Do we need our data to be perfect before we start with AI?

No. Most businesses don’t have perfect data, and that’s fine. Part of the work is finding which knowledge is reliable enough to ground an AI system, and tidying the rest as we go.

Will our data be used to train public models?

No. We design systems so your data stays within services you control, and we choose providers and settings that don’t train on your inputs.

How do you stop an AI system from making things up?

We ground answers in your own sources, add evaluation and guardrails, and design clear handover to a person when confidence is low. We test against real questions before anything goes live.

AI Solutions & Intelligent Systems | CU Digital Intelligence

Service 01

Assistants, agents and retrieval systems grounded in your own data.

Discuss this service See it in the Lab

We design and build AI that earns its place in a business: assistants, agents, LLM applications and retrieval systems grounded in your own data and connected to the tools your team already uses. The measure of success is simple: does it make the work measurably better.

Capabilities

01LLM applications
02AI agents
03RAG & search
04Knowledge systems
05Model integration
06Evaluation & guardrails

How we approach it

01UnderstandWe start with the business: the problem, the people, the data and the constraints. No assumptions.
02DesignWe define the product, the experience and the architecture before anything gets built.
03BuildWe engineer the system - software, automation or AI - with quality and maintainability from day one.
04IntegrateWe connect the solution to the tools and workflows the business already relies on.
05ScaleWe improve, extend and scale as requirements evolve. The system grows with the business.

See it in the Lab

Service Desk Intelligence

An AI assistant that triages, drafts and resolves routine requests, and hands complex cases to a human with full context attached.

Demonstrates how AI support can be grounded in a business’s own knowledge and escalate cleanly to people.

All concept builds

Run triage, then open a ticket to see the AI draft.

Concept buildService desk / Monday 08:408 waiting

Queue

8

Auto-resolved

0

Draft ready

0

Escalated

0

Eight tickets are waiting. Run triage to sort them.

Fake tickets. Real triage logic.

Time-back estimator

Move the sliders to get a rough sense of the hours and dollars tied up in manual, repetitive work each year.

People doing repetitive work5

Hours each, per week6h

Average hourly cost (AUD)$55

A rough estimate, not a quote. It assumes 46 working weeks and that 40 to 60 percent of repetitive work can be automated. Your numbers will differ.

Hours a year on repetitive work: 1,380
Estimated yearly cost: $75,900
Typically recoverable with automation: $30,360to$45,540

Talk through these numbers

Questions

No. Most businesses don’t have perfect data, and that’s fine. Part of the work is finding which knowledge is reliable enough to ground an AI system, and tidying the rest as we go.

AI Solutions

Discuss this service

Tell us a little about the problem. You don’t need a fully defined specification.

Honest answers
We’ll tell you honestly whether we can help - and how.
A real person
A real person - not a pipeline - reviews every enquiry.
No lists
Your details are only used to respond. No newsletters, no lists.
No lock-in
Dependency on us should be a choice, not a trap.

AI Solutions & Intelligent Systems