Cartographer reads your warehouse and builds the Decision Context Graph: entities, relationships, and causality, automatically. Every layer above it, from drilldown to deep analysis to journey intelligence, queries that same grounded foundation. Same question, same answer, every time.
ARCHITECTURE
Layers 1+2 build your context graph. Layers 3-6 unlock its value. Click any layer to learn more.
From raw data to strategic decision, each layer adds intelligence. Click any stage to see how it works.
Traditional semantic-layer projects take months: interviewing stakeholders, writing LookML, documenting relationships by hand. Cartographer reads your actual query logs and produces the same structure, in hours, without anyone authoring it.
The pipeline learns from behavior, not just schema. After analyzing thousands of historical queries, it knows which KPIs your team actually cares about, which dimensions they slice by, and which relationships matter for which questions. No manual definitions. No interviews. No guesswork.
Cartographer Engine
Ready to scan
6 AI agents • 4 hours vs 4 months • Automatic semantic discovery
Tables classified by their role: objects, events, mappings
KPIs inferred from aggregation patterns in real queries
JOIN paths mapped from actual usage, not just schema
Temporal granularity and cadence detected automatically
Dimensions surfaced from how the data is sliced in practice
Drill paths and significance signals synthesized across the graph
Multi-agent semantic discovery. Real query logs. Context graph in hours, not months.
Together with Layer 1, this completes your Decision Context Graph, an interactive map that becomes your organization's single source of truth for decisions.
Start from your North Star Metric, the one number that matters most, and see exactly how every metric connects, who owns it, and what drives it. Replace twenty disconnected dashboards with one navigable context graph.
Living Map
Ready to visualize
Revenue
Customers
AOV
Frequency
New
Returning
Price
Quantity
One living view • Real-time health • Gateway to all intelligence
Every organization has one ultimate measure of success. The Living Map starts there and decomposes it into contributing drivers.
Our AI validates that your KPI structure is Mutually Exclusive and Collectively Exhaustive, no overlaps, no gaps.
Every metric on the map has an owner, a person or team accountable for that number. No more finger-pointing.
Color-coded indicators show performance at a glance. Red, yellow, green, know instantly where to focus.
Navigate 200+ interconnected KPIs in a single view. Your complete Decision Context Graph.
Type any metric and the system generates a four-act story around it: the headline number, the drivers behind it, the metrics that move with it, and what to do about it. No analyst queue. No dashboard build. No waiting for someone to slice it the way you needed.
KPI Drilldown analyzes every dimension at once, surfaces patterns you would not have thought to ask about, and tells you which one to investigate first. Built on the context graph, so every claim is traceable back to the underlying data.
Click play to see the magic
MindPalace transforms a single metric into a 4-act data narrative with AI-powered insights.
4 acts • 30 seconds • Complete data story with AI insights
01
What's the number?
Current value, trend, AI summary
02
What's behind it?
Dimension breakdowns, contributions
03
What moves with it?
Correlations, relationships
04
So what?
Opportunities, alerts, actions
30 seconds. Any KPI. A full data story with statistical drivers.
Deep Analysis is a four-step investigation flow. You see value at every stage, and you decide what gets analyzed at each step. No black boxes. No wall of charts to make sense of afterwards.
The AI proposes hypotheses based on the actual patterns in your data. You select the ones worth testing, edit them to match your business, or add your own. The system runs the statistics and returns an executive-ready answer with p-values, effect sizes, and quantified business impact.
4 steps. ~30 seconds total. Executive-ready output.
4 steps. ~30 seconds total. Board-ready output.
Analyzing
Customer Lifetime Value (CLV)
-8% this quarter
What would you like to understand?
Find factors causing the -8% decline
Segments to analyze
No statistical jargon. Just three simple questions, WHERE, WHY, or HOW. MindPalace recommends the best one based on your KPI's current trend.
You stay in control
The AI proposes hypotheses from real patterns in your data. You decide which ones to run, edit them to fit your business, or add ones the model missed. Results stream in as each hypothesis completes, so you can act on the first finding without waiting for the rest.
Most funnel tools require you to define every stage by hand, then guess at the drop-offs. MindPalace discovers the journeys from the warehouse itself, ranks the biggest leak, and tells you why entities are falling out at that step.
Every business has flows: customers progress from signup to purchase to loyalty. Orders move from placed to shipped to delivered. Leads advance from qualified to closed. Flow Analysis discovers these journeys from your Cartographer metadata, identifies the biggest leak, and auto-drills into why. Cohort Analysis then asks: which groups of customers are succeeding through this flow, and which are failing?
Auto-discovered funnels. Biggest leak identified. Root cause explained.
6 entities, 14 segments, 8 temporal columns scanned
Signup → First Purchase → Repeat → Loyalty
Placed → Paid → Shipped → Delivered
Created → Qualified → Contacted → Won
Cartographer reads your warehouse metadata, entities, status columns, temporal fields, relationships, and suggests business process flows automatically. No manual funnel setup.
AI reads your Cartographer metadata and suggests business process flows, no manual funnel setup.
Automatically identifies the biggest drop-off and classifies severity: critical, warning, or healthy.
One click drills into the biggest leak using KPI Drilldown, get the 'why' without building anything.
Group entities by signup month, tier, or behavior, see retention heatmaps across time.
Compare metrics across groups: which customer segment has the highest lifetime value?
Cross-cut: run the same flow for different cohorts. See which group fails at which stage.
Auto-discovered funnels. Biggest leak identified. Root cause explained, under 15 seconds.
The foundation is built. The context graph is grounded. The map is alive. The next step: let you talk to it.
The Co-Pilot will bring conversational intelligence to your Decision Context Graph. Ask complex questions in natural language. Get contextual, data-backed answers grounded in everything Cartographer has learned about your business.
Built for human and AI agents alike. The context graph you build today will power the autonomous business operations of tomorrow.
"Why did revenue drop last week?" → Contextual, causal explanation
"Is our KPI structure MECE? What's missing?" → Structural validation
"What would happen if we raised prices 10%?" → Scenario simulation
MECE optimization suggestions
Automated strategic briefings
UNDER THE HOOD
The architecture is designed for the warehouses, security posture, and statistical workloads of real enterprise environments.
Python-powered analysis with scipy, statsmodels, pandas
P-values, confidence intervals, effect sizes, rigorous methods
React + TypeScript frontend
FastAPI Python backend
Supabase for auth/metadata
Claude AI for intelligence
THE DIFFERENCE
Faster matters. The bigger shift is what the system is actually doing on the other side of the question.
Most stacks return numbers. The Decision Context Graph returns the reasoning behind them: which driver moved, by how much, and why it matters, every time the same question is asked.
The architecture, the philosophy, and the failure modes we built around.
A Decision Context Graph is the missing layer between your warehouse and your decisions. Here is what it is, how we build one in four hours, and why it matters now.
Most AI-native BI tools let a language model calculate your business metrics. That is a category error. Here is the architecture we built instead, and why.
Data-driven decision making has become table stakes language. In practice, most leadership meetings still run on whoever argues best. Here is what actually goes wrong, and what would fix it.
Ready to go deeper? Our team will walk you through every layer of MindPalace and show you how it maps to your specific data challenges.