Turn reporting data into a briefing your team can use.

An online service has seen an increase in failed transactions. The analytics platform records a lower completion rate, application logs contain several recurring errors, and deployment records show that a release went live during the reporting period.

The analyst must compare periods, sort through log messages, check the timing of changes, and write a summary. Much of that work follows the same pattern each week.

The information is available, but turning it into a useful account of service performance takes repeated manual effort.

D4 can gather reporting data on a schedule and bring together relevant metrics, log events, and operational records.

The workflow can calculate period-on-period changes, filter duplicate events, and select the records associated with a change in performance. It can then pass that prepared context to a model with a specific task and an expected response format.

An LLM might group differently worded errors into proposed categories, summarise the main changes, and identify questions for investigation. It can draft the narrative around verified figures without being responsible for calculating them.

For example, the output could highlight that failed transactions increased during the same period as a rise in timeout errors. It could suggest checking the affected service while making clear that the available evidence does not establish the cause.

• Compare reporting periods: Calculate changes in agreed metrics using consistent definitions.
• Group log messages: Use AI to propose categories for related errors that appear with different wording.
• Summarise changes: Prepare concise explanations grounded in the supplied metrics and records.
• Add operational context: Include relevant releases, incidents, and maintenance events.
• Prepare audience-specific drafts: Produce a technical investigation brief or a shorter management update from the same evidence.
• Flag gaps: Identify missing sources, conflicting records, and conclusions that need further investigation.

D4 is model agnostic. Workflows can use different LLMs and AI providers according to the task, data requirements, and deployment constraints.

A smaller model may be sufficient to categorise log messages. A more capable model may be appropriate for synthesising several sources into a report. Different steps in the same workflow can use different models.

The workflow defines the inputs, expected outputs, validation, and routing around each call. Teams can evaluate alternative models against the same reporting requirements without redesigning the entire process.

Before a report is distributed, the workflow can check that quoted figures match the calculated metrics, required sections are present, and supporting records are referenced. Interpretations that need judgement can be routed to the analyst for review.

The analyst receives a prepared briefing with the main changes, relevant log groups, and open questions. A service owner can receive the approved summary, while an investigation system receives structured findings for follow-up.

Each reporting cycle starts with the same definitions and checks. People can spend more time assessing what the findings mean and deciding what to do next.