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Use Case
Most recurring reports follow the same structure every cycle: pull data from the same sources, calculate the same metrics, write commentary about what moved, format it the same way, send it to the same people. The logic is defined. It just takes time to execute.
We build automated reporting pipelines that pull your data on schedule, aggregate it into the metrics you need, draft the narrative sections, format to your template, and deliver to your recipients, without anyone touching it. Analysts review and refine instead of building.
Tell us which report is taking the most manual time.
Six components that together replace the manual work of recurring report production.
The system connects to your data sources (Salesforce, GA4, Stripe, databases, spreadsheets) and pulls the required data on schedule. No manual export, no copy-paste between systems.
Raw data from multiple sources gets cleaned, normalized, and aggregated into the metrics your report requires. Date alignment, currency conversion, and deduplication handled automatically.
The AI drafts the narrative sections (the executive summary, the highlights, the commentary on what changed and why) based on the data. The analyst reviews and edits rather than writing from scratch.
Output follows your report template exactly: your branding, section order, chart styles, and data table formats. The report looks like a human built it, because a human designed the template.
Reports deliver automatically via email, Slack, Notion, or as a PDF on your defined schedule: weekly, monthly, or ad hoc. No manual trigger required.
When a metric moves significantly outside normal range (a traffic spike, a revenue dip, a conversion rate change) the system flags it in the report rather than burying it in a table.
From scheduled trigger to delivered report, the full sequence.
Scheduled trigger fires
At your defined cadence (weekly on Monday morning, monthly on the first of the month) the system begins the data pull automatically.
Data pulled from source systems
The system queries Salesforce, GA4, your database, Stripe, or whichever sources the report requires. Connections are pre-configured and authenticated.
Data normalized and aggregated
Raw data is cleaned and aggregated into the metrics the report needs. Week-over-week comparisons, MoM growth rates, and cohort calculations run automatically.
LLM drafts narrative sections
The AI writes the executive summary, key highlights, and commentary sections based on the data. It follows your report voice and flags significant movements.
Report formatted to template
Data and narrative are composed into your report template. Charts, tables, and sections appear in the defined order with your branding.
Delivered to recipients
The finished report delivers via email, Slack, or Notion at the scheduled time. The analyst reviews and can add comments or corrections before forwarding if needed.
The right fit is a team producing the same structured report on a regular cadence from consistent data sources.
Teams spending two or three days before every board meeting pulling numbers, building slides, and writing commentary. The same process every month, mostly pulling from the same sources.
Ops teams responsible for a weekly status report that aggregates metrics from multiple systems. Currently involves a person spending Friday afternoon on data wrangling.
Agencies sending monthly performance reports to 20+ clients. Each report is similar in structure but draws from client-specific data sources. Manual production is a significant overhead.
Analysts who spend more time answering “can you pull the numbers for” requests than doing analysis. Automating the recurring reports frees capacity for higher-value work.
We’d rather tell you upfront than take a project that won’t deliver the result you need.
One-off reports
If you need a report built once and not repeated, the automation setup cost isn’t worth it. One-off analysis is better handled by a data analyst working directly in your BI tool.
Data that is too messy to pull reliably
Automated reporting depends on data quality. If your source data has structural inconsistencies, frequent schema changes, or unreliable availability, the report pipeline will break regularly. Fix the data problem before automating the reporting on top of it.
Tell us which report takes the most manual time, where the data comes from, and how often it goes out. We’ll reply within one business day with a rough scope and price range.