We automate reporting end-to-end — scheduled Power BI and Tableau reports delivered on time, AI-generated executive narratives explaining what the numbers mean, board packs assembled from live data, and anomaly alerts replacing the weekly discovery process.
Manual assembly, stale data, missed anomalies — these are the patterns we eliminate in every reporting automation engagement.
The weekly sales summary takes 3 hours on Friday. The monthly management accounts take 2 days. A senior analyst or finance manager pulling data, formatting in Excel, writing a narrative, and emailing it. That time could be spent on analysis rather than assembly. Automated reporting eliminates the assembly entirely.
The report was built from Thursday's data. Distributed Monday morning. The meeting is Tuesday. By then the data is five days old. Automated reports built from live data at a scheduled time are current when they arrive — not historical by the time they're opened.
Something went wrong on Tuesday. Nobody noticed until the Friday meeting. Four days of compounding impact that could have been addressed on Tuesday morning if anyone was watching. Automated anomaly alerts fire within hours of a metric deviating from baseline — not at the scheduled review.
Revenue down 8%. Is that bad? Compared to what? Why? A dashboard full of numbers without narrative requires the reader to interpret everything themselves. AI-generated executive summaries explain what changed, by how much, compared to what baseline, and what requires attention — in plain English alongside the data.
The executive summary. The detailed management accounts. The sales rep performance view. The operations team daily update. All drawing from the same data, all formatted differently, all assembled manually. Automated reporting delivers the right version to each audience at the right time — from one data source.
Data from multiple sources, formatted to board template, charts regenerated, narrative written, slides updated, reviewed, revised. Three days of work that produces a document that's already partially out of date. A live data model feeding a board pack template reduces this to a review and approval exercise.
Scheduled delivery, AI narrative, board packs, anomaly alerts — the full reporting automation stack.
Reports built from live data and delivered to defined recipient lists at configured times — daily operations report at 8am, weekly sales summary every Monday at 7am, monthly management accounts on the 2nd of each month. Power BI Subscriptions, Tableau Server scheduled delivery, and Zoho Analytics automated reports all configured and monitored. Delivery failures alerted to the operations team — not discovered when a recipient notices they didn't receive it.
Dashboards show numbers. Executive summaries explain them. We build AI narrative layers that generate plain English summaries of what changed, by how much, compared to which baseline, and what requires attention — updated automatically each reporting cycle. Leadership reads the insight, not just the metric. The "so what" is answered before the meeting starts.
Board packs assembled from live data sources against a defined template. Charts and tables populated automatically from the current period's data. The finance team reviews and approves — they don't build from scratch. Monthly management accounts that previously took two days take 90 minutes of review time. Quarterly board pack preparation reduced from three days to one day of review and commentary.
Configured monitoring across key KPIs — when a metric deviates beyond a defined threshold from its baseline, an alert fires immediately to the relevant person. Revenue drop beyond a configured percentage: CEO notified. Conversion rate deviation: marketing director notified. Support ticket spike: support head notified. Issues discovered within hours of occurrence — not at the next scheduled review.
One data source, multiple report versions. Executive summary (5 KPIs, trend narrative, anomalies, actions). Sales management (pipeline, conversion, rep performance). Finance (P&L, cash, budget vs actual). Operations (production, capacity, efficiency). Each audience receives their version at the right time, formatted appropriately, containing exactly what they need — nothing more.
Automated reports are only valuable if people open and act on them. We configure delivery tracking — open rates, time spent, actions taken post-receipt. Reports that aren't being read are investigated: is the content wrong, the timing wrong, the audience wrong, or the format not working? Reporting automation that measures its own effectiveness.
Every business needs a different cadence. These are the most common patterns — adapted to your specific audiences and timing requirements.
Plain English explanation of what the numbers mean — generated automatically each reporting cycle alongside the dashboard data.
Illustrative example — actual narrative generated from your specific data, metrics, and business context.
Manual processes replaced, board packs automated, anomaly detection configured — across the restaurant chain, Atlantic LNG, and professional services clients.
The restaurant chain engagement included full reporting automation as a core deliverable — five distinct audience-specific reports delivered on different schedules to different recipient lists, all generated automatically from the same Power BI data model. The executive team had previously received a manually assembled weekly summary built from three separate system exports. Post-automation: the Monday summary arrived at 7am, built from current data, with no human involvement in its production. The finance director reviewed and approved — not built.
Every Friday afternoon, an analyst spent 3 hours pulling data from the ERP, CRM, and finance system, combining in Excel, building charts, writing a brief commentary, and emailing to leadership. The report covered: project delivery status, pipeline health, revenue vs target, and team utilisation. By Monday morning when leadership read it, the data was three days old. We automated the entire process — the report is now built from live data at 7am every Monday by Power BI Service, with an AI-generated commentary layer explaining the key movements.
As part of the Atlantic LNG Power BI implementation, we configured an anomaly detection and alert layer on top of the executive dashboard. Key operational and financial metrics were monitored against rolling statistical baselines — when any metric deviated beyond a configured threshold, an alert was generated and directed to the relevant stakeholder. This replaced a process where unusual data patterns were only discovered when someone reviewed the dashboard or when an issue had already escalated to a visible problem.
The Monday summary is in leadership's inbox at 7am — built from current data, with narrative explaining what changed. The meeting uses the report rather than waiting for it.
Numbers without context require the reader to interpret everything. AI-generated narrative explains what changed, by how much, against what baseline, and what requires attention — in plain English.
The weekly review becomes a response coordination meeting rather than a discovery session. Issues are already known, already flagged, already being addressed by the time the meeting happens.
Executive, sales, finance, operations — different needs, different schedules, different formats. All from one data model, all automated, all delivered without manual intervention.
ISO 27001. NDA before any data is shared. We map your current reporting process before designing the automation.
We map every manual report before automating anything. The process design matters as much as the technical build.
We map every manual report — who builds it, from what sources, how long it takes, who receives it, and how they use it.
Delivery schedule, audience lists, report format, and AI narrative scope agreed with stakeholders before any build begins.
Automated reports built, schedules configured, AI narrative tested against recent data, anomaly thresholds calibrated.
Delivery tracking configured, open rates monitored, report content adjusted based on feedback and usage data.
Monthly close involves two days of data assembly before any analysis can begin. Automated reporting shifts this — the data assembly happens automatically, the two days become 90 minutes of review and commentary. Finance time spent on analysis, not preparation.
The report you read Monday morning was built from data exported Friday afternoon. By the time it reaches you it's already out of date. Automated reports built from live data deliver current intelligence — not a historical picture.
Your most capable analysts spend Friday afternoons pulling data and formatting reports that could be automated. That time is better spent on analysis, interpretation, and recommendations — not data assembly and formatting.