AI Report Generation: Board Packs in Minutes, Not Days

Both. We build report generation for financial reports (monthly P&L, balance sheet, cash flow, board packs, quarterly business reviews, segment reporting) and operational reports (logistics KPI packs, supply chain performance, HR metrics dashboards, customer support weekly reviews, marketing performance reviews, product usage reports). The underlying pattern is the same: pull from a data source, run defined calculations, identify variances, generate commentary, format the output. Configuration differs by report type (which data sources, which calculations, which commentary templates) but the platform is shared. Most deployments start with one high-value report and expand to 4-8 report types in the first year.

Three inputs drive commentary. First, the materiality framework: which variances warrant commentary and which don't. Second, the attribution logic: for a flagged variance, what dimensions to drill into (e.g. a gross margin variance drills into product category, then region, then promotion activity). Third, a writing-style example set: 12+ months of your team's prior commentary, which the agent uses to match tone, structure, and level of detail. The first month or two involves analyst feedback on draft commentary, which refines both the attribution logic and the style prompts. The agent doesn't invent reasons; if the data doesn't support an attribution, it says so.

Yes. We integrate with Tableau, Power BI, Looker, Metabase, and Sigma for both data retrieval (reading existing prepared datasets or published data sources) and output embedding (inserting AI-generated commentary into existing Tableau dashboards via the REST API or into Power BI via a custom visual). Most clients prefer to keep their existing visualizations and augment them with commentary and narrative, rather than rebuilding. The agent can read from existing dashboards, so you don't have to rebuild your data model. The goal is augmentation, not replacement. The BI tool remains the visualization layer; we add the narrative layer on top.

The pipeline includes a data quality layer that runs before report generation. Checks cover: row counts against expected ranges (e.g. monthly sales volume should be within ±30% of trailing 3 months), null rates on key dimensions, cross-system consistency (revenue in the warehouse matches revenue in NetSuite within a tolerance), and anomaly detection that flags values significantly outside historical ranges. If source data fails quality checks, the report run pauses and your team receives an alert with specific details. The report doesn't generate on suspect data. We also log every data quality check outcome so patterns of recurring issues surface in a monthly ops review rather than being rediscovered every cycle.

Edge cases fall into two buckets. First, unusual business events (acquisition, divestiture, major reclass, one-time charge) that produce variances the agent can detect but can't contextualize from historical data alone. The agent flags these as 'material variance with no clear historical analog' and asks the analyst to provide context, rather than inventing one. Second, truly novel metrics (a new KPI added mid-year, a new reporting segment). The configuration layer needs to be updated and the agent is explicit about what it can and can't report on. The agent doesn't silently produce weak commentary; it either has the data and framework to produce good commentary or it surfaces the gap.

Wrong usually means one of three things. First, a factual error in a number: this is prevented upstream by the cross-reference computation (numbers come from a single source, not manual entry). Second, a wrong attribution: the agent claimed a variance was driven by X when Y was the actual cause. The analyst corrects, the correction feeds back, and the attribution logic is refined. Third, a wrong tone or over-confidence in commentary: the analyst edits directly and the edits write to the style training set. Across deployments, analyst edit rate on commentary drops from 60-70% in month 1 to 20-30% by month 3 as the agent calibrates to the team's voice and preferred framing.

Every number in the report traces to a specific query, table, and filter set. An audit export produces a line-by-line manifest: report section, line item, value, source SQL, timestamp of the underlying data, and any transformations applied. For SOX-scoped financial reports we produce attestations showing the controls operated: data freshness check passed, reconciliation to source system passed, materiality thresholds applied, human review occurred. External auditors get scoped read access to the manifest. Numbers that flow from the agent-generated commentary back into the report are flagged explicitly (e.g. a KPI that's defined as a rolling 4-quarter average has the computation disclosed). Nothing in the report is a black-box calculation.

A first report type (typically the monthly P&L or an operational KPI pack) runs 8-10 weeks. Weeks 1-2 are discovery: data sources, calculation definitions, templates, commentary style. Weeks 3-5 build the data pipeline and variance detection. Weeks 6-7 build the commentary engine and calibrate against 3-6 months of prior reports. Weeks 8-9 run a shadow cycle: the pipeline produces the report alongside the manual process and the team compares line by line. Week 10 cuts over with the analyst reviewing before distribution. Adding subsequent report types runs 3-5 weeks each once the platform is live, because the calculation engine, commentary style, and assembly pipeline are reusable. Full portfolio of 6-8 report types runs 6-9 months.