Summary
In 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.

Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Adam Honig about using AI to automate CRM maintenance

Interview

Introduction
How 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 Info

Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

Thank 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.
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Links

The 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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