Case Study — YieldSphere

Execution Efficiency Dashboard

Increasing Successful Execution Throughput by 18% Without Added Infrastructure Spend

$4.5M in additional captured yield through real-time execution bottleneck intelligence

Industry

DeFi Yield Aggregator

Timeline

14 weeks

Team

5 engineers

Tech

Ethers.js + TimescaleDB + React

The Challenge

A DeFi yield aggregator executing reallocations across 3 chains was seeing a claimed 87% transaction success rate — but effective throughput after accounting for failed transactions, excessive slippage, and MEV losses was 23% below what the strategy models assumed. Capacity planning relied on quarterly manual transaction log reviews. No real-time visibility into execution bottlenecks, failure patterns, or MEV exposure existed at the transaction level.

Our Approach

How We Solved It

Execution Pipeline Instrumentation

Instrumented 48 key points across the transaction execution pipeline — mempool submission, gas price at inclusion, slippage realized, and MEV exposure per transaction — establishing the first real-time execution-quality data foundation the fund had ever had.

Bottleneck Detection Engine

Built a real-time bottleneck detection algorithm using queuing theory to identify the current constraint limiting execution throughput on each chain, updating every 30 seconds as network conditions change.

Execution Performance Analytics

Developed execution-level performance analytics that decompose the gap between assumed and realized throughput into attributable causes: failed transactions, gas underpricing, slippage, and MEV extraction — enabling targeted improvement actions.

Throughput Forecasting

An ML model predicts end-of-window execution success rate from early network-condition data, giving the operations team an actionable lead time to adjust gas strategy, batch timing, or route selection before a reallocation window closes.

Engineering Process

How We Built It

Real-Time Queuing Theory Application

The bottleneck algorithm implements Little's Law and queue-based throughput analysis on the live mempool and execution stream, identifying which stage is constraining system throughput at any moment.

TimescaleDB for Execution Time-Series

TimescaleDB continuous aggregates pre-compute hourly and daily execution summaries without impacting real-time query performance — analytical queries return in under 500ms despite the 48-signal, 30-second resolution data volume.

Configurable Alert Thresholds

Alert thresholds for gas price, slippage, and MEV exposure are configurable by the operations team within policy limits — giving operators ownership of execution strategy while maintaining risk boundaries.

Architecture Decisions

Key Technical Choices

Bottleneck as Dynamic, Not Static

Traditional execution capacity planning treats bottlenecks as fixed by design. Our real-time bottleneck detection revealed that the constraint shifts between gas pricing, mempool congestion, and slippage several times per day — a finding that invalidated all previous throughput-improvement assumptions.

Causal Attribution Before Correlation Analysis

We built execution-domain knowledge into the system (network condition → gas strategy → outcome causal model) before adding statistical analysis, avoiding the classic 'correlation without causation' trap in unguided ML on execution data.

Operator-Facing First, Management-Facing Second

The primary dashboard is optimized for the operations team making real-time execution decisions, not for management reporting. Management views are aggregations of operator-level data — not separately designed views.

Results

What We Delivered

18%

Execution Throughput Increase

$4.5M

Additional Yield Captured Annually

Zero

Added Infrastructure Spend

99.4%

Execution Data Capture Rate

Solution Blueprint

How It All Fits Together

Execution Instrumentation Layer

48 execution-pipeline signal points
Mempool monitoring
Real-time event stream

Analytics Layer

Real-time bottleneck detection
Execution-quality decomposition
End-of-window throughput forecasting

Operations Layer

Operator execution dashboard
Gas & slippage exception alerts
Execution performance reports

Lessons Learned

What We Improved

The Dynamic Bottleneck Discovery Changed Everything

The finding that the execution constraint shifts between gas, mempool congestion, and slippage several times per day invalidated prior assumptions behind years of execution-strategy tuning. The most valuable output of the project was this insight, not the dashboard.

Operators Need Actionable, Not Informational

Early dashboard prototypes showed too much data. Operators needed 3 things: current bottleneck, severity, and recommended action. Removing 80% of the initial dashboard metrics increased adoption by 4x.

Throughput Improvement Is Strategic, Not Just Technical

The platform identified 12 distinct execution-bottleneck causes. 8 required no new technology — they were gas-strategy, batching, and route-selection policy changes. The technology made the problem visible; the operations team solved it.