AI for IT & Digitalization
Alice Labs builds the AI infrastructure layer that enables enterprise-wide adoption — from secure data pipelines and system integration to IT service automation and Model Context Protocol (MCP) implementation. We help CTOs move from AI experimentation to production.
Part of the team that delivers
An experienced team with broad AI and tech backgrounds from leading companies
Linus
Co-founder & AI Consultant
Alice
CEO & Co-founder
Jens
AI Consultant
Eric
Co-founder & AI Consultant
Lisa
Project Lead & Implementation
Why enterprises pick Alice Labs
Production-grade AI delivery, EU-native, senior team
Results From Our Clients
Verified outcomes from completed AI implementations
AI Agent for Order Management
Ljusgårda (Supernormal Greens)
- 83% cost reduction
- 70-80% automation
- 6-week implementation
Document Automation: 60h → 3min
Public Sector
- 95% time reduction
- 60h → 3min/doc
- 1000+ hours/month saved
AI-Driven Content Production
Media Company
- $100K first year
- $40K/month recurring
- 12-month build-up
Ready to see similar results?
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AI Use Cases for IT Teams
Building the foundation for enterprise-wide AI adoption
Secure Data Pipelines
AI-powered data ingestion, transformation, and routing that connects enterprise systems while maintaining security and compliance.
Enterprise-gradeIT Service Automation
AI agents that triage tickets, auto-resolve common issues, predict incidents, and optimize resource allocation.
40-60% faster resolutionAI Governance Platform
Model registry, access controls, usage monitoring, and bias detection — giving IT visibility over all AI deployments.
Full complianceSystem Integration via MCP
Model Context Protocol implementation for secure, standardized AI access to CRM, ERP, databases, and APIs.
Secure & scalableLet's discuss your AI journey
Our team will help you prioritize use cases and build a concrete roadmap.
What Our Clients Say
"We decided early on to embrace AI technology and needed a partner who could explore opportunities, propose solutions, lead change management, and build them. With Alice, we got everything in one place and have implemented multiple solutions that increased efficiency so significantly that an entire team could be reallocated."
Andreas Wilhelmsson
CEO & Co-founder
Supernormal Greens / Ljusgårda
"Alice Labs' AI training gave us all a real aha-moment, whether we were completely new to the field or experienced! The training contained a perfect balance between theory and practice. We have definitely become more efficient at work!"
Åsa Nordin
IT Manager
Trollhättan Energi
"The collaboration with Alice Labs has been easy, educational, and incredibly supportive. We engaged them to improve our processes and create more efficiency in the team, and the result truly exceeded expectations. Through their guidance, we've gained better structure, faster workflows, and more time for what actually creates results."
Frida
Partner Manager
Bruce Studios
"Fast, professional, and wonderful people. Find out for yourself <3"
Johannes Hansen
Founder
Johannes Hansen AB
Frequently Asked Questions
What is the CTO's role in AI adoption?
CTOs need to build the infrastructure layer — secure data access, model deployment pipelines, and governance frameworks — that enables every other function to use AI effectively. We help CTOs move from experimentation to scalable production.
What is Model Context Protocol (MCP)?
MCP is a standardized protocol for securely connecting AI models to enterprise data sources. It enables AI agents to access real-time data from your CRM, ERP, databases, and APIs without exposing raw credentials or violating security policies.
How does AI improve IT service management?
AI agents can triage tickets, auto-resolve common issues, predict incidents before they occur, and optimize resource allocation. Typical results: 40-60% reduction in ticket resolution time and 30% fewer escalations.
Can AI help with legacy system modernization?
Yes. AI agents can bridge legacy systems with modern APIs, automate data migration, generate documentation for undocumented systems, and create integration layers that extend legacy investments without full replacement.
How do you handle data security for AI systems?
All deployments run in VPC-isolated infrastructure. We implement encryption at rest and in transit, role-based access controls, comprehensive audit trails, and data residency compliance. On-premise options available.
What AI infrastructure do we need?
It depends on your scale and use cases. For most enterprises, we recommend starting with cloud-based AI services, secure API gateways, and a centralized model registry. We can deploy on AWS, Azure, GCP, or private infrastructure.
How does AI help with data quality?
AI agents continuously monitor data quality metrics, detect anomalies, flag inconsistencies, and automate cleansing processes. This is critical because AI model performance is directly tied to data quality.
Can AI automate DevOps processes?
Yes. AI can optimize CI/CD pipelines, predict deployment failures, automate testing, and manage infrastructure scaling. This reduces deployment risk and frees engineering time for feature development.
How long does AI infrastructure setup take?
Basic AI infrastructure (API gateway, model deployment, data pipeline) can be operational in 2-4 weeks. Full enterprise AI platform buildout typically takes 60-90 days. We follow an iterative approach — start simple, scale as value is proven.
How do you ensure AI governance at scale?
We implement model registries, access control policies, usage monitoring, bias detection, and performance tracking. This gives IT teams visibility and control over all AI deployments across the organization.
Have more questions? Let's talk.
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