Observable AI is rapidly becoming a critical component for enterprises relying on Large Language Models (LLMs). As business owners and entrepreneurs across Europe explore AI adoption, understanding its role in improving system reliability and transparency is invaluable. AI itself isn’t new, but making it observable, the process of closely monitoring, tracking, and diagnosing its performance, marks an important shift. For reliable AI solutions, which are trusted to handle sensitive data or make key decisions, factoring observability into Site Reliability Engineering (SRE) practices isn’t optional anymore. It’s urgent.
Let me take you through why observable AI is increasingly being hailed as the missing layer for enterprises managing complex LLM systems and how founders like me, leading bootstrapped startups, can utilize it without drowning in complexity.
Why Observable AI Matters for Founders and Teams
The technologies behind LLMs, OpenAI’s GPT series, for example, have proven their capabilities in speeding up content creation, providing intelligent customer service, and even processing applications for regulated industries like finance. Yet, enterprises hit barriers when scaling these solutions reliably. Case in point: a major bank deploying LLMs faced unexpected failures months later, where 18% of critical decisions were undesirably misrouted without any alerts or diagnostics to flag the issue. The lack of observability was the root cause.
Observable AI fixes this failure pattern. For founders who bootstrapped their businesses like I did, investing time and resources into AI can feel risky. Observable AI serves as a safety net. Transparent, traceable workflows mean fewer surprises when scaling. That makes every euro spent more effective, which is music to the ears of anyone working with lean budgets.
What Observable AI Brings to Large Language Models
Let’s break it down. Observable AI has four major pillars that amplify its role as an essential framework for business success:
Golden Signals of Performance
Observability platforms define measurable data points for AI systems , latency, traffic, errors, and saturation. These are often referred to as “golden signals” of system health. Introducing these into AI workflows helps teams proactively catch bottlenecks or inconsistencies in how LLMs interpret real-world queries.Error Budgets and Thresholds
Observable AI incorporates Error Budgets, a guiding system that determines acceptable failure rates before the system is considered unreliable. Enterprise founders working in regulated industries, like intellectual property protection or healthcare startups, need error thresholds to align AI performance with compliance requirements.Audit-Ready Transparency
For startups pitching to investors or collaborating with stakeholders, audit readiness is non-negotiable. Observable AI automatically traces prompts, models, outputs, and decisions taken, making regulatory audits smoother than ever. Need documentation for partners? One click downloads the logs comprehensively.Collaboration Across Teams
Weekly SRE scorecards powered by observability tools make AI data accessible to non-technical founders. Teams can review benchmarks without hiring specialized consultants. For example, tools such as New Relic Observability or Skywork.ai provide dashboards to visualize performance metrics across divisions.
How Entrepreneurs Can Get Started
Budget constraints are always top-of-mind; most early-stage ventures don’t have extensive engineering teams, let alone site reliability experts. Observable AI tools tailored for LLMs exist, and many are even free! Here’s a quick guide to getting started:
Pick Scalable AI Observability Tools
Free tools like Canvanizer work great for small-scale projects, while larger systems can test platforms such as Arize AI for deeper observability layers.Integrate With Existing Systems
Most platforms connect seamlessly to data pipelines without heavy technical configurations. Choose tools that integrate with existing ticketing or analytics software your team already uses.Automate Alerts
Set up real-time error notifications. Don’t risk team members overlooking crucial failures. This ensures quick resolution for incidents before they snowball.Use Pre-Deployed Benchmarks
Observable AI platforms often come preloaded with industry best practices. Use these to align your AI performance with specific business goals.
Common Mistakes and Lessons Learned
Here are pitfalls I’ve seen other founders slip into, and, let’s be honest, some I’ve learned the hard way too!
Mistake 1: Overlooking Collaboration
Observable AI is a team tool. If co-founders, engineers, and creative leads don’t align on expectations for LLM outcomes, it breaks the loop of accountability.
Mistake 2: Ignoring Edge Cases
Many AI systems fail quietly at the edges. In regulated industries especially, negligence in identifying these cases can result in harsher penalties or lost customers.
Mistake 3: Treating AI Metrics as Vanity Stats
Just because your AI responds faster doesn’t mean it performs better. Read the actual error logs or allow the observability tools to score performance relative to your business priorities.
