Embedding Human Expertise into Reporting Infrastructure

Reporting today is data-intensive by design. ESG, climate, supply chain, and financial datasets are increasingly granular, interconnected, and auditable. At the same time, the final output must remain clear, coherent, and strategically aligned.

Large Language Models (LLMs) can help transform data into text. But they require context – and context is expensive. Common approaches attempt to solve this in three ways:

Each of these can work in isolation. None of them scale sustainably. The structural problem is this: There is no deterministic layer that embeds domain expertise into the transition between data and AI.

Without such a layer, automation either becomes unpredictable — or dependent on constant technical intervention.

Between raw data and AI inference, there must be a layer that:

In other words: human judgment must become part of the infrastructure. Not as an afterthought or buried in engineering code. But structured, explicit, and intelligible.

Human judgment must not be an external input to the system. It must be encoded into it. And that is difficult — unless the structure itself is designed for experts.

To operationalize this principle, I developed PDL — Prompt Data Language.

PDL is a format for writing highly structured natural text. It follows the widely adopted Markdown standard, but extends it with a small but expressive set of logical elements: retrieving values, defining conditions, iterating over data.

The input is designed for domain experts; the output is designed for humans and LLMs alike.

With PDL, structured datasets and domain logic are fused into deterministic yet elegant natural language before any AI model is involved. What the model receives is not raw data, but curated, intentional context.

This makes the human contribution tangible and maintainable. It becomes part of the system — not an informal comment in a workflow, not a technical workaround, but an explicit layer.

Imagine a structured dataset of products and their emissions. A PDL template could look like this: What this achieves is deceptively simple.

It translates structured data into consistent natural language. It embeds a clear threshold. It makes the decision logic explicit. It produces deterministic output — every time. It can be evolved easily and constantly.

The logic is visible. The reasoning is traceable. Any domain expert can read and refine this logic. Then the AI systems can build on it. The result is structured, auditable, and scalable.

In our automation work, this layer typically sits right before AI inference happens.

Consider a recurring scenario: A company has not yet provided all datapoints required for a report. Each week, a checklist is needed — along with a management summary.

Instead of manually drafting this from scratch or feeding entire databases into an LLM, a PDL template extracts missing datapoints, applies domain logic and generates a structured briefing. This briefing is then used by an AI system to produce a coherent management summary.

The result is:

Importantly, the quality of the outcome depends on the quality of the underlying data. Structured environments like the VERSO Hubs — where ESG, climate, and supply chain data are curated systematically — provide the ideal foundation for this approach.

The more disciplined the data collection, the more powerful this abstraction layer becomes. Data does not merely serve compliance. It becomes a strategic asset for truly intelligent automation.

And because this layer remains human-readable, consultants and domain experts can continuously refine the logic and add depth without requiring engineering resources for every iteration.

Infrastructure only matters if it creates tangible value.

If you work with us in services, it means both your and our expertise become scalable. Domain knowledge is no longer confined to individual documents or projects. It becomes part of a reusable, evolving structure.

If you are using our Hubs, structured automation increases the leverage of your data. The more consistently your information is curated, and the more human intelligance it can be merged with, the more precisely it can be transformed into actionable outputs.

If you are considering working with us, it means you are not investing in short-term AI experimentation. You are building a foundation that integrates automation responsibly — with human judgment embedded from the beginning.

Across all scenarios, the principle remains the same: AI becomes more reliable when expertise is structured — and only then can it become part of your everyday workflow.

The future of reporting will not be defined by who “uses AI.” It will be defined by who designs the systems around it. Automation without structure leads to noise. Manual processes without automation lead to inefficiency. The sustainable path lies in structured collaboration between humans and machines.

PDL is one building block in that architecture: Structured collaboration between humans and machines — transparent, traceable, and accountable.

The industry continues to evolve. We intend to shape that evolution in a way that keeps reporting precise, actionable, and accountable. We are committed to building infrastructure that strengthens quality rather than replacing judgment.

If you are curious, explore our open-source PDL RepositoryPDL Repository or interact with it in the PDL PlaygroundPDL Playground*. It is a small but tangible glimpse into how we think about the future of reporting.