AI Projects successfully delivered
In ROI generated for AI clients
Improvers in our AI practice
Across 7 countries and 3 continents
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.
We connect your AI ambition to your actual data, engineering, and organizational reality. Executive strategies, team readiness assessments, use case prioritization, and a roadmap your teams can actually execute.
We run AI workshops, enablement programs, and adoption sprints led by the same practitioners who deliver AI engagements for enterprise clients. Your teams build real skills into the work they're already doing. Not classroom awareness. Actual capability.
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.
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.
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.
Stages 1-2
From zero AI usage to off-the-shelf AI assistance. Humans approve every action, building the foundational trust required to advance.
Stages 3-4
AI moves beyond individual tasks to full workflows and governance infrastructure becomes essential. This is where most organizations stall.
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.
Each stage builds the muscle memory and governance infrastructure required to advance. Skipping stages doesn't accelerate adoption. It undermines it.
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.
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.
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?
Microsoft Azure Solutions Architect Expert — validated expertise in enterprise cloud architecture and AI-ready infrastructure design
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.
Increase in qualified leads
Day delivery
Faster operating speed
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.
Formulation challenge resolution time, down from weeks
MVP to full product launch, with paying customers
Powered by Azure OpenAI on Microsoft Azure Cloud
The Institute of Food Technologists had decades of proprietary scientific knowledge locked in journal archives that food scientists couldn't practically access. Improving built IFT Co-Developer — an AI-powered SaaS platform featuring "Sous," an AI chatbot that gives food scientists instant access to IFT's knowledge base, solves complex formulation challenges, and generates customized ingredient recommendations. Taken from MVP to full B2B SaaS product launch.
Scalable across Catalis's full client base
No on-premise infrastructure required
Sensitive information redacted without manual review
Catalis was processing sensitive government documents on aging on-premise infrastructure that couldn't scale to meet client demand. Improving built a fully cloud-native redaction system on AWS — using Textract for OCR, Comprehend for text analysis, and serverless architecture that processes documents automatically and scales across departments without added overhead.
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.
Technical Director
Chief AI Officer
Technical Director
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.
Chief AI Officer & CCO
VP of Technology
Technical Director
