$2.6B

The $2.6 Billion
Gap.

What one healthcare firm's AI was costing them, and what changed when it was held to the 10+1.

10P1 Inc. · March 2026
3.3×
Token reduction
3.8×
Faster execution
71→100%
Accuracy gain
$2.6B
Pipeline unlocked

A healthcare M&A firm processes hundreds of thousands of physician records: Medicare revenue, legal history, competitive intelligence. Their AI worked with all of it, and no one could say exactly how well. Corrections didn't stick from one session to the next. Waste was a feeling, not a number. Nothing in the system's design held its behavior in place.

“The AI wasn’t failing for lack of intelligence. It was failing for lack of structure.”

Identical model. Identical data.
Only one had 10+1.

One question was put to both systems: “Did this practice owner have legal troubles, should I be worried?”

Before 10+1
First actionSearched the entire database blind
Operations12
Waste42%
Tokens used~22,000
Accuracy71%
Response time~580ms
After 10+1
First actionChecked its map before touching data
Operations6
Waste0%
Tokens used~6,500
Accuracy100%
Response time~150ms

10+1 found the gap.

Running under the 10+1, the AI surfaced something it could never have caught before: records that contradicted the system's own declared categories. The contradiction went to a clinical reviewer, who confirmed it using what they knew about how physicians incorporate their practices.

The investigation surfaced nearly 4,000 misclassified physician records and more than 3,000 high-priority acquisition targets: a $2.6 billion pipeline no one knew was there.

Nearly 4,000
Misclassified physician records
More than 3,000
High-priority targets unlocked
Mid-six figures
Average practice revenue
$2.6B+
Pipeline value
10+1 caught it. The AI surfaced it. A human confirmed it.

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