Our Approach to AI

AI implementation should be justified

Technically sound, financially sensible, and genuinely needed.

Too many AI projects fail because they start with the technology rather than the business need. Or because they produce impressive strategies that never translate to working solutions. We’ve built our approach to avoid both traps. Here’s how we think differently about AI implementation.

Why ‘Justified’ Matters

In a world of AI hype, we believe every AI investment should be justified:

Technically justified

using the right approach for your specific problem

Financially justified

clear ROI before you invest

Strategically justified

aligned with your business goals

Sometimes that means saying ’not yet’ or ’not AI’. We’d rather give honest advice than sell unnecessary solutions.

Our Core Principles

Principle 1

We Start With What Keeps You Awake at Night

We don’t arrive with a toolkit of AI solutions looking for problems to solve. We start by understanding what’s actually challenging your business.

Maybe it’s:

Taking two weeks to produce a quote when competitors take two days
Manually processing thousands of documents
Losing high-value opportunities you should be winning
Knowing there’s value in your data but not knowing how to extract it

Only after we understand your real challenges do we explore whether and how AI could help. Sometimes the answer is AI. Sometimes it’s better processes. Sometimes it’s both. We’ll tell you honestly.

When a security systems company engaged us, they didn’t ask for AI - they asked for help with automating their slow quotation process. Only after understanding their process did we identify where AI could reduce it from 14 to 3 days.

Principle 2

Clear Communication at Every Level

Technical depth without the jargon. Business focus without dumbing down.

We’ve learned that successful AI implementation requires speaking multiple languages fluently:

To your board: ROI projections, risk assessments, strategic alignment
To your technical teams: Architecture decisions, model selection, infrastructure requirements
To your operational staff: How this makes their jobs easier, what changes, what stays the same

This isn’t about simplifying complex concepts - it’s about explaining them in ways that matter to each audience.

If you need someone to tackle a difficult technical problem and then explain the solution to a non-technical person, Chris is who you want.

Principle 3

Justified ROI at Every Step

The word “Justified” isn’t just in our name - it’s in everything we do.

Every recommendation comes with a business case. We calculate the expected return before suggesting any investment. If the numbers don’t work, we’ll tell you - even if it means no project.

Proof of concept before production
Phased investment with validation gates
Clear metrics for success
Honest assessment when AI isn’t the answer

We developed a machine learning solution to automatically categorise products against 1000s of attributes, leading to $2M in annual operational cost savings.

Principle 4

Technical Excellence, Practically Applied

Deep technical expertise applied to real-world constraints.

We understand the latest AI research AND your legacy systems. We know what’s theoretically possible AND what’s practically achievable with your team and budget.

This practical approach means:

Solutions that integrate with what you have
Architectures your team can maintain
Recommendations sized to your resources
Compliance and governance built in from the start

We developed a retail store layout optimisation tool which takes into account the practical and physical constraints of the stores and the constraints of the available data.

Principle 5

Partnership, Not Preaching

We don’t arrive with predetermined methodologies or one-size-fits-all frameworks.

Your industry has nuances we need to understand. Your company has its own culture and constraints. Your team has valuable expertise and insights.

Real partnership means:

Learning your business before suggesting changes
Respecting what works while improving what doesn’t
Building with you, not for you
Ensuring you own and understand the solution

Chris is very thorough and professional, he challenged our ideas with practical insight and added technical credibility to our nascent project.

The Justified Process

Our process isn’t just a series of steps - it’s an adaptive approach that responds to what we learn along the way.

UNDERSTAND

Deep dive into your business

↓

IDENTIFY

Find opportunities with real ROI

↓

VALIDATE

Proof against business metrics

↓

IMPLEMENT

Build production-ready solutions

↓

EMBED

Transfer knowledge & capability

↑ Iterate as we learn ↓

What Makes Each Step Different

1. Understand

We dig deeper than requirements documents

This isn’t a checkbox exercise. With years of product management experience behind us, we know the real challenges often aren’t in the brief. We’ll spend time with your teams, observe your processes, and ask the questions that uncover hidden bottlenecks.

We map not just what you do, but why you do it that way
We identify constraints you might not even realise you’re working around
We understand your customers’ pressures, not just yours
We learn your business language and culture

This deep understanding is why we can challenge assumptions constructively and spot opportunities others miss.

2. Identify

Cross-industry insights applied to your context

Having implemented AI across retail, finance, healthcare, and manufacturing, we bring patterns of what works and what doesn’t. But we never force-fit solutions.

We know a technique that transformed retail might solve your logistics problem
We’ve seen enough failures to recognise the warning signs early
We can predict implementation challenges before they happen
We bring fresh perspectives while respecting your domain expertise

Real example: Our retail layout optimisation approach drew from techniques we’d seen in financial portfolio optimisation - different industry, similar mathematical challenge.

3. Validate

Business validation, not just technical accuracy

Many can build a model with 95% accuracy. But accuracy doesn’t pay bills. We validate against what actually matters to your business.

Does this save the time we predicted?
Will your team actually adopt this solution?
Does it integrate with your existing workflow?
What’s the real pound value of this improvement?

Example: When developing a machine learning system to automate the classification of 1000s of product attributes, we realised that the senior leadership team needed estimated cost savings, not precision/recall metrics.

4. Implement

Full-stack expertise from databases to deployment

Our background spans software engineering, data science, and AI infrastructure. This means we don’t just hand over a model - we deliver working solutions.

If you have a technical team:

We collaborate effectively, speaking their language
We ensure knowledge transfer happens throughout
We design for maintainability, not complexity

If you don’t:

We handle everything from data pipelines to user interfaces
We set up monitoring and maintenance procedures
We document everything in language you understand

We’ve deployed solutions on AWS Lambda, built real-time processing pipelines, and created user interfaces that non-technical staff love to use.

5. Embed

Your capability, not our dependency

Success means your team owns and operates the solution confidently. We build in knowledge transfer from day one:

Your team shadows us during development
We create documentation at multiple levels of detail
We run training sessions tailored to different roles
We’re available for questions after handover

The goal: you should understand not just how to use the solution, but why it works and how to evolve it.

Built for Reality, Not Theory

This isn’t a waterfall process. When Validate reveals our assumptions were wrong, we go back to Understand. When Implement uncovers a better approach, we re-Validate.

This iterative approach means:

Faster correction of wrong assumptions
Continuous improvement throughout the engagement
No sunk cost fallacy - we pivot when needed
Learning incorporated immediately

How We’re Different

A Rare Combination

Most AI consultancies excel at strategy OR implementation. We do both, which means:

Strategies that can actually be built - because we understand the technical realities
Implementations that align with strategy - because we understand the business context
One partner throughout - no handovers, no lost in translation
Faster time to value - no gaps between planning and doing

What This Means for You

Clear communication - We translate complex AI concepts into business impact
Honest recommendations - We’ll tell you when AI isn’t the answer
Practical solutions - Designed for your real-world constraints
True partnership - We succeed when you succeed