Turn scattered spreadsheets and system exports into pipelines, dashboards, and data your AI systems can actually use.
Data that is ready before the dashboard
Most "we need analytics" requests are really a data problem: numbers live in five different systems, nobody trusts the export, and every report is rebuilt by hand. WhiteGuava starts there, pipelines that pull from your real sources, clean and structure the data, and land it somewhere queries and dashboards can rely on.
The same pipelines double as the foundation for AI: agents and automation are only as good as the data behind them, so we build the analytics layer and the AI-ready infrastructure as one system, not two separate projects.
This is also where a lot of "AI in automation" projects quietly fail, not because the model is wrong, but because the data feeding it was never made reliable. Getting the pipeline right first is what makes everything built on top of it trustworthy.
What this engagement covers
Data pipelines
Ingestion from your CRM, ERP, product database, and third-party APIs into a structured warehouse, with scheduled or event-driven refreshes.
Analytics dashboards
Business-facing dashboards for the metrics your team actually checks, revenue, usage, operations, built on top of clean, versioned data.
AI-ready infrastructure
The same pipelines feed RAG systems, agents, and automation, so your AI work is grounded in current, structured data instead of one-off exports.
Predictive & business intelligence
Once the fundamentals are solid, we add forecasting, anomaly detection, and recommendation models where they change a real decision.
Data quality and governance
Versioning, validation, and access control on the data layer, so "which number is correct" stops being a recurring meeting question.
From raw exports to a single source of truth
Consolidating CSVs, SaaS exports, and manual spreadsheets into one structured place your team and your AI systems both trust.
Who this is for
Leadership teams flying on gut feel
Businesses making decisions without a reliable, current view of revenue, usage, or operations because the data is scattered across tools.
Teams about to invest in AI
Companies planning agents or automation that need the underlying data made trustworthy first, or the AI layer will just amplify bad numbers.
Ops teams drowning in manual reports
Anyone still rebuilding the same spreadsheet report by hand every week or month.
Frequently Asked Questions
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