AI & its practical application in business

Discover how the latest OCR and voice technology advancements from Mistral and OpenAI are revolutionizing business processes! In this episode, Bas Alderding and Koen Ter Velde explore: ✅ Mistral's breakthrough OCR technology with superior accuracy for complex documents✅ OpenAI's new audio models for speech-to-text and text-to-speech capabilities ✅ How to integrate these technologies into your business workflows ✅ The surprisingly affordable pricing ($1 per 1000 pages!) ✅ Real-world applications across different industries Whether you're looking to streamline document processing, enhance customer interactions, or create innovative voice experiences, these technologies offer unprecedented opportunities to transform how your business operates. Chapters: 00:00 Introduction to AI Developments 01:28 Exploring Mistral's OCR Technology 12:30 OpenAI's Voice Technology Updates 

KeywordsAI, business, document management, retrieval augmented generation, proof of concept, facility management, asylum seekers, technology, innovation, data processingSummaryIn this podcast episode, Bas Alderding and Koen Ter Velde discuss the practical applications of AI in business, specifically focusing on a proof of concept (POC) they developed for the Dutch Central Agency for the Reception of Asylum Seekers (COA). They delve into the challenges of document management within the agency and introduce the concept of Retrieval Augmented Generation (RAG) as a solution. The conversation covers the technical aspects of their implementation, including document ingestion, summarization, and the role of an AI agent in facilitating user interaction. They also explore future developments and the scalability of their solution, highlighting its potential for broader applications beyond contract management.TakeawaysThe proof of concept was developed for COA.RAG stands for Retrieval Augmented Generation.Document management is a significant challenge for organizations.AI can help streamline the retrieval of information from documents.Summarization of documents is crucial for effective information retrieval.The solution involves a multi-step process for document ingestion.AI agents can enhance user interaction with the system.Scalability is a key feature of the developed solution.Future developments include parsing images and scanned documents.The RAG model can be applied to various use cases beyond contracts.TitlesUnlocking AI's Potential in Document ManagementRevolutionizing Asylum Seeker Support with AISound Bites"We built a custom tool for COA.""We have a whole list of feedback.""It's really scalable now."Chapters00:00 Introduction to AI in Business01:47 Understanding Retrieval Augmented Generation (RAC)04:35 The Problem with Document Management07:09 Proposed Solution: Augmented Rack Model09:56 Technical Overview of the Proof of Concept19:27 Future Developments and Scalability

KeywordsAI implementation, project canvas, data requirements, skills, metrics, governance, integration, stakeholders, cost analysis, project timelineSummaryIn this conversation, Bas Alderding and Koen Ter Velde discuss the importance of proper planning in AI implementation, introducing the AI Project Canvas as a structured approach to navigate the complexities of AI projects. They explore various components of the canvas, including data requirements, necessary skills, key metrics for success, value propositions, governance, integration strategies, stakeholder engagement, cost analysis, and project timelines. The discussion emphasizes the need for a comprehensive understanding of these elements to ensure successful AI project execution.TakeawaysProper planning is critical for AI success.The AI Project Canvas provides a structured approach.Data requirements must be clearly defined.Identifying necessary skills is essential for implementation.Key metrics help measure project success.Value propositions should be concise and clear.Integration strategies are vital for project success.Stakeholder engagement is crucial throughout the project.Cost analysis helps in understanding project viability.A clear timeline aids in effective project management.TitlesMastering AI Implementation: The Project Canvas GuideNavigating AI Projects: Essential Planning StrategiesSound Bites"Proper planning is critical for AI success.""What data is needed for the project?""Integration is crucial for project success."Chapters00:00 Introduction to AI Implementation Planning01:53 The AI Project Canvas Overview03:46 Understanding Data Requirements05:51 Defining the Value Proposition06:36 Case Study: Real Estate Contracts13:55 Identifying Required Skills16:13 Key Metrics for Success17:25 Governance and Ethical Considerations22:23 Integration Strategies23:19 Stakeholder Engagement25:19 Cost Analysis and Revenue Projections28:32 Project Timeline and Milestones

KeywordsAI, reasoning models, deep research, business applications, AI developments, OpenAI, DeepSeek, market research, automation, technologySummaryIn this podcast episode, Bas Alderding and Koen Ter Velde discuss the latest advancements in AI, focusing on reasoning models and deep research tools. They explore how these technologies are evolving, their practical applications in business, and the implications for future AI developments. The conversation highlights the emergence of new reasoning capabilities, the significance of deep research tools like Perplexity, and the potential for automation in various sectors.TakeawaysAI is rapidly evolving, especially in 2025.DeepSeek models are significant advancements in AI.Autonomous research agents can perform complex tasks.Reasoning models enhance intelligence without needing more data.Reinforcement learning is key to improving model outputs.Reasoning models can unlock new business applications.Deep research tools are changing how research is conducted.Perplexity offers innovative solutions for deep research.Future AI will automate many research processes.AI can significantly improve market research efficiency.Sound Bites"AI is evolving rapidly in 2025.""DeepSeek models are pushing AI boundaries.""Future AI will automate research processes."Chapters00:00 Introduction to AI Developments02:13 Emergence of Reasoning Models05:55 Understanding Reasoning Models10:10 Practical Applications of Reasoning Models14:35 Concerns with Open Source AI Models17:31 Deep Research: A New Frontier22:39 Future of AI in Business

