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 UsersAcross production deployments
17+Portfolio Company DeploymentsSame framework, different data
4-10 WeeksTo First Production AppNot 6-12 months
80-90%Cost Reductionvs. 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

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

  • Standardized predictive modeling
  • Cross-portfolio visibility
  • Data isolation & compliance
  • Instant executive roll-ups
17+Unified Portfolio Entities
Real-timeForecasting Engines
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.

MinutesPer document (was hours)
13Specialized 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
SecondsQuery response time
NaturalLanguage queries
4 wkTo production
A sample forecast review app: revenue plan, actual and forecast by month and region, with a chat that explains a variance and a rules check.
An app of this kind: forecast against plan by region, with the answer checked against your rules. Illustrative example, sample data.

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.

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Or email us: accounts@evolveml.io