AI Recommendation Engines

Overview

What we deliver

We build AI recommendation engines that surface relevant products, content, and offers based on user behavior and context.

We design recommendation engines that help users find what they want and help businesses increase basket size, watch time, or engagement. Our work begins with a clear definition of the recommendation goal, whether that is cross-sell, related content, or personalized homepages. We review your data sources, including catalogs, user events, and purchase history, and select models that fit the use case. We build pipelines that score items in real time or in batch, then expose results through APIs that your front-end teams can consume. We A/B test variations against control groups to measure lift. After launch, we monitor model drift, retrain on fresh data, and add new signals as your catalog and audience grow. The result is a recommendation system that adapts to behavior and produces measurable revenue and engagement gains.

Fit Check

Built for teams like yours

Who it's for

Ecommerce platforms with large catalogs
Media and streaming services
Marketplaces matching buyers and sellers
SaaS apps personalizing in-product content
Subscription brands driving repeat purchases

Pain points we solve

Low click-through on product suggestions
Generic homepages for every visitor
Manual merchandising taking too long
Missed cross-sell and upsell revenue
Cold start problems for new users or items

What's included

Capabilities

Everything we cover in this engagement.

Use case scoping and goal definition
Data pipeline and event tracking setup
Collaborative and content-based models
Real-time and batch scoring
API and widget integration
A/B testing and experiment design
Model monitoring and retraining
Cold start and fallback strategies

How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Discovery

We define goals, data sources, and target placements.

02

Data prep

We clean catalogs, events, and user data for training.

03

Modeling

We train and evaluate models against your objectives.

04

Integration

We expose recommendations through APIs and widgets.

05

Test and refine

We run A/B tests, monitor metrics, and retrain.

What you get

Deliverables & outcomes

What you get

Production recommendation API
Data pipelines for events and catalog
Trained models with evaluation reports
Front-end widgets or components
Experiment results and lift analysis
Monitoring and retraining playbook

Outcomes you can expect

Higher click-through on recommendations
Increased average order value
More time spent on content
Better conversion from new visitors
Less manual merchandising effort

Timeline

8 to 12 weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

Python, TensorFlow, AWS Personalize, Vertex AI, Algolia Recommend

KPIs we track

Click-through rate, conversion rate, average order value, revenue per session, model accuracy

Client stories

What clients say

"

We had been prototyping an AI quoting agent for nine months and could not get it past demo quality. They came in, scoped a real eval set, swapped our retrieval layer, and added guardrails for the edge cases that kept burning us. Went live in seven weeks. It now handles 41 percent of inbound quote requests without a human touching them.

Kyle A.

"

We were paying three agencies and a lifecycle freelancer to argue over attribution. RevoraOps absorbed all of it in 30 days, killed our worst-performing Meta ad sets, and rebuilt the welcome flow from scratch. CAC dropped 31 percent in the first full month. Honestly the relief of having one weekly call instead of four was worth it alone.

Megan W.

Proof