Keywords#AI, #FutureofJobs, #Automation, #WorkforceSkills, #GenerativeAI, #JobTransformation, #HumanAugmentation, #AIImplementation, #BusinessUseCases, #WorkforceDevelopmentIn this podcast episode, Bas Alderding and Koen Ter Velde discuss the implications of the Future of Jobs Report 2025, focusing on how AI is transforming the workforce. They explore the balance between job automation and augmentation, the skills needed for future roles, and the importance of human elements in AI integration. The conversation emphasizes the need for organizations to adapt and prepare their workforce for the changes brought by AI, while also highlighting the potential for new job creation alongside automation.Takeaways- Generative AI is enhancing human work rather than replacing it.- One third of current jobs are at risk of automation.- AI can augment roles by handling routine tasks.- Skills like prompt engineering will be crucial in the future.- Companies should start with small AI projects to build understanding.- A willingness to change is essential for workforce adaptation.- Human creativity and social skills will remain valuable.- AI can assist in decision-making processes.- Curiosity and experimentation are key to leveraging AI.- Hiring adaptable and curious individuals is vital for success.Chapters00:00 Introduction to the Future of Jobs Report 202501:42 Understanding the AI Revolution: Automation vs. Augmentation07:15 Exploring Use Cases for AI in Business10:37 Essential Skills for the Future Workforce16:11 Strategies for Human-Machine Collaboration19:37 Conclusion: The Importance of Human Skills in an AI World

KeywordsAI, OpenAI, AGI, AI trends, AI agents, business applications, NACO, AI implementation, machine learning, automationSummaryIn this podcast episode, Bas Alderding and Koen Ter Velde discuss the latest developments in AI, focusing on OpenAI's new O3 release and its performance on the Arc AGI benchmark test. They explore emerging AI trends for 2025, particularly the rise of AI agents and their applications in business. The conversation includes a case study on NACO, showcasing how AI can enhance efficiency in handling quotation requests. The hosts also speculate on the future of AI agents and the potential for achieving general intelligence in AI systems.TakeawaysOpenAI's O3 release marks a significant advancement in AI.The Arc AGI benchmark tests complex intelligence, not just programming skills.AI agents are expected to become more prevalent in business applications.NACO's project demonstrates the practical use of AI in automating quotation processes.The cost of AI computation is decreasing, making it more accessible.AI models need specific instructions to perform effectively in business contexts.Future AI models may require less context to understand tasks.The integration of AI agents can lead to substantial efficiency gains.General intelligence in AI could simplify implementation processes.The podcast encourages audience engagement and feedback.Sound Bites"A significant leap in my opinion.""We will be seeing more agents.""The potential upside of this is huge."Chapters00:00 Introduction to AI Developments10:44 Emerging AI Trends for 202516:59 Case Study: AI Agents in Action

#AI #artificialintelligence #business #automation #n8n #openai #machinelearning #newsletter #generativeai #productivity In this episode, Bas Alderding and Koen Ter Velde, founders of AI development agency SevenLab, demonstrate how they automated their company newsletter and social media content using AI workflows. They share their journey of transforming a time-consuming manual process into an efficient automated system that serves 20,000+ subscribers.Takeaways:AI automation can reduce newsletter creation from 2 days to a fully automated processCombining multiple AI tools creates a comprehensive content pipelineN8n workflows enable seamless integration of various AI servicesPerplexity AI helps with automated research and topic explorationGPT-4 can generate engaging, context-aware contentDALL-E integration creates relevant social media visualsContent can be automatically segmented for different audience typesThe same workflow can be adapted for various communication needsManual oversight ensures quality control of AI-generated contentAutomation can be scaled gradually, starting with simple workflowsChapters:00:00 Introduction and Newsletter Automation Challenge02:32 Why Automation Was Needed05:57 Overview of N8n Workflow07:29 Topic Research with Perplexity10:01 Web Scraping and Content Aggregation13:01 AI Content Generation Process17:08 Newsletter Segmentation and Distribution20:07 LinkedIn Post Generation and Image Creation24:43 Tips for Getting Started with Automation27:11 Benefits and Future Applications28:12 Conclusion and Call to ActionWant to learn more? Visit sevenlab.dev or schedule a meeting through cal.sevenlab.nl/team/sevenlab/sevenlab-introduction

