Vitalii Duk from Dynamiq: You can deploy chatbots in hours

"You can start deploying your customer-facing chatbot in a matter of hours and start realizing the value."

Rod Rivera

Feb 06, 2025

Transcript

Chris Rod Max discuss the latest developments in AI with Vitalii Duk, CEO of Dynamiq’s. They explore the challenges and opportunities in deploying generative AI solutions, the importance of customer education, and the unique features of Dynamiq’s platform.

Vitalii shares insights from his extensive experience in data science and machine learning, emphasizing the need for persistence and adaptability in the rapidly evolving AI landscape.

Chapters

00:00 Introduction to Dynamiq and AI Development
04:07 Vitalii’s Background and Experience in AI
09:29 Challenges in Deploying Generative AI Solutions
13:54 Success Stories: Real-World Applications of Dynamiq
16:51 Pitching Dynamiq’s: Differentiation in a Crowded Market
22:23 Understanding Customer Needs and Education
26:17 Addressing AI Security and Compliance Concerns
30:16 Dynamiq’s Unique Features and Capabilities
36:15 Platform Walkthrough: Demonstrating Dynamiq’s Functionality
45:02 Key Learnings and Advice for Founders

Takeaways

Dynamiq’s simplifies the journey from prototyping to deployment.
The market for generative AI development tools is growing.
New tools are needed for generative AI applications.
Dynamiq’s aims to unify various functionalities in one platform.
Companies can deploy features quickly using Dynamiq’s.
Customer education is essential for understanding AI capabilities.
Data privacy and security are critical concerns for enterprises.
Dynamiq’s offers an end-to-end solution for AI development.
Technical capability is necessary to use Dynamiq’s effectively.
Persistence and adaptability are key for success in AI.

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Episode Transcript

Chris: Welcome to another episode of the Chris Rod Max show, where we discuss the latest AI developments with industry leaders. Today, we’re joined by my co-hosts Rod and Max, along with our special guest, Vitalii Duk, CEO and founder of Dynamiq’s.

Vitalii: Hey guys, thanks for having me.

Understanding Dynamiq’s: The Enterprise GenAI Platform

Chris: Let me share my understanding of Dynamiq’s - it seems like a ‘what you see is what you get’ toolkit for AI deployment. Similar to how non-technical users can build websites by moving boxes around, Dynamiq’s appears to let users deploy AI products by linking databases in a low-code manner. But Vitalii, please tell us more about yourself and Dynamiq’s.

Vitalii: I’m Vitalii, founder and CEO of Dynamiq’s. My background spans over 11 years in data science. Dynamiq’s is an end-to-end platform for building GenAI applications. Our core focus is simplifying the entire journey - from prototyping to deployment, observability, and implementing guardrails. We have a low-code builder at the platform’s heart, with pre-built blocks for common tasks like vector database connections. We also support custom Python code integration for engineers and data scientists who prefer more control.

Background and Industry Experience

Chris: Before we dive deeper into Dynamiq’s, could you share more about your background and how you arrived at this idea?

Vitalii: I’ve been deeply involved in building internal machine learning platforms. I worked at Careem, later acquired by Uber, where I led the machine learning platform development across 14 countries serving 50 million users. We built everything from A-B testing platforms to feature stores. This experience, plus collaboration with Uber’s ML team, gave me extensive insights into building platforms.

Challenges in Enterprise GenAI Adoption

Chris: What challenges do enterprises face when adopting generative AI solutions?

Vitalii: There’s been a paradigm shift in AI applications. Previously, we built specific models for individual tasks like sentiment analysis or entity extraction. Now, foundation models can handle multiple tasks, with even more complexity through agentic capabilities. This new paradigm requires new tools focused on prompts, LLM fine-tuning, and RAG techniques. The traditional ML tooling doesn’t fit this new world of LLM-centric applications.

Platform Development and Feature Prioritization

Rod: How did you decide which features to build first, given the platform’s extensive functionality?

Vitalii: We had a clear vision from early customer discovery calls about core platform needs. While not everything existed in our original vision - like LLM agents and RAG capabilities which came later - we knew we wanted to unify everything under one roof. The market is currently fragmented with separate tools for observability, evaluations, and RAG. Our vision was to create a unified platform to eliminate the complexity of stitching together multiple tools.

Customer Success Story

Chris: Could you share a success story of how a company has used Dynamiq’s?

Vitalii: We have a Series B B2B SaaS platform customer who’s built 5-6 different use cases on Dynamiq’s. They’ve implemented everything from document summarization to customer-facing assistants and internal HR co-pilots. They deployed these features in hours instead of months, which was crucial given their limited engineering resources. They get built-in observability, evaluation capabilities, and the flexibility to test different models like OpenAI or Anthropic.

Market Education and Differentiation

Rod: How do you present Dynamiq’s to companies given the market confusion around AI tools?

Vitalii: One surprising insight is that many companies aren’t as advanced in GenAI as we might think. Some are just looking for centralized enterprise access to ChatGPT. We often need to educate customers about what’s possible with LLMs and how they can streamline operations. Our pitch focuses on showing potential use cases and demonstrating how quickly they can be implemented compared to building from scratch.

Security and Compliance

Chris: How do you address AI security and compliance concerns?

Vitalii: Data privacy and sovereign AI are hot topics. We’ve built much in-house and maintain infrastructure agnosticism. We offer on-premise deployment options in private clouds or bare metal servers, ensuring data stays within customer premises. Customers can deploy private instances of open-source models like Llama 2 and use vector databases like weaviate, all within their infrastructure. However, we remind customers that running LLMs on private infrastructure can be costly.

Platform Demonstration

Rod: Could you show us how Dynamiq’s looks?

Vitalii: [Demonstrates platform features] - Low-code builder with pre-built templates - RAG workflow capabilities with query refinement - Multiple LLM integrations (OpenAI, Anthropic, open-source models) - Vector database integrations - Agent definition and tool configuration - Multi-agent orchestration - Python customization options - Monitoring and observability features - Model comparison and evaluation tools

Closing Advice

Chris: What have you learned in your year in this space?

Vitalii: Persistence is crucial for founders in this space. Despite the crowded market, long-term focus and customer engagement are key. You need to iterate quickly as the technology evolves - for example, we initially focused heavily on fine-tuning, but now see more demand for RAG and agentic solutions. Being adaptable to customer needs is essential in this rapidly changing field.