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Decision intelligence report

Commercial data

Pipeline coverage depends on fewer deals than the headline suggests

Sales pipeline — synthetic demoOctober 25, 2026
Executive summary

What this dataset says

The supplied probabilities imply 158,700 in weighted value from 271,000 in face value. Lumen alone represents 66.5% of that weighted value. Values retain the source currency; no currency is inferred.

Face value
271,000

Source currency units

Weighted value
158,700

Σ(value × probability)

Top-deal exposure
66.5%

Lumen

Effective deal count
2.0

1 / concentration index

Evidence

Findings supported by the data

01−66.5%

Stress-test a single deal slipping

Removing Lumen from the current period reduces weighted coverage to 53,100. This is a scenario, not a forecast that the deal will fail.

02HHI 0.494

Deal count overstates diversification

4 rows have the same concentration as 2.0 equally weighted deals. Index = Σ(weighted-value share²). Validate one row per opportunity before treating rows as distinct deals.

03Sensitivity, not prediction

Probabilities need calibration

These are user-supplied probabilities, not validated win rates. There is no quota, close-date horizon, or win/loss history here, so no attainment forecast or confidence interval is justified.

Composition

Weighted value by account

Lumen105,600
Northstar25,500
Atlas25,200
Fieldwork2,400
Action plan

Recommended next steps

1

Review the evidence before acting

Use the calculations, scenarios, and limitations in the executive summary. Validate denominators and row grain before making decisions.

Method & scope

How to use this report

Analysis performed

Patlr profiled 4 rows and 6 source fields, detected 4 record groups, and added 11 analysis-ready fields while preserving every original column.

Interpretation boundary

Results describe the submitted records and depend on their coverage and accuracy. Associations and anomalies should be validated against source context before decisions are made.

Evidence-only mode

Patlr does not add external facts. AI-assisted classifications are retained only when paired with an exact quote from the submitted text; unsupported output is left blank. Interpretations should still be validated before decisions.