Book a Free Call
CLOSE
Predictive AI Models

See the future of
your revenue.

We build custom machine learning models that forecast revenue trends, predict customer churn, and surface hidden growth opportunities — trained on your data, not industry averages.

Forecast My Growth
Why Businesses Fly Blind on Revenue

You Are Making Million-Dollar Decisions on Last Quarter's Data.

Spreadsheet forecasts are guesses dressed up as strategy. By the time you see a trend in your historical data, you have already missed the opportunity — or already lost the customers. Predictive AI changes the timeline: instead of reacting to what happened, you act on what is about to happen. The companies that win the next decade will not be the ones with the most data. They will be the ones who trained models to extract signal from it.

Before

“We cannot predict which customers are about to churn”

After EVOLVRX

Churn prediction model trained on your behavioral data — identifies at-risk accounts 30-90 days before they leave, so your team can intervene.

Before

“Our revenue forecasts are always wrong by 20-40%”

After EVOLVRX

Custom forecasting model that learns your business seasonality, market patterns, and leading indicators — producing forecasts with measurable confidence intervals.

Before

“We have years of data but no idea what it is telling us”

After EVOLVRX

Exploratory ML analysis that surfaces non-obvious patterns, correlations, and anomalies hidden in your historical data — turning raw records into actionable intelligence.

Before

“We tried an AI tool but it could not learn our business context”

After EVOLVRX

Models trained specifically on your data, your segments, and your business logic — not a generic tool with a generic model. Context-aware from day one.

The Full Picture

From Raw Historical Data to Decisions You Can Act On.

Not dashboards — predictive intelligence that changes how you operate.

Revenue Forecasting

Time-series models that learn your business seasonality, market cycles, and growth patterns. Weekly and monthly revenue forecasts with confidence intervals — not point estimates, but ranges your planning team can work with.

01

Revenue Forecasting

Churn Prediction

Classification models that score every customer on churn risk using behavioral signals — login frequency, support tickets, feature usage, and payment patterns. Flags at-risk accounts 30-90 days before they cancel.

02

Churn Prediction

Lead Scoring

ML models trained on your historical conversion data that score inbound leads by likelihood to buy, deal size, and sales cycle length — so your sales team works the right leads first, not just the newest ones.

03

Lead Scoring

Demand Forecasting

Inventory and resource planning models that predict demand spikes before they happen. Seasonal adjustments, promotional lift modeling, and external signal integration — so you are never caught understocked or overstocked.

04

Demand Forecasting

Anomaly Detection

Unsupervised models that continuously monitor your operational metrics and alert you when something deviates from normal — fraud patterns, system degradation, sudden drops in engagement — before they escalate.

05

Anomaly Detection

Customer Lifetime Value

LTV prediction models that estimate each customer's long-term value from early behavioral signals. Allocate acquisition budget to the segments that will actually be profitable, not just the ones that convert fastest.

06

Customer Lifetime Value

Price Optimization

Elasticity models that predict demand response to pricing changes across your product catalog. Data-driven pricing that maximizes revenue without cannibalizing volume.

07

Price Optimization

Attribution Modeling

Multi-touch attribution models that correctly credit each marketing touchpoint in the path to conversion. Move budget from last-click winners to the channels actually driving decisions earlier in the funnel.

08

Attribution Modeling

How It Works

From Raw Data to Running Production Models.

A five-step system built to take your raw data from audit to production model. Each step compounds on the last.

01 / 05
Data Audit & Scoping
01

Data Audit & Scoping

We assess what data you have, where it lives, its quality, and what prediction problems are solvable with it. You get an honest view of what is possible — including what your data cannot yet support.

Feature Engineering
02

Feature Engineering

Raw data is rarely model-ready. We clean, transform, and engineer the features that give models predictive signal — handling missing data, encoding categorical variables, and creating derived signals from raw records.

Model Development
03

Model Development

Multiple model architectures tested against your data — regression, gradient boosting, neural networks, and time-series models where appropriate. The model that performs best on your specific problem goes forward.

Validation & Calibration
04

Validation & Calibration

Backtesting against held-out historical data. Model performance benchmarked against your current forecasting approach. You see exactly how much more accurate the model is before it goes anywhere near production.

