still surprisingly archaic, why trust and security are some of the biggest blockers to adoption, and how enterprise teams can use AI to remove bottlenecks without removing human decision-making.

Dror also shares how Coval is approaching enterprise AI differently, from on-prem deployments and Microsoft Teams integrations to strong governance and compliance layers. They also dig into the future of jobs, why AI will likely reshape work more than eliminate it, and why the people who know how to leverage these tools will have a massive advantage going forward.

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⏰ TIMESTAMPS:
00:00 – AI, jobs, and why new skill sets will matter most
01:03 – Dror Asaf’s background and why he started Coval
03:42 – Raising pre-seed funding and finding the right VC partner
08:31 – Key milestones: first deployment, first failures, and building the team
11:05 – How Coval uses agents in enterprise operations and supply chain
14:04 – What Dror learned about agent reliability, governance, and hallucinations
23:58 – Coval’s edge: security, on-prem deployment, and Microsoft Teams
27:56 – The long-term vision for enterprise AI adoption
33:16 – Will AI replace jobs or transform them?
38:33 – Dror’s favorite AI tools: Claude Code, Co-work, and more

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In this episode of the AI Agents Podcast, host Demetri Panici sits down with David Wong, Chief Product Officer at Thomson Reuters, to explore how AI agents are starting to reshape legal, tax, audit, and other professional services industries.

They discuss how Thomson Reuters evolved far beyond its well-known news brand into a software and research powerhouse for lawyers, accountants, tax professionals, and risk teams — and why AI is now becoming one of the biggest technological shifts those industries have ever seen.

David shares his journey from engineering, consulting, and ad systems into leading product at Thomson Reuters, along with how his team tested early GPT models on legal research years ago and watched the technology go from failing badly to becoming good enough for serious professional use cases.

Demetri and David also break down how AI can help with legal research, tax preparation, compliance work, and document-heavy workflows, why tax is such a strong fit for AI systems, and how the future of professional services may involve smaller teams supported by highly capable AI agents.

This episode is a must-watch for anyone interested in AI agents, legal tech, tax automation, enterprise workflows, and the future of knowledge work — especially if you want to understand how AI is beginning to transform industries built on expertise, research, and structured judgment.

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00:52 – Meet David Wong, Chief Product Officer at Thomson Reuters

01:49 – David’s background and journey into product leadership

03:24 – What Thomson Reuters actually does beyond news

06:02 – Early exposure to AI and machine learning

08:39 – Testing GPT-3 on legal research years before the boom

10:20 – Where AI helps in legal research and written work

13:55 – How AI applies to tax preparation and compliance

24:00 – Teaching AI to use tools instead of “doing the math”

33:48 – How AI could flatten team structures in professional services

47:23 – Where to learn more about Thomson Reuters and CoCounsel

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In this episode of the AI Agents Podcast, host Demetri Panici sits down with Bryan Raymond, founder and CEO of Unstructured, to break down one of the least flashy but most important layers in AI: data preparation. They dig into why so many AI prototypes still fail in practice, why RAG systems struggle with messy enterprise data, and how structured inputs like JSON, Markdown, and HTML can dramatically improve model performance.

Bryan explains how Unstructured helps enterprises turn raw files, scanned documents, audio, video, and other messy sources into AI-ready data for RAG pipelines and agent systems. The conversation covers why context quality matters so much, why tables and document layout are still hard for models, how vision-language models changed the game, and what it takes to move from AI prototype to production.

They also get into where AI is heading in 2026: declining failure rates, more practical “bread and butter” use cases, better multi-agent systems, and why companies like Cursor and Lovable have succeeded by packaging great UX around powerful infrastructure. If you want a clearer view of what’s actually missing in enterprise AI stacks right now, this episode is a must-watch.

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⏰ TIMESTAMPS:
00:00 – Why AI still feels exciting and broken at the same time
01:03 – Bryan Raymond’s background and the origin of Unstructured
03:03 – What Unstructured does: turning raw data into AI-ready data
06:06 – Why RAG exists and why structured data matters so much
10:42 – The hard part: tables, layouts, scanned PDFs, and document parsing
17:48 – Who needs this most: gen-AI teams vs data engineering teams
20:20 – The industries moving fastest with enterprise AI
26:28 – 2026 predictions: lower failure rates and stronger agent systems
29:33 – Cursor, Lovable, enterprise UX, and where AI infrastructure is heading
33:42 – AI jobs, junior engineers, and where real opportunity still exists
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