Why Observable AI Should Be Top Priority for Female Startup Founders
Women founders often face unique challenges: smaller teams, tighter budgets, and more scrutiny from investors. Observable AI turns technology into an ally by offering accessible, real-time insights. This transparency strengthens your business case when pitching to skeptical venture capitalists or corporate collaborations. Platforms like Rootly or Squadcast have begun creating features for non-technical users, making them perfect tools to improve AI implementations in bootstrapped startups run by women.
Final Insights
Adopting observable AI flips the script for founders managing LLMs. It doesn’t just prevent AI mishaps; it’s about building systems that scale sustainably and transparently. For business owners seeking reliability without big-budget chaos, this approach closes the gap between experimentation and infrastructure.
By tracking every decision AI makes, observability creates trust, not just for the founder, but for customers, regulators, and team members. My advice? Start small and grow observability practices incrementally, just like scaling your business. Every smart decision helps build not only better routines but opens the door to long-term resilience.
FAQ
1. What is Observable AI?
Observable AI refers to the ability to monitor, track, and diagnose the performance of artificial intelligence systems in real time to improve reliability and transparency. It emphasizes measurable data points like latency, errors, and system health. Discover how observable AI works
2. Why is Observable AI crucial for enterprises using Large Language Models (LLMs)?
Observable AI ensures reliability and accountability in LLM systems by providing tools for proactive issue detection, error budgets, and traceable workflows. This is vital for enterprises handling sensitive data or regulated industries. Learn more about the impact of Observable AI on enterprises
3. What are the “golden signals” of AI performance monitoring?
Golden signals include crucial metrics such as latency, traffic, errors, and saturation. These metrics help enterprises identify system performance issues quickly, ensuring proactive correction before problems escalate. Understand golden signals with New Relic Observability
4. How does Observable AI handle regulated industry requirements?
It incorporates error thresholds and budgets, aligning AI performance with compliance needs. Enterprises in industries like healthcare or finance can use audit-ready transparency tools for streamlined regulatory processes.
5. Are there tools that simplify Observable AI for small businesses?
Yes, tools like Canvanizer for small-scale projects and Arize AI for deeper analytic frameworks enable small businesses to effectively integrate Observable AI into their workflows and improve reliability. Explore how Arize AI supports small businesses
6. How does Observable AI improve collaboration within startups?
Weekly Site Reliability Engineering (SRE) scorecards allow diverse teams to access AI performance data. New dashboards from platforms like Skywork.ai make metrics comprehensible for non-technical team members. Learn how Skywork.ai enhances collaboration
7. What are common pitfalls when adopting Observable AI?
Some common mistakes include failing to ensure team collaboration, neglecting to analyze edge cases, and focusing on vanity AI metrics like processing speed instead of performance quality.
8. How can founders and entrepreneurs start using Observable AI?
- Choose scalable tools like Canvanizer or Arize AI.
- Integrate observability into existing systems.
- Automate real-time error notifications.
- Use pre-deployed benchmarks offered by observability tools. Learn how to get started on Observable AI
9. What is the advantage of automation in Observable AI?
Observable AI automates error notifications and performance dashboards, helping teams resolve incidents quickly and efficiently. This reduces downtime and avoids service degradation. See how automation improves observability
10. Why should Observable AI be a priority for women-led startups?
Women entrepreneurs often face tighter budgets and limited team sizes. Tools like Rootly and Squadcast make observability accessible, empowering founders with transparent, real-time AI insights to secure investor confidence. Explore how Rootly supports women founders
About the Author
Violetta Bonenkamp, also known as MeanCEO, is an experienced startup founder with an impressive educational background including an MBA and four other higher education degrees. She has over 20 years of work experience across multiple countries, including 5 years as a solopreneur and serial entrepreneur. Throughout her startup experience she has applied for multiple startup grants at the EU level, in the Netherlands and Malta, and her startups received quite a few of those. She’s been living, studying and working in many countries around the globe and her extensive multicultural experience has influenced her immensely.
Violetta Bonenkamp’s expertise in CAD sector, IP protection and blockchain
Violetta Bonenkamp is recognized as a multidisciplinary expert with significant achievements in the CAD sector, intellectual property (IP) protection, and blockchain technology.
CAD Sector:
- Violetta is the CEO and co-founder of CADChain, a deep tech startup focused on developing IP management software specifically for CAD (Computer-Aided Design) data. CADChain addresses the lack of industry standards for CAD data protection and sharing, using innovative technology to secure and manage design data.
- She has led the company since its inception in 2018, overseeing R&D, PR, and business development, and driving the creation of products for platforms such as Autodesk Inventor, Blender, and SolidWorks.
