Artificial Intelligence Services
250+
AI Projects successfully delivered
$4.4B
In ROI generated for AI clients
400+
Improvers in our AI practice
21 Offices
Across 7 countries and 3 continents
From First Strategy to Full Production, We Own the Entire Artificial Intelligence Lifecycle
Every stage of the Artificial Intelligence lifecycle is a practice Improving has built so your engagement doesn't stall when the work moves from one phase to the next.
Agent Creation & Deployment →
From scoped proof of concept to enterprise-grade deployment. We build agentic systems that connect to your live data and act autonomously. GenAI, NLP, custom LLMs, and the production engineering that keeps them performing.
Machine Learning & Data →
Custom ML models, intelligent automation, and MLOps infrastructure built on your proprietary data. Not LLM wrappers. Not proof of concepts that never ship. Production machine learning, with the case studies to back it up.
The Trust Evolution: 8 Stages of Artificial Intelligence Adoption
Most organizations don't fail at AI because of the technology. They fail because they skip the trust-building stages that make AI safe to scale. Our maturity model defines the progression every enterprise goes through, not just in tools, but in governance, team readiness, and the way work gets done.
WAVE 1
Tools
Stages 1-2
From zero AI usage to off-the-shelf AI assistance. Humans approve every action, building the foundational trust required to advance.
WAVE 2
Process
Stages 3-4
AI moves beyond individual tasks to full workflows and governance infrastructure becomes essential. This is where most organizations stall.
WAVE 3
Agentic Operations
Stages 5-8
Agents run autonomously, coordinate in parallel, and span multiple products. This is where organizations stop managing AI and start operating with it.
The Real Challenge isn't the Technology, It's Building the Trust to Let it Work
Each stage builds the muscle memory and governance infrastructure required to advance. Skipping stages doesn't accelerate adoption. It undermines it.
Start With An AI Readiness Assessment
We'll map your organization against our 8-stage IOS maturity model and show you exactly where you are and what comes next. No commitment required.
Who We Build With & What We Build On
We don't just have relationships with the world's leading technology platforms. We build real things with them, for clients like you. Here's how our ecosystem works together across a full AI engagement.
Setting the Foundation for AI Success
Before a single model is trained
Every successful AI engagement starts with alignment: on goals, constraints, team structure, and what "done" actually looks like. Our strategy-phase partners help us get that right before anything is built.
Which cloud platform is right for your AI workloads and team?
What does a realistic 90-day AI roadmap look like for your organization?
How do you structure your team to deliver AI continuously?
AI Readiness Assessment
Cloud Strategy
Team Model Design
90-Day Roadmap
Microsoft Azure Solutions Architect Expert — validated expertise in enterprise cloud architecture and AI-ready infrastructure design
Getting Your Data House in Order
The work most teams skip. And regret
AI runs on data. Before building anything, we help clients establish a reliable, governed, and scalable data platform so what we build on top of it actually works. This is where AI initiatives either get the foundation they need or stall.
Where does your data live, and is it trustworthy enough for AI?
How do you enable real-time data availability without losing governance?
What does a modern data foundation actually require at your scale?
Data Architecture
Lakehouse Design
Governance Model
Streaming Pipelines
Databricks Certified Data Engineer Associate — certified expertise in lakehouse architecture, data pipelines, and ML-ready data platforms
Building and Deploying AI That Works
Where strategy becomes production
This is where we build AI models, agents, integrations, and the infrastructure that ties it together. Our build-phase partner ecosystem covers cloud deployment, foundation models, and agentic systems that operate autonomously in production environments.
Which foundation model is right for your use case — and should you build or buy?
How do you deploy AI models reliably in a cloud-native environment?
What does an Agentic AI system that connects to your live data actually look like?
Custom AI/ML Models
Agentic MVP
MLOps Pipelines
AI/ML Deployment
Microsoft Azure AI Engineer Associate — certified expertise in building and deploying AI solutions on Azure including OpenAI, Cognitive Services, and ML pipelines
Keeping AI Systems Performing After Launch
Production is where promises are tested
Getting to production is one thing. Staying there reliably, with full visibility into model performance, data drift, and infrastructure health, is another. Our observability-phase partners give us the monitoring and optimization tooling to keep AI systems performing under real enterprise load and improving over time rather than decaying.
How do you maintain AI model performance as data drifts over time?
How do you get full observability into AI systems without drowning in noise?
Where is cloud spend going and what can you responsibly reduce?
Observability Stack
Model Monitoring
Security Posture
IaC Pipelines
Google Cloud Professional Data Engineer — certified expertise in designing data processing systems and building performant data pipelines for ongoing platform optimization
Building With Anthropic
Claude powers some of the most capable agentic systems we build for enterprise clients. We bring Anthropic's safety-focused models into production environments where reliability isn't optional.
Artificial Intelligence That Reaches Production. And Delivers.
3x
Increase in qualified leads
90
Day delivery
42%
Faster operating speed
Agentic AI for Sales Enablement — From Manual Process to Production in 90 Days
Lakeshore Learning's sales team spent hours manually sourcing educational funding opportunities across government websites and spreadsheets. Improving built an Agentic AI system that crawls live data sources, identifies relevant opportunities autonomously, and hands off to sales with full context — deployed in 90 days.
Our Practitioners Teach What They Build - Watch Them Do It
Every session is led by an Improving practitioner, the same people delivering AI engagements for enterprise clients. Deep technical content, real delivery experience, open to everyone.
Building Trust at Scale for Growth and AI: What to Do First, Next, and Later in Data Governance
Preston Mesarvey
Technical Director
Governance That Actually Works: How Well-Designed AI Systems Make Responsibility Visible
Devlin Liles
Chief AI Officer
Define the Need, Solve the Problem: An AI-First Playbook for Developers
Claudio Lassala
Technical Director
Ready to Move from AI Initiative to AI Impact?
Tell us where you are and we'll tell you exactly how we can help. No generic proposals, no sales pitch, just a direct conversation about your situation.
Devlin Liles
Chief AI Officer & CCO
Scott Poulin
VP of Technology
Kevin Jourdain
Technical Director