In this episode, Bas Alderding and Koen Ter Velde, founders of AI development agency SevenLab, share insights into a recent AI project they built for a government organization in the Netherlands during a hackathon. The project aimed to streamline the regulatory compliance inspection process using AI, low-code, and no-code tools. They explain how they developed a prototype to automate data collection, transcription, and report generation, making inspections faster and more efficient.Takeaways:AI and transcription technologies can significantly reduce the time needed for regulatory compliance inspections.Multi-step AI processes can convert unstructured spoken data into structured, actionable reports.Combining low-code tools like Flutterflow and N8n with AI allows for rapid prototyping.Leveraging AI-assisted coding tools, such as Cursor, speeds up the development process.Using open source tools like Whisper for audio transcription allows for flexible and scalable integration.AI-generated reports require manual validation to ensure compliance and accuracy.Real-world applications of AI can make data-heavy processes more seamless and effective.Chapters: 00:00 Introduction and Overview of the Hackathon Project01:25 AI Training and Brainstorming for Government Inspection Use Case03:00 Hackathon Setup and Challenges04:30 The Inspection Process and Automating Compliance06:00 Description of Solution: Recording, Transcription, and Summary09:00 Switching from Flutterflow to Full Code for Flexibility13:00 Demonstration of AI-assisted Coding Tool (Cursor)17:45 Detailed Walkthrough of the Inspection App22:00 Backend Flow with N8n for Data Processing30:00 Creating Custom Letters and Reports Using AI34:20 The Benefits of Low-Code Tools in AI Projects37:00 Conclusion and Call to ActionWant to get in touch? Schedule a 15min intro through cal.sevenlab.nl/team/sevenlab/sevenlab-introduction

#AI #Gemini #Google #OpenAI #GPTSearch #watermarking #artificialintelligence In this conversation, Bas Alderding and Koen Ter Velde discuss two major developments in AI: Google's Gemini model updates and AI content watermarking. They explore Google's new grounding feature that allows AI models to verify information through internet searches, and discuss Google's SynthID watermarking technology for AI-generated content.Key Takeaways:Google's Gemini models now include a grounding feature that can search and verify information before providing answersThe grounding feature allows developers to control when and how often the model uses internet searchesGoogle's SynthID technology watermarks AI-generated content with invisible fingerprintsOpenAI has launched GPT Search for pro users, similar to Google's grounding featureDevelopers can now choose specific regions for data processing in Google's AI platformChapters:00:01 Introduction and Overview01:11 Explaining AI Grounding03:32 Demonstration of Gemini's Grounding Feature15:45 Discussion of AI Watermarking27:50 OpenAI's GPT Search Feature32:20 Preview of Future Topics (Computer Use in AI)Conclusion and Contact Information Want to get in touch? Schedule a 15min intro through cal.sevenlab.nl/team/sevenlab/sevenlab-introduction or visit sevenlab.dev

In this conversation, Bas Alderding and Koen Ter Velde, founders of AI development agency SevenLab, discuss advancements in AI including multi-agent architectures, OpenAI's new "Swarm" release, and other AI tools that have potential applications for business. They explore the benefits of multi-agent systems, recent developments in video models from Meta, and Tesla's robot innovations. The conversation also touches on the application of AI in support systems, emphasizing the effectiveness of specific AI agents in orchestrating complex tasks.Takeaways:- Multi-agent architectures allow AI agents to handle specialized tasks, enhancing overall efficiency.- Meta’s new video models, like MovieGen and Pyramid Flow, promise significant advancements in video generation.- OpenAI’s Swarm aims to make building AI agents easier and more practical.- Tesla’s new robot technologies, including the Optimus humanoid, showcase the growing capabilities of AI in robotics.- The efficiency of AI support systems can be significantly improved with multi-agent setups.- Different AI models have specific tasks, leading to better performance when properly orchestrated.- Faster AI response times are expected to dramatically improve user experience in AI-driven applications.Chapters: 00:00 Introduction to Multi-Agent Systems02:00 Recent AI News: Meta's MovieGen and Pyramid Flow05:00 Flux 1.1 Pro Image Generation Model Overview06:20 Tesla's Robotics Update: Robotaxi, RoboVan, and Optimus09:00 OpenAI's Swarm and Multi-Agent Architectures12:00 Example Use Cases for Multi-Agent AI Systems18:00 Practical Examples from SevenLab's AI Agents21:00 Multi-Agent Systems in Support Scenarios24:00 Improving AI Response Times for User Interactions26:20 Conclusion and Contact Information

In this conversation, Bas Alderding and Koen Ter Velde, founders of AI development agency SevenLab, discuss the transformative impact of artificial intelligence (AI) on businesses. They explore various applications of AI, the risks associated with its implementation, and the importance of data quality. The conversation also delves into the differences between machine learning and deep learning, best practices for AI implementation, and real-world use cases that demonstrate the effectiveness of AI in improving business processes.Want to know more, schedule an intro call at: https://cal.sevenlab.nl/team/sevenlab/sevenlab-introduction