Solving the Paradox of GenAI: Break through the glass ceiling of scaling GenAI

From our perspective, this gap is the main reason companies fail to productise their GenAI products and they get stuck in the prototyping phase. For nearly a decade, Faktion has been focused on closing this gap by turning promising AI prototypes into practical, scalable systems that meet real-world needs.

In this post, we examine the challenges of Generative AI, explain why traditional methods fall short, and outline how Faktion’s approach converts prototypes into workable solutions—all while working toward a future where domain experts and business users can independently build, monitor, manage, and scale AI tools and applications.

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The idea behind Generative AI is that companies can use an always-on, AI-driven system to automate everyday tasks and even decision-making. However, several issues arise when putting this idea into practice:

• Unpredictable Outputs and Reliability Concerns:
  Unlike traditional systems that produce predictable results, Generative AI can yield varying outputs. These results require regular checks and updates to remain accurate and useful in different scenarios.
• Gaps in Domain Knowledge:
  While AI engineers are adept at developing AI tools and products, they may not fully understand the detailed rules and practices of specific industries. This makes it challenging to determine if AI outputs meet the necessary standards.
• Limited Tools for Industry Experts:
  Domain experts in various fields can often spot errors or missing context in AI-generated content, but the current tools and systems are not designed in a way that they could make necessary adjustments without any deep AI understanding and development skills.

Our vision at Faktion is to build AI tools, products, and platforms that put AI in the hands of business users, to not just use them but also to monitor, manage and scale them. To achieve this, we have created our dedicated approach built on three key principles:

Deep and Comprehensive AI Building Experience

Our team brings extensive expertise in creating state-of-the-art AI models—from data ingestion and model training to deployment and continuous improvement.

We possess not only technical skills in machine learning and generative AI models but also a comprehensive insight into every phase of development. Our experience covers all iterations, pitfalls, and challenges associated with building, scaling, and maintaining AI systems.

Robust Software Engineering & Integration

Our success in AI is built not only on advanced models but also on strong software engineering. This means designing and building scalable, user-friendly interfaces that translate complex AI processes into tools that address real-world business needs.

We focus on integrating intricate AI and MLOps tasks into intuitive, visual environments that bridge the gap between technical operations and everyday workflows, ensuring that powerful AI solutions are both accessible and easy to operate.

A Product Mindset: Turning AI & MLOps Tasks into Intuitive UX/UI

We believe that the true power of AI is unlocked when technical complexity is translated into user-friendly tools where domain experts and business users will eventually be autonomous in monitoring, managing and scaling AI tools and applications.

However, currently there is a high technical expertise needed that only AI engineers obtain and no domain expert or business user. Hence, we approach AI development with a product mindset where not only do we build AI tools and applications, but we also build interfaces to monitor, manage and scale them without any technical expertise needed.

In this approach, we enable domain experts to validate outputs and influence system improvements directly. Their feedback is then productively transformed into concrete tasks for our AI developers, ensuring that our systems are continuously refined and aligned with evolving business needs.

Generative AI is not a one-off deployment; it is a continuously evolving system that requires robust productisation to achieve sustainable value. Our comprehensive strategy not only bridges the gap between technical development and domain expertise but also integrates advanced technologies to streamline continuous improvement.

A Business-Centric UI/UX for AI Monitoring and Validation

Empowering non-technical users is critical. Our solutions include:

• Monitoring Dashboards:
  • Real-Time Metrics: Interactive dashboards display key performance indicators (KPIs) such as accuracy, response times, and error rates through graphs, heatmaps, and other visualizations.
  • Alert Systems: Automated alerts notify users when performance metrics deviate from expected ranges, prompting immediate action.
• Labeling and Annotation Interfaces:
  • Input/Output Visualization: Side-by-side displays of AI-generated outputs with their original inputs provide the necessary context for effective validation.
  • Annotation Tools: Users can highlight problematic sections, add comments, and tag outputs. This creates a high-quality dataset for retraining the model.
• Validation Workflows:
  • Positive and Negative Feedback: Simple mechanisms—such as thumbs up/down—allow users to validate outputs. Negative feedback is enriched with detailed explanations, which are automatically converted into actionable tickets for the AI engineering team.
• No-Code Application Building:
  Drag-and-drop workflow builders empower business users to create and modify data pipelines and validation workflows swiftly and without technical intervention.

Under the Hood: MLOps/LLMOps for Continuous Improvement

The backbone of our productisation strategy is a robust MLOps/LLMOps framework that automates every phase of the AI lifecycle:

• Advanced Tools and Technologies:
  • Azure Machine Learning: Provides scalable model training, deployment, and orchestration, ensuring secure and efficient operations.
  • LangFlow: Enables visual design and management of language model workflows, ensuring alignment with business objectives.
  • LangChain: Facilitates the creation of robust chains of prompts and maintains context across interactions, enhancing our retrieval-augmented generation systems.
  • Faktion Proprietary SDKs: Standardise data ingestion, model integration, and feedback loops, accelerating prototyping and deployment.
• MLOps/LLMOps Workflows and Tasks:
  • Model Training and Deployment: Data ingestion pipelines (leveraging Azure Data Factory and custom tools) gather and preprocess data. Trained models are containerized and deployed on Kubernetes clusters.
  • Real-Time Monitoring and Feedback Integration: A dedicated feedback API captures user validations in real-time. This data is categorised and automatically translated into tickets for iterative model refinements.
  • Iterative Improvement: Scheduled retraining cycles incorporate the latest validated examples into our ground truth dataset, with continuous integration/continuous deployment (CI/CD) pipelines ensuring rigorous testing before rollouts.
  • Governance and Compliance: Detailed audit trails and version control mechanisms ensure every update is compliant with industry standards (such as GDPR and HIPAA).

Generative AI holds transformative promise, but without the right blend of deep AI development, robust software engineering and a product mindset, its potential remains unfulfilled. Faktion’s comprehensive productisation strategy addresses this paradox head-on. While our long-term goal is to empower domain experts and business users to independently build, monitor, manage, and scale AI systems, we recognize that, in the near term, continuous involvement from experienced AI engineers is essential. This balanced approach ensures that our solutions remain both innovative and immediately practical, driving business value and staying aligned with real-world demands.

Are you ready to transform your AI prototypes into scalable, production-ready solutions? Contact us today, and let’s work together to build the future of AI.