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Execution Timelines

The Proof is in the
Execution.

We provide a direct window into the Buffaly runtime. These are reconstructions of actual operational sessions showing Buffaly handling real healthcare work, analyzing massive datasets, and learning at the speed of production.

Autonomous Learning Arc

From Discovery to
Executable Code.

Watch Buffaly take on a task it could not predict beforehand, solve it in context, retain the method as a reusable skill, and then turn that method into native ProtoScript code. That is how ad hoc work becomes deterministic execution.

No Restart Required

The system internalizes logic and generates its own reusable capability in real time.

79.7% Lower Task-Specific Cost

Task-specific cost drops from 24,924 to 5,065 - a dramatic 79.7% reduction.

View Learning Demo
Runtime: FairPath_Learning_Session
OPTIMIZING...
Step 01. Discovery
Autonomously completes a novel task via tool usage.
Step 02. Reusable Skill
Method retained as a reusable skill that Buffaly can invoke again.
Step 03. Native Code
Method compiled to ProtoScript for deterministic execution.
[SemanticProgram.Deterministic]
function CalculateEligibility(Patient p) {
    // Now executing as native code in your VPC
    return p.Scores.Filter(s => s.Value >= 3).Count();
}
Global Orchestrator: HHS_Analysis_Log
FILE_SIZE: 11.2GB
# Analyzing 11GB Medicaid Provider Spending...
> Task: Segmenting 284k remote-care billing rows. [DONE]
> Task: Applying OIG statistical pattern risk scoring. [DONE]
> Task: Enriching 2,682 NPIs with Geocode context. [DONE]
Analysis Complete
TIME: 1 evening
Extreme Data Scale

11GB of Public Claims
Analyzed Overnight.

Watch Buffaly take a massive HHS data dump and turn it into strategic intelligence. In a single evening, it built a custom analysis pipeline to find anomalous provider behavior.

11.2GB
Source CSV
284k
Filtered Rows
View Medicaid Demo
Production-Ready ROI

15k Patients for
Less than $13.

Buffaly took a real clinical archive, built and ran its own HL7 pipeline during the session, imported 15,075 patient records, and turned the result into a scored, payer-aware opportunity set for downstream action.

  • HL7 pipeline built during the session
  • 15,075 patient records imported
  • Scored, payer-aware output pipeline
View HL7 Demo

HL7 Workflow Snapshot

Imported Records
15,075
Imported Patient Records
Estimated Cost
$12.70*
Session Token Cost
HL7
ICD
Rx
* Estimated API-equivalent cost while building the import and qualification pipeline in a single session.

The Proof is in
Your Data.

Generic demos only go so far. We recommend a 30-day pilot on one of your existing, high-friction healthcare workflows.