Deployment & Integration
05

Deployment & Integration

Model served via API or embedded in your existing workflows. Predictions surfaced where your team actually makes decisions — CRM, dashboard, email alert — not in a separate tool nobody checks.

What You Get

What Happens When You See Around Corners.

Five outcomes that stack — and compound — once your predictive models are actually running.

Outcome 01

Revenue Forecasts Your Finance Team Can Trust

Forecasts with measurable accuracy — not gut feel. When the model says Q3 revenue will be £2.1-2.4M, it has been backtested and calibrated against your historical actuals. Your planning cycles become faster and less contentious.

Outcome 02

Customers Retained Before They Decide to Leave

A churn prediction model that fires 60 days before the subscription ends gives your customer success team time to intervene. You stop losing accounts to problems you never knew were building.

Outcome 03

Sales Team Efficiency That Compounds

When your sales team works the highest-probability leads first, conversion rates go up and time-per-deal goes down. Lead scoring ML compounds: it gets more accurate as more conversions happen.

Outcome 04

Data Assets That Get More Valuable Over Time

Unlike a software tool, a well-maintained ML model improves as you collect more data. You are building an asset — not paying for a subscription that delivers the same outputs regardless of your specific history.

Outcome 05

Decisions Made on Signal, Not Noise

When you can separate the signal from the noise in your operational data — what actually predicts outcomes versus what just correlates with them — every decision gets sharper. The compounding benefit is a business that learns faster than its competitors.

Scroll
Our Stack

The Stack Behind the Models

Model development, feature engineering, classical ML

Gradient boosted tree models, tabular data, churn/lead scoring

Deep learning models, neural network architectures

Time-series forecasting, seasonality decomposition

Data transformation, feature pipeline, training data prep

Data warehousing, large-scale historical data access

Model versioning, experiment tracking, deployment registry

Model serving, prediction endpoints, real-time inference

Pipeline orchestration, scheduled retraining, data ingestion

Managed ML training, AutoML, scalable model hosting

FAQ — Your predictive AI questions, straight answers.

Still have questions? We can help!

Talk to our team about your data readiness and ML goals.

Support Center
FAQ FAQ

Your predictive AI questions, straight answers.

1

It depends on the problem. Churn prediction typically needs 12-18 months of behavioral data. Revenue forecasting benefits from 2+ years of history with clear seasonality patterns. We assess your data in the discovery phase and tell you honestly what is and is not solvable.

2

We provide backtested accuracy metrics on held-out data before deployment — so you see exactly how the model would have performed against your actual history. Accuracy varies by problem: churn models typically achieve 75-90% precision; revenue forecasts within 5-10% MAPE on stable businesses.

3

A focused predictive model — churn, lead scoring, or revenue forecasting — typically takes 6-12 weeks from data audit to production API. More complex, multi-model systems take longer depending on data readiness and integration requirements.

4

Not necessarily. If your data is in a standard analytics warehouse (BigQuery, Snowflake, Redshift) or CRM (Salesforce, HubSpot), we can access what we need. For messier data situations, we scope the data engineering work as part of the project.

5

Yes, if retrained on new data. We build retraining pipelines so models update regularly as new conversions, cancellations, and outcomes come in. Models deployed without retraining drift as your business evolves — ours do not.

6

We integrate predictions where your team actually works — CRM scores visible to sales reps, churn risk alerts in your customer success platform, revenue forecasts in your BI dashboard. The goal is predictions at decision time, not a separate tool.

7

Yes. We are stack-agnostic and integrate with BigQuery, Snowflake, Redshift, dbt, and most BI tools. We work with what you have rather than requiring a full stack replacement.

8

Yes. All model code, training pipelines, and documentation are yours. We hand over everything — you can retrain, modify, and operate the models independently after delivery.

9

Off-the-shelf tools use generic models trained on industry data. Our models are trained on your data, your customer segments, and your business patterns. The performance difference is significant — especially for churn and LTV, where context is everything.

10

Book a data readiness call. We will assess what data you have, identify the most valuable prediction problems, and give you a scoping estimate for the first model within one week.

Ready to Stop Reacting and Start Predicting

Let's Build a Model That Sees What Your Data Already Knows.

Book a free data readiness call. We will assess your data, identify the most valuable prediction problems for your business, and give you a scoping estimate — no commitment required.

Book a Data Readiness Call Ask Us Anything