Agentic Lakehouse Intelligence
From raw data dump to full KPI causal intelligence in under 24 hours — fully automatic. Three databases unified by AI agents: Graph DB for causal networks, SQL DB for actual numbers, Vector DB for the why.
The Challenge in Industrial
Industry-specific data silos and manual processes that slow down decision-making
Data Scattered Across Silos
Enterprise data lives in dozens of formats — PDFs, spreadsheets, CAD files, emails, reports. No unified view exists. Analysts spend more time finding data than analyzing it.
6 Weeks of Manual Setup
Traditional approaches require 3 data engineers spending 6 weeks on manual taxonomy creation, manual mapping, and manual schema design. Every new data source restarts the cycle.
Numbers Without Causal Understanding
Flat SQL tables show what happened but not why. When EBITDA drops, teams scramble through documents manually to trace root causes — from financial KPIs down to operational drivers.
AI Capabilities for Industrial
Purpose-built AI agents that understand your domain
Graph DB — KPI Causal Network
Maps causal relationships between financial and operational KPIs. Traverse from EBITDA Margin down through Revenue, Operating Cost, Occupancy Rate, and Tenant Retention.
SQL DB — The Actual Numbers
Stores structured data — tenant retention by asset, vacancy sqft, financial impact, rental income loss. The quantitative foundation for every causal insight.
Vector DB — The Why
Searches lease agreements, tenant correspondence, asset manager reports, and fund playbooks to surface the context behind every data point.
Auto-Construction Pipeline
From raw data dump to full KPI causal intelligence — zero data engineers, under 24 hours, fully autonomous. No manual taxonomy, no manual mapping, no manual schema.
How It Works
From raw data to intelligent insights — powered by AI agents
“EBITDA margin dropped from 34% to 21% across Fund II. What is driving this?”
| Asset | Retention | Drop |
|---|---|---|
| Asset 3 | 93% | -2% |
| Asset 7 | 77% | -15% |
HVAC and elevator issues cited as primary reason
June report flagging CapEx freeze impact
Fund I precedent: reissuing CapEx reversed decline
Tenant retention at Asset 7 dropped 92% → 77%, the dominant causal driver of EBITDA compression. Three tenants exited citing deferred maintenance — consistent with asset manager June report flagging CapEx freeze. Recommend reissuing CapEx per Fund I precedent.
Auto-Construction Pipeline
From raw data dump to full KPI causal intelligence in under 24 hours — zero data engineers required.
Enterprise data lakehouse — from raw data to causal KPI intelligence
The Challenge
Enterprise data was scattered across dozens of silos — financial reports, operational spreadsheets, tenant correspondence, asset manager reports, and fund playbooks. When EBITDA margin dropped from 34% to 21% across Fund II, the team had no way to quickly trace the root cause. Traditional setup required 3 data engineers spending 6 weeks on manual taxonomy, mapping, and schema creation. Every new data source restarted the cycle. Analysts spent more time finding data than analyzing it.
6 weeks → under 24 hours
What required 3 data engineers and 6 weeks of manual setup now completes autonomously in under 24 hours. Raw data dump to full KPI causal intelligence — no manual taxonomy, no manual mapping.
Graph + SQL + Vector unified
Three databases working as one. Graph DB maps KPI causal networks, SQL DB stores the actual numbers, Vector DB surfaces the why — all queried by a single Lumina Superagent.
Root cause in minutes, not weeks
Tenant retention dropped at Asset 7? The Superagent traverses Graph DB for causal paths, pulls actuals from SQL DB, and searches Vector DB for tenant exit letters and asset manager reports — synthesizing a root cause automatically.
Auto-construction pipeline
New data sources are ingested, concepts discovered, knowledge graphs constructed, and schemas induced — all automatically. Every new data source enriches the existing intelligence.
The Results
Explore Other Solutions
Ready to become
AI-first?
Schedule a 30-minute demo and see how DeepAuto can transform your enterprise data and workflows in weeks — not months.