- Her leadership has been instrumental in scaling CADChain from a small team to a significant player in the deeptech space, with a diverse, international team.
IP Protection:
- Violetta has built deep expertise in intellectual property, combining academic training with practical startup experience. She has taken specialized courses in IP from institutions like WIPO and the EU IPO.
- She is known for sharing actionable strategies for startup IP protection, leveraging both legal and technological approaches, and has published guides and content on this topic for the entrepreneurial community.
- Her work at CADChain directly addresses the need for robust IP protection in the engineering and design industries, integrating cybersecurity and compliance measures to safeguard digital assets.
Blockchain:
- Violetta’s entry into the blockchain sector began with the founding of CADChain, which uses blockchain as a core technology for securing and managing CAD data.
- She holds several certifications in blockchain and has participated in major hackathons and policy forums, such as the OECD Global Blockchain Policy Forum.
- Her expertise extends to applying blockchain for IP management, ensuring data integrity, traceability, and secure sharing in the CAD industry.
Violetta is a true multiple specialist who has built expertise in Linguistics, Education, Business Management, Blockchain, Entrepreneurship, Intellectual Property, Game Design, AI, SEO, Digital Marketing, cyber security and zero code automations. Her extensive educational journey includes a Master of Arts in Linguistics and Education, an Advanced Master in Linguistics from Belgium (2006-2007), an MBA from Blekinge Institute of Technology in Sweden (2006-2008), and an Erasmus Mundus joint program European Master of Higher Education from universities in Norway, Finland, and Portugal (2009).
She is the founder of Fe/male Switch, a startup game that encourages women to enter STEM fields, and also leads CADChain, and multiple other projects like the Directory of 1,000 Startup Cities with a proprietary MeanCEO Index that ranks cities for female entrepreneurs. Violetta created the “gamepreneurship” methodology, which forms the scientific basis of her startup game. She also builds a lot of SEO tools for startups. Her achievements include being named one of the top 100 women in Europe by EU Startups in 2022 and being nominated for Impact Person of the year at the Dutch Blockchain Week. She is an author with Sifted and a speaker at different Universities. Recently she published a book on Startup Idea Validation the right way: from zero to first customers and beyond, launched a Directory of 1,500+ websites for startups to list themselves in order to gain traction and build backlinks and is building MELA AI to help local restaurants in Malta get more visibility online.
For the past several years Violetta has been living between the Netherlands and Malta, while also regularly traveling to different destinations around the globe, usually due to her entrepreneurial activities. This has led her to start writing about different locations and amenities from the POV of an entrepreneur. Here’s her recent article about the best hotels in Italy to work from.
About the Publication
Fe/male Switch is an innovative startup platform designed to empower women entrepreneurs through an immersive, game-like experience. Founded in 2020 during the pandemic “without any funding and without any code,” this non-profit initiative has evolved into a comprehensive educational tool for aspiring female entrepreneurs.The platform was co-founded by Violetta Shishkina-Bonenkamp, who serves as CEO and one of the lead authors of the Startup News branch.
Mission and Purpose
Fe/male Switch Foundation was created to address the gender gap in the tech and entrepreneurship space. The platform aims to skill-up future female tech leaders and empower them to create resilient and innovative tech startups through what they call “gamepreneurship”. By putting players in a virtual startup village where they must survive and thrive, the startup game allows women to test their entrepreneurial abilities without financial risk.
Key Features
The platform offers a unique blend of news, resources,learning, networking, and practical application within a supportive, female-focused environment:
- Skill Lab: Micro-modules covering essential startup skills
- Virtual Startup Building: Create or join startups and tackle real-world challenges
- AI Co-founder (PlayPal): Guides users through the startup process
- SANDBOX: A testing environment for idea validation before launch
- Wellness Integration: Virtual activities to balance work and self-care
- Marketplace: Buy or sell expert sessions and tutorials
Impact and Growth
Since its inception, Fe/male Switch has shown impressive growth:
- 5,000+ female entrepreneurs in the community
- 100+ startup tools built
- 5,000+ pieces of articles and news written
- 1,000 unique business ideas for women created
Partnerships
Fe/male Switch has formed strategic partnerships to enhance its offerings. In January 2022, it teamed up with global website builder Tilda to provide free access to website building tools and mentorship services for Fe/male Switch participants.
Recognition
Fe/male Switch has received media attention for its innovative approach to closing the gender gap in tech entrepreneurship. The platform has been featured in various publications highlighting its unique “play to learn and earn” model.
