Intelligence ROI

How we measure decision quality, track compound intelligence, and trace the receipts on operational ROI.

Intelligence ROI traces the loop: agents surface findings, findings drive moves, moves shift dimension scores. The view shows how much your team is moving and how often the loop closes.

Intelligence ROI

We go past measuring activity. We trace whether your product team is getting smarter over time by tracking decision quality, recommendation accuracy, and the compound intelligence flywheel.

Hit Intelligence > ROI to access this view.

What we measure

ROI runs on the compound flywheel: agents surface findings, findings drive moves, moves shift dimension scores, dimension lifts compound into a higher composite.

Metric	Read
Actions dispatched	Total moves sent by agents (Linear issues, Slack messages, re-scores) in the period
Dimensions improved	Count of dimensions that scored higher on re-score after a move was dispatched
Score delta	Composite score move across all products in the period
Loop closure rate	Percentage of dispatched moves with a verified score lift linked to them
Velocity	Score points gained per week, averaged over the last 30 days

Decision intelligence score

The decision intelligence score (0-100) is a longitudinal read that ties your team’s launch decisions to actual outcomes. Where dimension scores measure current capability, the DI score measures whether acting on recommendations actually moves your operations.

Three components

Component	Weight	Read
Outcome quality	40%	Weighted hit rate of recommendations. Larger lifts count more than small ones.
Decision velocity	30%	How fast your team acts on recommendations. Faster move equals higher score.
Learning acceleration	30%	Whether your recent hit rate is improving over your historical average. Positive trend means the system is learning.

Confidence levels

Cycles completed	Confidence	Read
< 5	Low	Early stage. Score is directional but needs more data.
5-9	Medium	Developing. Patterns are emerging.
10+	High	Reliable. Score reflects real decision-quality trajectory.

The DI score compounds: the more cycles your team completes (Score, Connect, Correlate, Act, Learn), the more reliable the read.

How loop closure works

A loop is closed when:

1. An agent dispatched a move targeting a specific dimension
2. A re-score happened after the move was dispatched
3. That dimension’s score lifted on the re-score

Loops that are still pending (move dispatched but no re-score yet) read as open in the flywheel view.

To maximize loop closure, re-score products after your team addresses a recommendation. Even a quick URL re-score captures recent moves.

Time ranges

Use the period selector to read ROI over:

• Last 7 days for recent agent activity
• Last 30 days for the monthly view (aligned with billing and credit resets)
• Last 90 days for the quarterly view (planning, investor updates)
• All time for the full account history

Knowledge graph

Behind the scenes, we build a decision graph connecting signals, scores, decisions, and outcomes across cycles. The graph powers compound intelligence queries:

• What caused this score move? Trace from an outcome back through the recommendation, the agent that produced it, and the signal that informed it.
• Which recommendations worked? Follow paths from recommendations through to measured outcomes.
• How did human calls affect outcomes? Every approval, rejection, and Teach DAC reaction lands as a node in the graph.

Node types

Node	What it represents
Signal	An adapter event (PR merged, issue completed, deploy shipped)
Score snapshot	A point-in-time dimension or composite score
Recommendation	A coaching move surfaced by the intelligence engine
Outcome	A measured score move after a recommendation was acted on
Human decision	An approval, rejection, Teach DAC reaction, or outcome attribution

The graph grows with every cycle. Over time, it becomes a queryable history of your team’s decision patterns and their consequences.

Peer benchmarking

When enough teams have scored, we compute anonymized peer benchmarks by segment (company stage, team size, industry). Your coaching observations carry benchmark comparisons:

“Your score of 2.1 reads below the peer median of 3.2 (25th percentile across 150 teams). Closing this gap could move your composite materially.”

Benchmarks compute nightly from the aggregate scoring database. Your individual scores are never shared or identifiable.

Product breakdown

The ROI view breaks down by product, showing:

• Which products have the most active flywheel loops
• Which products have the highest score velocity
• Which products have open (unverified) loops that need a re-score

Reading the charts

Score velocity chart: weekly composite deltas per product. Upward trajectory means your team is moving faster than the platform is raising the bar.

Actions dispatched by type: breakdown of Linear issues, Slack messages, re-scores, and coaching recommendations. Skew toward re-scores suggests agents are catching regressions; skew toward Linear issues suggests proactive movement.

Dimension heat map: which of the 27 dimensions are lifting most consistently across all products. Strong dimensions (4/4 across multiple products) signal systemic organizational strength.

Measuring compound ROI

The compound flywheel on the Agent Studio dashboard traces real operational impact.

Metric	Read
Completed cycles	Full Score, Connect, Correlate, Act, Learn revolutions
Accuracy trend	Recommendation hit rate per period (monthly)
Intelligence depth	How much the system’s recommendation precision has moved
Score velocity	Composite move per month

The flywheel

Each revolution makes the next sharper.

1. Score assess current maturity
2. Connect adapter signals ground-truth the assessment
3. Correlate agents surface cross-function patterns
4. Act recommendations become moves (Linear tickets, coaching)
5. Learn outcomes are measured and attributed

Teams with 10+ completed cycles see materially better recommendation accuracy than teams in their first cycle. That’s the compound moat: a competitor can copy the framework, but they can’t copy 12 months of your team’s decision intelligence data.

Use in investor updates

The ROI view is built to be screenshot-ready for investor and board reporting. The headline metrics (score delta, loop closure rate, velocity) quantify your team’s continuous improvement cadence in a format that lands beyond subjective self-assessment.

Related

Agent Studio

Configure agents and manage the compound flywheel.

DAC

Financial attribution and evidence citations in coaching.

Peer benchmarks

Read your position relative to peers.

How scoring works

Signal-score blending and the 27-dimension methodology.