KPI Tree - Decision Intelligence for Humans and AI Agents

Metric moves. Owner notified. Action tracked. Impact verified.

KPI Tree starts alongside your BI tool, not instead of it. Your dashboards stay, and this is the causal, owned, verified layer on top. Over time, most teams end up retiring their legacy BI tool, once KPI Tree is the system of record for how decisions get made.

One causal model, not a folder of dashboards

Every relationship is a directed driver edge with confidence attached. AI drafts it, your team corrects it, your data decides. It is the ground truth your AI agents inherit.

One query per metric, watched continuously

Comparisons and rolling totals are precomputed and every aggregation runs in our engine, so the warehouse bill stays flat. Outliers, gaps and staleness are tracked automatically.

Revenue · Liverpool St

15%

£43,452

vs £51,100 last period

Drivers with confidence, not narrative

Correlation alone is not causation, so every edge is run daily through proprietary ML models and statistical tests, from Pearson correlation to Granger causality, Benjamini-Hochberg corrected. Then the people who know the business prune what the statistics cannot see.

Driver

Conversion rate

23%

Granger-causal · lag 3d · q < 0.05

Outcome · 58% contribution

Revenue

15%

Pushed to a named owner, verified against the metric

The Accountable owner is notified the moment the metric moves, with the driver attached. The action is tracked against the metric, the impact is verified, and the objective it serves updates on the strategy map. Then the model learns.

KPI Tree app · 09:14

Revenue is 15% below target. Conversion rate is the primary driver (Granger-causal at lag 3d). @Sarah Chen you are Accountable.

View driversCreate task

See cause and effect across your entire business

Decompose your North Star into every lever your teams control. Every relationship has a direction and a measurable strength. The Five Whys, pre-answered.

0:00

Explore the whole platform

See the system. Ask it anything. That's explainable AI.

Answers where the team already works

@kpitree in Slack. Wake up to a briefing. Same governed answer, every surface.

Emma #revenue
@kpitree what's driving the drop this week?

KPI Tree app · 09:14

Conversion rate, down 23% (Granger-causal, lag 3d). David Mitchell is Responsible.

Mention it in any channel

What is driving the change, who owns it, chart attached, answered from the same causal model.

The Slack assistant

KPI Tree App 08:00

Good morning, Sarah. Revenue is tracking £31.5k/day behind target, down 13.1% MTD. The gap is concentrated in the online store and Liverpool Street: checkout failures and a staffing gap, not demand.

Online store -9.8%. Payment errors since Monday's release. Granger-causal, q < 0.01.

Liverpool Street -40.9% MTD. Two open shifts behind it. David is Responsible.

Checkout fix verified at +£32k. The EU store shows the same error pattern.

Do next: 1. Roll back the checkout release (you, today) · 2. Backfill Liverpool Street (David) · 3. Apply the fix to the EU store.

Create tasksOpen briefing

Start the day with what needs you

A daily briefing of the metrics you own: what moved, why, and whether yesterday's action worked, in Slack, email or WhatsApp.

The daily briefing

Automation

When a metric misses, the workflow fires. Humans approve. Agents execute.

Triggers watch the metrics themselves: a missed target, a crossed threshold, a metric gone silent. Build the rest visually, chaining AI agents, approvals, Slack, email and tasks into flows that escalate up your real org chart when nobody acts. Every run keeps its full history.

The native agents run on your metric data with your permissions, on Claude, OpenAI or Gemini, with your own keys and spend caps if you want them.

Canopy Agent Workflows Canopy Agents

Trigger

On target missedMetrics

Run action-plan agentAgents

Wait for Sarah's approvalUtilities

Approved

Escalate to managerOwnership

For agents

Your semantic layer says how metrics are calculated. Canopy adds what drives them, who owns them, what actually worked, and the plan they serve.

Every other context layer helps agents answer. Canopy is the only one that closes the loop: each metric carries its driver, its owner, whether the last action actually worked, and the strategic bet riding on it. Canopy reads the long-term plan alongside the day to day, so agents move the business, not just describe it, and precomputed context turns ten queries per metric into one.

Meet Canopy

Rooted in your warehouse. Governed on every surface.

Enterprise security, by design

SOC 2 Type II, SSO and directory sync, audit log streaming, and an architecture that does not store your data.

Security

Availability

Confidentiality

Trust Center

Continuous Monitoring

Controls Verified

CREST Penetration Testing

Warehouses connect natively, SaaS tools through MCP

One sync query per metric, comparisons precomputed, so the warehouse bill stays flat. Anything with an MCP server becomes a source, no ETL project.

The whole platform, at a glance

Metric trees, root cause, ownership, objectives and initiatives, verified impact, subscriptions, data quality and the compute engine. One page, everything KPI Tree does.

The system of record for how your company makes decisions.

Understanding changes behaviour. Dashboards don't. Understand and automate every decision, with a name on every number.

Book a demo⌘B

The manifesto

People change when they see the system, see their place in it, and see what moves when they do.

Read why we built KPI Tree

Common questions

What is KPI Tree?

KPI Tree is the system of record for how your company makes decisions. It maps your metrics into causal trees that connect every outcome to the drivers beneath it, gives every metric a named owner with clear RACI, tracks the actions taken against the metric they were meant to move, puts objectives and key results on the same causal tree, and runs automated root cause analysis when a metric shifts. Teams see not just what changed, but why, who owns it, and what to do next.

What is a business context layer?

A business context layer gives AI agents your organisation's model, owners, definitions and history, so they act with real context instead of guessing. Canopy is KPI Tree's business context layer. It is distinct from a semantic layer, which defines metric SQL and definitions over the warehouse. Your warehouse and semantic layer are the sources that the context layer sits above, adding who owns each metric, how it behaves, and what has already been tried.

How is KPI Tree different from a BI tool like Tableau or Looker?

Tools like Tableau and Looker are built to visualise data on dashboards and charts, leaving you to interpret what a number means. KPI Tree is built to drive decisions. It connects each metric to its causal drivers, its named owner, and the actions meant to move it, then verifies whether those actions worked. A dashboard tells you a number went down. KPI Tree tells you why it moved, who owns it, and what to do next.

Does KPI Tree prove causality?

Yes. KPI Tree proves causality with proprietary ML models and statistical tests such as Pearson correlation, lagged cross-correlation, partial correlation and Granger causality, not LLM pattern matching. When a metric moves, it tests candidate drivers against that evidence, so you can trust the relationships in your metric trees rather than guessing at correlations.

Is KPI Tree SOC 2 compliant?

Yes. The KPI Tree platform is SOC 2 Type II. The Type II report is packaged on Growth plans and above, so customers on those plans can review the full documentation as part of their own security assessment.

Beyond insight. Personalised action plans for every employee.

Meet Canopy, the business context layer on top of your semantic layer.