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AI Engineering Podcast

AI Engineering Podcast

By: Tobias Macey
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 This show is your guidebook to building scalable and maintainable AI systems. You will learn how to architect AI applications, apply AI to your work, and the considerations involved in building or customizing new models. Everything that you need to know to deliver real impact and value with machine learning and artificial intelligence.© 2024 Boundless Notions, LLC.
Episodes
  • The Anti-CRM CRM: How Spiro Uses AI to Transform Sales

    The Anti-CRM CRM: How Spiro Uses AI to Transform Sales
    Jul 21 2025
    SummaryIn this episode of the AI Engineering podcast Adam Honig, founder of Spiro AI, about using AI to automate CRM systems, particularly in the manufacturing sector. Adam shares his journey from running a consulting company focused on Salesforce to founding Spiro, and discusses the challenges of traditional CRM systems where data entry is often neglected. He explains how Spiro addresses this issue by automating data collection from emails, phone calls, and other communications, providing a rich dataset for machine learning models to generate valuable insights. Adam highlights how Spiro's AI-driven CRM system is tailored to the manufacturing industry's unique needs, where sales are relationship-driven rather than funnel-based, and emphasizes the importance of understanding customer interactions and order histories to predict future business opportunities. The conversation also touches on the evolution of AI models, leveraging powerful third-party APIs, managing context windows, and platform dependencies, with Adam sharing insights into Spiro's future plans, including product recommendations and dynamic data modeling approaches.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsYour host is Tobias Macey and today I'm interviewing Adam Honig about using AI to automate CRM maintenanceInterviewIntroductionHow did you get involved in machine learning?Can you describe what Spiro is and the story behind it?What are the specific challenges posed by the manufacturing industry with regards to sales and customer interactions?How does the type of manufacturing and target customer influence the level of effort and communication involved in the sales and customer service cycles?Before we discuss the opportunities for automation, can you describe the typical interaction patterns and workflows involved in the care and feeding of CRM systems?Spiro has been around since 2014, long pre-dating the current era of generative models. What were your initial targets for improving efficiency and reducing toil for your customers with the aid of AI/ML?How have the generational changes of deep learning and now generative AI changed the ways that you think about what is possible in your product?Generative models reduce the level of effort to get a proof of concept for language-oriented workflows. How are you pairing them with more narrow AI that you have built?Can you describe the overall architecture of your platform and how it has evolved in recent years?While generative models are powerful, they can also become expensive, and the costs are hard to predict. How are you thinking about vendor selection and platform risk in the application of those models?What are the opportunities that you see for the adoption of more autonomous applications of language models in your product? (e.g. agents)What are the confidence building steps that you are focusing on as you investigate those opportunities?What are the most interesting, innovative, or unexpected ways that you have seen Spiro used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI in the CRM space?When is AI the wrong choice for CRM workflows?What do you have planned for the future of Spiro?Contact InfoLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksSpiroDeepgramCognee EpisodeAgentic MemoryGraphRAGPodcast EpisodeOpenAI Assistant APIThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
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    47 mins
  • Unlocking AI Potential with AMD's ROCm Stack

    Unlocking AI Potential with AMD's ROCm Stack
    Jun 23 2025
    SummaryIn this episode of the AI Engineering podcast Anush Elangovan, VP of AI software at AMD, discusses the strategic integration of software and hardware at AMD. He emphasizes the open-source nature of their software, fostering innovation and collaboration in the AI ecosystem, and highlights AMD's performance and capability advantages over competitors like NVIDIA. Anush addresses challenges and opportunities in AI development, including quantization, model efficiency, and future deployment across various platforms, while also stressing the importance of open standards and flexible solutions that support efficient CPU-GPU communication and diverse AI workloads.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsYour host is Tobias Macey and today I'm interviewing Anush Elangovan about AMD's work to expand the playing field for AI training and inferenceInterviewIntroductionHow did you get involved in machine learning?Can you describe what your work at AMD is focused on?A lot of the current attention on hardware for AI training and inference is focused on the raw GPU hardware. What is the role of the software stack in enabling and differentiating that underlying compute?CUDA has gained a significant amount of attention and adoption in the numeric computation space (AI, ML, scientific computing, etc.). What are the elements of platform risk associated with relying on CUDA as a developer or organization?The ROCm stack is the key element in AMD's AI and HPC strategy. What are the elements that comprise that ecosystem?What are the incentives for anyone outside of AMD to contribute to the ROCm project?How would you characterize the current competitive landscape for AMD across the AI/ML lifecycle stages? (pre-training, post-training, inference, fine-tuning)For teams who are focused on inference compute for model serving, what do they need to know/care about in regards to AMD hardware and the ROCm stack?What are the most interesting, innovative, or unexpected ways that you have seen AMD/ROCm used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AMD's AI software ecosystem?When is AMD/ROCm the wrong choice?What do you have planned for the future of ROCm?Contact InfoLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksImageNetAMDROCmCUDAHuggingFaceLlama 3Llama 4QwenDeepSeek R1MI300XNokia SymbianUALink StandardQuantizationHIPIFYROCm TritonAMD Strix HaloAMD EpycLiquid NetworksMAMBA ArchitectureTransformer ArchitectureNPU == Neural Processing Unitllama.cppOllamaPerplexity ScoreNUMA == Non-Uniform Memory AccessvLLMSGLangThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
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    42 mins
  • Applying AI To The Construction Industry At Buildots

    Applying AI To The Construction Industry At Buildots
    Jun 14 2025
    SummaryIn this episode of the Machine Learning Podcast Ori Silberberg, VP of Engineering at Buildots, talks about transforming the construction industry with AI. Ori shares how Buildots uses computer vision and AI to optimize construction projects by providing real-time feedback, reducing delays, and improving efficiency. Learn about the complexities of digitizing the construction industry, the technical architecture of Buildoz, and how its AI-driven solutions create a digital twin of construction sites. Ori emphasizes the importance of explainability and actionable insights in AI decision-making, highlighting the potential of generative AI to further enhance the construction process from planning to execution.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsYour host is Tobias Macey and today I'm interviewing Ori Silberberg about applications of AI for optimizing building constructionInterviewIntroductionHow did you get involved in machine learning?Can you describe what Buildotds is and the story behind it?What types of construction projects are you focused on? (e.g. residential, commercial, industrial, etc.)What are the main types of inefficiencies that typically occur on those types of job sites?What are the manual and technical processes that the industry has typically relied on to address those sources of waste and delay?In many ways the construction industry is as old as civilization. What are the main ways that the information age has transformed construction?What are the elements of the construction industry that make it resistant to digital transformation?Can you describe how you are applying AI to this complex and messy problem?What are the types of data that you are able to collect?How are you automating that data collection so that construction crews don't have to add extra work or distractions to their day?For construction crews that are using Buildots, can you talk through how it integrates into the overall process from site planning to project completion?Can you describe the technical architecture of the Buildots platform?Given the safety critical nature of construction, how does that influence the way that you think about the types of AI models that you use and where to apply them?What are the most interesting, innovative, or unexpected ways that you have seen Buildots used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Buildots?What do you have planned for the future of AI usage at Buildots?Contact InfoLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksBuildotsCAD == Computer Aided DesignComputer VisionLIDARGC == General ContractorKubernetesThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
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    49 mins

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