What is the difference between AI strategy and AI integration?

Strategy decides what's worth building. Integration starts with a working product and ends with an AI feature inside it. That means the model, its data, what the user sees, and the checks that keep it honest after launch. Most of it takes three shapes: LLM features like assistants and chat, the pipelines that keep them accurate, and on-device AI where privacy or latency rule out a server. And when the user is your own team instead of your customers, that's its own page: internal tooling and workflow automation.

Can you add AI to a product that's already live?

That's most of what we do. The work is in deciding where the model runs and how its output gets judged before a user sees it. It runs on the device when latency, battery, or privacy matter, and on a server when the model's too big. The newest on-device surface we build for is smart glasses. We ship through your release process, then instrument the feature so you can tell it's still working a quarter later.

What kind of AI integration work has L+R actually shipped?

Mostly LLM features inside products that already have users: conversational coaches, engines that keep a brand’s content in its own expert voice, and LLM-curated reporting, among others. One example: for Unilever, the Slimfast Coach combines 253 messages into 4.4 billion unique sequences. Have an integration in mind? Get in touch.

AI Transformation

AI Integration Services for the product you already ship

Most AI work doesn't start from scratch. It lands in a product that already has users, a release cycle, and a team on call when the model gets it wrong.

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About this service

We put AI and machine learning into products people already use, from LLM features to the pipelines that keep them accurate after launch. If you're still choosing what to build, start with AI strategy and roadmapping.

Explore The AI And Machine Learning We've Put Into Live Products.

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ELC Learning Hub

Scaling employee learning across a global beauty workforce

ONYX

Scaling brand creation with an identity system built for agentic workflows

Slimfast® Coach Chatbot and Health Tracker

Guiding health tracking through a conversational AI coach for Slimfast

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Warner Bros. Advanced Narratives

Helping entertainment leaders explore new models for AI-powered storytelling

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Neural Magic

Positioning high-performance AI infrastructure with a clearer digital brand system

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Ironic Reports

Helping wedding planners anticipate microclimate risk with AI-powered intelligence

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Pinecone

Sharpening a category-leading AI brand without losing its core identity

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Clients In And Around AI

These teams range from global consumer brands to companies building AI infrastructure.

Where We've Put AI Into Production

Where AI integration lands first depends on the data a business already has.

How We Integrate AI Into A Live Product

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L+R starts where AI makes your product better, then ships it through the release process you already run.

AI Integration Services: Questions We Get Asked

Where the model runs, how it's judged, and what we've shipped.

Strategy decides what's worth building. Integration starts with a working product and ends with an AI feature inside it. That means the model, its data, what the user sees, and the checks that keep it honest after launch. Most of it takes three shapes: LLM features like assistants and chat, the pipelines that keep them accurate, and on-device AI where privacy or latency rule out a server. And when the user is your own team instead of your customers, that's its own page: internal tooling and workflow automation.
That's most of what we do. The work is in deciding where the model runs and how its output gets judged before a user sees it. It runs on the device when latency, battery, or privacy matter, and on a server when the model's too big. The newest on-device surface we build for is smart glasses. We ship through your release process, then instrument the feature so you can tell it's still working a quarter later.
Mostly LLM features inside products that already have users: conversational coaches, engines that keep a brand’s content in its own expert voice, and LLM-curated reporting, among others. One example: for Unilever, the Slimfast Coach combines 253 messages into 4.4 billion unique sequences. Have an integration in mind? Get in touch.