Generative AI, powered by Large Language Models developed by companies like OpenAI and Anthropic, has ignited a revolution across all industries. Organizations everywhere are racing to harness this foundational technology—automating processes, creating new revenue streams, and reimagining what's possible. We are witnessing just the beginning of this transformation. But this revolution presents new challenges for companies, development teams and processes.
While this foundational technology has immense economic potential, companies are struggling to realize this potential within their businesses. The lack of talent, experience, and mainly the absence of tools tailored for AI is providing companies with tough challenges. Like every digital disruption, this will create both winners and losers. Winners, those companies that manage to put the technology into action fast, will eventually see substantial competitive advantage over their not so effective rivals. So, what are the complexities that companies need to deal with when executing on their AI strategy?
High System Complexity: Input and outputs of LLM applications are often very complex and very difficult to validate in detail by human experts.
Randomness by Design: LLM are highly complex, statistical models that always include a level of randomness that makes testing hard and requires new approaches.
Deep Domain Expertise Required: LLM applications address specific business processes which require deep expertise in the subject matter and therefore require that development teams effectively collaborate with domain experts from the respective fields.
Growing AI Portfolios: Large, lighthouse projects are still common for companies to start with AI development, but they are increasingly followed by smaller and more specialized use cases. This requires that companies will be able to develop and manage these at reasonable costs to ensure business cases are positive. Managing a portfolio of applications efficiently becomes a critical success factor for AI roadmaps.
Increasingly Complex Architectures: Multi-model applications that process text, images, video and audio will become more common in future. Agentic systems will combine multiple smaller applications to handle large, complex tasks. All of this increases the complexity of systems both in development and operations, and requires adapted processes to ship these applications at scale.
Accelerating Rate of Change: Every couple of weeks, a new, improved foundational model is released by the main providers. This presents companies with the difficult decision to update their systems or wait for a later version. Updating enables them to reap the benefits, but requires substantial re-testing to ensure that the applications works as intended.
GenAI enables companies to tackle challenging problems that were not feasible with traditional approaches. Still, this potential comes with a new set of challenges that require novel approaches for developing, testing and operating these applications. Unfortunately, the technical ecosystem is not built for these types of applications.
The AI space sees a rising number of solutions from both existing players and upcoming startups. Emerging solutions that attempt to address the problems fall within the following categories:
Prompt Management: Development teams increasingly integrate solutions that enable them to control the prompts, the instructions that are sent to the LLM. These solutions are very technical and provide very limited support for effectively testing applications at the required breath and depth.
Observability Platforms: Monitoring the results generated by applications in production is important but not sufficient. These platforms leave out the critical step to test an application thoroughly before new versions reach users on production.
Technical Evaluation Libraries: Various technical libraries are available today to test detailed aspects of applications like accuracy, security, and safety. These require deep technical understanding and are not built to collaborate with domain experts, leaving this critical perspective out.
This tooling gap explains why so many AI projects face delays, quality issues, and ultimately fail to deliver the expected value.
At Zenetics, we've experienced these challenges firsthand while building several complex AI applications. Based on our own experience and the solution we developed for developing, testing and operating these applications, we've created Zenetics, an AI-first quality management platform that enables cross-functional teams to ship reliable LLM applications quickly and confidently. Our solution is built on four core principles:
Ship Applications Fast and With Confidence: Empower teams to move from idea to production rapidly without sacrificing reliability, even while increasingly more applications are operated on production.
True Collaboration: Enable domain experts and engineers to work together effectively through intuitive interfaces and workflows. Combine both perspectives and experiences to build solutions for complex business problems.
Radical Simplification: Dramatically reduce the complexity and cost of building, testing, and operating ambitious AI applications. Leverage state-of-the-art approaches for testing GenAI applications at scale.
Security by Default: Protect sensitive information across all environments with built-in security controls.
We believe the winners in the AI era will be organizations that can rapidly develop, test, and deploy reliable AI applications at scale. Zenetics provides the missing infrastructure that makes this possible—turning AI projects into production-ready applications through comprehensive testing, monitoring, and quality control.
Founded by Michael Muckel, a distinguished architect of data and machine learning products with executive experience across media, travel, and retail industries, Zenetics brings proven expertise to the challenges of AI development.
Join us in building a future where AI's transformative potential can be realized with confidence, quality, and speed.
ZENETICS is one of the leading solutions for testing complex AI applications. Schedule a meeting to learn more about how to set up an effective LLM testing strategy and how ZENETICS can help you with that.