AI · 1 min
Introduction to machine learning: a beginner's guide
What machine learning is, the main types of problems it solves, and what it takes to move a model from a notebook into a decision that matters.
Read article →02 · Applied AI
AI that does real work in your systems — with evidence for every step.
Agents, LLM applications, machine learning and analytics, built on governed data and designed for the work that comes after the demo.
The problem
Most AI never makes it past the pilot.
A demo is easy. A system your risk team will sign off, your staff will trust and your engineers can operate is not. It needs grounded data, typed tools, evaluation, tracing and humans at the right moments.
We build applied AI like any other production system — and design the oversight in from the start, so it can be trusted with real work.
Services
Applied AI services
AI Agents & Agentic Workflows
Production agents that plan, call your systems, check their own output and hand off to people.
LLM Applications & RAG
Grounded assistants over your documents and data, with permissions and checkable citations.
Machine Learning
Forecasting, classification and recommendation models taken all the way to monitored production.
Architecture
Where this practice sits
We activate governed data: the analytics, models, assistants and agents that turn it into decisions and actions.
Protected by
- AI guardrails
- Tool scoping
- Evaluation & tracing
01 · Governed data
- Lakehouse / warehouse
- Master data
- Documents & knowledge
02 · Activation
- Analytics & metrics
- ML models
- LLM apps & RAG
- AI agents with human gates
03 · Outcomes
- Decisions
- Automated workflows
- Answers with citations
Deliverables
What you'll have at the end
A system in production
Not a slide or a notebook — a working agent, model or application integrated into the workflow.
Evaluation evidence
Golden sets, regression tests and red-team results your risk team can review.
Traces and controls
Every run recorded; approval gates where the cost of error is high.
Capability left behind
Code in your repos, runbooks in your wiki, and trained people on your team.
Projects
Applied AI in practice
Case studies
Case studies for this practice are coming soon.
Insights
Related reading
- AI · 1 min
The complete guide to AI transformation
AI transformation is mostly not about models. It is about picking the right workflows, getting the data ready, building governance in, and leaving capability behind.
Read article → - AI · 1 min
The future of data science
LLMs and agents are changing what data scientists spend their time on. The craft is shifting from building every model by hand to framing problems, evaluating systems and engineering for production.
Read article →
FAQ
Frequently asked questions
Pick one workflow. We'll show you what an agent could do with it.
A 45-minute call with a senior AI engineer.