Reports that explain the number, not just show it
AI-generated narrative summaries and anomaly explanations layered onto your existing dashboards, so reporting time goes to the decision instead of the write-up.
Where this usually breaks down
A dashboard shows that a number moved. It doesn't say why, and someone still has to work that out by hand - checking three other charts, remembering whether a campaign launched that week, then writing up a paragraph that reads differently depending on who's writing it that cycle. Do that across a dozen recurring reports for different stakeholders, each wanting a different angle on the same numbers, and the write-up becomes the actual bottleneck, not the analysis.
The alternative is worse: nobody does the dig, and the report goes out as a stack of charts. Whoever reads it either guesses at the cause or books a meeting to ask someone to explain it - a meeting a one-paragraph explanation could have prevented.
What this service actually solves
AI-powered reporting adds a narrative layer on top of the dashboards you already use - an automatically generated first-pass explanation of what changed and a plausible reason why, sitting next to the chart instead of replacing it. Anomalies get flagged and described in plain language before a person even opens the report, and the same underlying data can be reframed automatically for different stakeholders without someone rewriting it by hand each time.
AI-powered reporting is automated narrative generation and anomaly explanation layered on top of your existing dashboards, so a report arrives with a first-pass explanation attached instead of just a chart.
How we run it
We start with the dashboards and BI tools you already have rather than replacing them, since the reporting infrastructure usually isn't the problem - the missing narrative layer is. From there we set up anomaly detection tuned to what actually matters for your business, not every statistical blip, connect it to a summarisation layer that writes the explanation in plain language, and configure separate report formats for the stakeholders who need different levels of detail from the same data.
Capabilities & deliverables
AI-Generated Narrative Summaries
- Plain-language write-up of what changed and why, generated automatically
- Consistent tone and framing across every reporting cycle
Automated Anomaly Explanation
- Detection tuned to metrics that actually matter for your business
- A plausible explanation attached to the anomaly, not just a flag
Stakeholder-Specific Reporting
- The same underlying data reframed for different audiences
- Executive summary and operational detail generated from one source
BI Tool Integration
- Layered onto dashboards you already use
- No requirement to migrate off existing BI infrastructure
What's in scope, area by area
| Area | What we deliver |
|---|---|
| Narrative Layer | Automated summary generation connected to your existing dashboards |
| Anomaly Detection | Tuned thresholds and explanation logic for the metrics that matter |
| Stakeholder Reports | Separate report formats generated from the same underlying data |
How an engagement runs
Metric Prioritisation
We identify which metrics actually warrant an automated narrative versus which are fine as a raw chart, since not every number needs an explanation attached.
Anomaly Threshold Tuning
Detection is calibrated against your actual historical variance, not a generic statistical default that flags noise as a story.
Narrative Configuration
We set the tone, format, and level of detail for the generated summaries so they read like your team wrote them, not a generic template.
Stakeholder Report Mapping
Each recurring report gets mapped to the audience it serves, so an executive summary and an operational deep-dive come from the same data without separate manual write-ups.
Integration & Rollout
The narrative layer is connected to your existing BI tools and rolled out alongside the dashboards people already check.
How this compares
| AI-Powered Reporting | Manual Reporting |
|---|---|
| A first-pass explanation arrives with the chart | Someone has to dig into the cause after seeing the number |
| Consistent narrative across every reporting cycle | Quality and framing depend on who wrote that week's report |
| Same data, reframed automatically per stakeholder | Separate manual write-ups needed for each audience |
The AI-generated explanation is a starting point, not a final verdict - it's still worth a human sanity check before it goes to a board or a client.
What this changes for the business
- Report review time drops because a first-pass explanation is already attached instead of requiring a separate investigation
- Anomalies get flagged and explained before a stakeholder has to ask what happened
- Different audiences get a report suited to them without a separate manual write-up for each
Who needs this
Teams producing the same report for multiple audiences
If the same data gets rewritten by hand for an executive summary and an operational review, this removes that duplication.
Businesses with dashboards nobody explains
If a metric moves and the answer to 'why' currently depends on whoever happens to be free to investigate, this closes that gap.
Related work
We're still building out published proof for this specific service — ask us directly and we'll walk through relevant examples.
Common questions
No - it layers on top. Most clients keep their existing dashboard tool and add the narrative and anomaly-explanation layer alongside it, rather than migrating to new reporting infrastructure.
It's a well-informed first pass, not a guaranteed diagnosis. The model surfaces the most statistically plausible explanation based on correlated data, but a genuinely unusual event can still get mischaracterised, which is why we recommend a human review the summary before it goes to a client or a board.
Yes - that's the point of the stakeholder-specific reporting layer. Once configured, an executive summary and a detailed operational report both generate from the same underlying data without someone rewriting either by hand.
Depends on how many metrics and stakeholder formats are in scope, but a typical initial setup runs three to six weeks, including anomaly threshold tuning against your actual historical data rather than generic defaults.
It will catch what it's tuned to look for. Thresholds are set deliberately during setup so the system flags what matters to your business rather than every statistical blip, but that also means it can miss something genuinely novel outside the patterns it was tuned against - which is why it complements analyst judgment rather than replacing it.
Yes - that's part of the narrative configuration step. We set tone and format so the generated summaries read consistently with how your team already communicates internally.
Tired of explaining the same anomaly to five different stakeholders?
We'll add a narrative layer to the dashboards you already have, not replace them.
Talk to us →