RESULTS

Proof,
Not Promises.

Every engagement has a 90-day measurement window. If we don't prove value, we retire it cleanly. Here's what we've delivered.

15+
Daily Active Users

Across production deployments

17+
Portfolio Company Deployments

Same framework, different data

4-10 Weeks
To First Production App

Not 6-12 months

80-90%
Cost Reduction

vs. traditional data science teams

CASE STUDY

Lead Intelligence for a National Services Company

Problem

The sales leadership team managed a growing pipeline with spreadsheets and gut feel. Prioritization decisions were made by instinct. Board reporting required days of manual assembly from four systems.

Delivered

A custom analytics platform connected directly to their existing CRM and project management systems. ML-powered lead scoring, conversion analytics, and performance tracking.

Key Results

  • check_circle Measured ROI within the first month
  • check_circle Payback in under 2 months
  • check_circle 40% increase in lead conversion accuracy
  • check_circle Automated weekly board-ready reports
  • check_circle Unified 4 siloed data streams
Timeline Week 1-4: Discovery + first version
Refinement Week 4-8
Rollout Month 3: Full Scale
Lead Intelligence illustration
Measured results illustration
17+

Unified Portfolio Entities

Real-time

Forecasting Engines

Predictive forecasting illustration
CASE STUDY

ML-Powered Forecasting Across 17+ Companies

The Problem

A PE firm needed consistent revenue visibility across a diverse portfolio. Each company reported differently. Operating partners assembled cross-portfolio views manually, leading to delayed insights and human error.

The Solution

An ML-powered forecasting platform that standardizes revenue predictions across 17+ portfolio companies. Multiple model comparison. Unified view for operating partners.

query_stats

Standardized predictive modeling

group_work

Cross-portfolio visibility

security

Data isolation & compliance

speed

Instant executive roll-ups

CASE STUDY

Intelligent Document Analysis for a Growth Equity Firm

The Problem

Legal team manually reviewed NDAs against internal guidelines — hours per document, inconsistent rule application, critical provisions missed under deadline pressure.

What We Built

A 4-layer AI analysis pipeline that reads each document, compares against firm-specific rules, identifies deviations, and generates a redlined version with tracked changes — in minutes.

Minutes

Per document (was hours)

13

Specialized AI workers

70%

Cost reduction per review

100%

Rule consistency

CASE STUDY

AI-Powered Analytics Across 24M+ Support Tickets

The Problem

A telecom provider was drowning in ticket data across multiple systems. No way to detect patterns, predict escalations, or answer operational questions without weeks of manual analysis.

What We Built

A natural language analytics platform that lets operations teams ask questions of 24M+ tickets in plain English — with instant answers, trend detection, and proactive alerts.

24M+

Tickets searchable

Seconds

Query response time

Natural

Language queries

4 wk

To production

What Our Clients Say

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"EvolveML didn't just give us a tool; they gave us clarity. Within 6 weeks, we moved from guessing our pipeline conversion to predicting it within a 3% margin of error. The speed of execution is unlike anything we've seen from consultants."

Michael R. VP of Sales Operations, National Logistics Firm
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"Managing 17 different data architectures was a nightmare until EvolveML built our unified forecasting engine. We now have a single source of truth for the entire portfolio that updates automatically every 24 hours."

Sarah J. Operating Partner, Private Equity Group

Ready to See Results Like These?

We measure ROI explicitly. If it doesn't prove value in 90 days, we retire it — no questions asked.

Schedule a Conversation Or email us: accounts@evolveml.io