AI Reporting & Analytics
AI-powered reporting that connects to your live data sources, generates automated reports, answers natural language queries, and surfaces insights your dashboards were never designed to show — without a data analyst in the loop.
Why Business Reporting Is Broken
Most companies have dashboards nobody reads and reports that take days to produce. The data exists — getting it into a decision-maker's hands in time to act on it is the unsolved problem.
The analyst pulls data, cleans it, builds a view, writes commentary, formats slides, and sends it — by which point the week it described is two weeks ago. Reporting cycles that lag decisions aren't useful.
Dashboards show metrics. They don't explain what changed, why it changed, or what to do next. Every time a number moves, someone has to manually investigate and write the narrative.
Skilled analysts spend 60–70% of their time producing recurring reports rather than doing actual analysis. They're expensive, underutilized, and perpetually behind on ad-hoc requests.
Business users who need answers have to queue a request for an analyst or learn SQL. By the time the query comes back, the meeting is over or the decision has already been made.
When report generation, data narrative, and distribution are automated, the bottleneck disappears. Decision-makers get accurate, contextualized reports on schedule — and ad-hoc answers in seconds, not days.
How AI Reporting & Analytics Works
Not a prettier dashboard. An AI layer that connects to your live data, writes the narrative, surfaces the anomalies, and delivers the right insight to the right person at the right time.
Postgres, Snowflake, BigQuery, Salesforce, HubSpot, Shopify, and more connected via secure read-only API. Single source of truth, not spreadsheet exports.
Reports run on schedule (daily, weekly, monthly) or triggered by a natural language question — 'What drove the revenue dip last Tuesday?' — with no SQL required.
Model runs the query, compares to benchmarks and prior periods, identifies outliers, and writes a structured narrative: what changed, by how much, and likely why.
Charts, tables, KPI callouts, and written commentary assembled into a branded report — PDF, Slack message, or email, depending on the delivery target.
Report delivered to stakeholders on schedule. Full report history archived and searchable. Every insight traceable back to its source query.
Business users ask questions in plain English. The AI writes the SQL, runs it, and returns a narrative answer with supporting charts — no analyst required.
The system flags significant deviations from trends or targets automatically — you're told about the revenue dip before you go looking for it.
Not just charts — written commentary explaining what changed, the magnitude, the likely driver, and a recommended action. Boardroom-ready in seconds.
Pulls from multiple systems in a single report — CRM, product analytics, finance, and marketing data combined into one coherent view.
What Teams Use AI Reporting For
All use cases live in production. Metrics are 90-day averages from active deployments.
Revenue, pipeline, churn, and operational KPIs compiled from 6 data sources every Monday morning. AI writes the narrative, flags the anomalies, and delivers a branded PDF to leadership before the weekly review. One analyst used to spend a full day on this.
Daily pipeline health digest sent to sales leadership — deals at risk, velocity changes, rep performance vs quota, and conversion rate by stage. Natural language queries answered ad-hoc ('Which deals are most likely to slip this quarter?').
Revenue, ROAS, CAC, and inventory data synthesized daily. Anomaly detection fires a Slack alert the moment ROAS drops below threshold or a product goes out of stock. Weekly report delivered automatically to the brand team.
Product usage, support ticket volume, and engagement signals combined into a daily customer health score. Accounts trending toward churn flagged to CS team automatically with a recommended intervention playbook.
AI Reporting Results Across Deployments
Aggregated from 40+ reporting & analytics deployments. Measured 90 days post-launch.
Where AI Reporting Delivers the Most ROI
By report type, 90-day average across active clients.
Average ROI across all client types
Everything Included in AI Reporting & Analytics
Full-stack delivery — data connections, report templates, NL query layer, anomaly detection, and ongoing report expansion.
Every workflow, configuration, and script is yours — with full documentation and Loom walkthroughs. Zero lock-in
Yes. We integrate with Looker, Metabase, Tableau, and Power BI as data sources and/or delivery targets. In many deployments, we sit alongside existing BI tools — handling automated narrative and NL queries while the dashboards remain in place for self-service exploration.
Business users type a question in plain English. The AI translates it into a SQL query against your connected data sources, runs it, and returns a narrative answer with a supporting chart. The SQL is logged and auditable. No database access required from the user's side.
Factual accuracy is high — the AI only reports on data it retrieved, not inferred data. Narrative interpretation (e.g., 'revenue dipped because of X') is informed by the data patterns and context you provide at setup. We benchmark narrative accuracy against your analyst team during launch and tune the prompts accordingly.
Yes, and cross-source synthesis is one of the most valuable things it does. A weekly revenue report that combines Salesforce pipeline, Stripe revenue, and product analytics data would typically require three separate queries and a manual merge. The AI pulls and synthesizes all three in a single report run.
We connect to PostgreSQL, MySQL, BigQuery, Snowflake, Redshift, Salesforce, HubSpot, Shopify, Stripe, Mixpanel, Google Analytics, Meta Ads, and most systems with a REST API. Custom connectors are scoped per engagement.
It replaces the report-production part of an analyst's job — the recurring weekly reports, the standard board packs, the ad-hoc 'can you pull the numbers on X?' requests. It frees your analysts to do actual analysis: building models, identifying opportunities, investigating complex questions that require judgment, not just query execution.
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