AI changes everything
Legacy software is a system. You assign a task. It performs the task. You provide input, it converts to output. Again and again. Unless there's a bug — you know there's a bug because for some input, you get unexpected output. That's not the case anymore.
Modern AI-enabled applications are complex
They use autonomous agents. They incorporate reasoning. They call external tools. They manage context across many turns. They utilise multiple AI models for different parts of the same workflow.
AI-enabled application surrounded by orbital concepts: Agents, Reasoning, Models, MCP, Embeddings, Vector DBs, Fine-tuning, Context. Eight orbital nodes around a central application.
Without transparency, you operate in a black box
Without understanding what is happening within the application and why, you are operating a black box. You lose the ability to steer the AI. You can't retain control over your systems, data and decision-making process. Even worse, without knowing, you may be introducing compliance risks and biases to your experiences.
What you need is a glass box
No technical complexity
Building and maintaining a scalable AI stack should not require heavy architectural lifting.Transparency & autonomy
Access advanced features on traceability, observability and evaluation for the AI-enabled application.Security & Compliance
ISO certified active audit trails and a hardened platform avoids privacy and personal-data concerns.
Value through context
Track, measure and evaluate AI-enabled digital experiences to understand user request context.
The lifecycle of an AI request
Visitor or editor performs a request. AI-enabled application sends input and context to the AI provider. AI provider queries the LLM. LLM gets extra context from the vector database. LLM provides answers. AI provider creates observable traces. Internal user inspects, monitors, understands and influences AI operations for the organisation.
How does an admin inspect the answer of a specific user query? How does an admin calculate the cost of running an AI feature? How does an admin pick the preferred answer between similar answers? How do you ask domain experts to review the answers provided by a model? How do you ask the legal team to provide the green light on the context?
These questions have answers when the application is a glass box. They don't when the application is a black box.
