Why applied AI beats BI and ERP
Our approach
Better everyday work.
Every day.
We help businesses use AI to improve everyday workflows, automate repetitive tasks and get more from the systems, data and people they already rely on.
The work that sits { between } teams, systems, and outcomes.
This is not about buying AI tools. It's about fixing the workflow first.
Practical applications, not theory
Who owns this workflow?
Most organisations can't answer this clearly
Ownership.
Someone accountable. Not a committee.
Every workflow needs a named owner. Not a department. Not a working group. One person with the authority to act, and live with the outcome.
Clarity.
Clear criteria. Not endless data.
Good outcomes don't come from more information. They come from knowing what question is being answered, and what "good" looks like.
Support.
Tools that enable. Not complicate.
The right systems surface what matters, remove friction from the process, and support judgement, instead of adding work.
THIS IS WHAT OUR WORK IS BUILT AROUND
years of experience in commerce operations
of workflows have no owner
Patterns we've seen across
Retail · Distribution · Manufacturing · Services
Where we work
Each engagement focuses on one workflow at a time
Bounded, owned, and practical
What happens if we talk
Three steps.
One workflow.
Understand
Identify one workflow that matters. Observe how the work happens. Agree who owns it.
Build
Create practical support. Test it inside the real workflow. Measure what helps.
Support
Embed into operations. Watch for drift. Keep the workflow healthy.
No platforms · No programmes
One workflow at a time
We start with one real workflow your team already runs
Let's make the work clearer.
If this resonates, we should talk. No pitches. No pressure.
Start a conversationCommon questions about the approach
Common questions about the approach
What is the WithPraxis approach in one sentence?
Start with a real workflow, check the data and systems involved, build something practical with human review and measurable outcomes, then extend it once it works.
Why start with a workflow rather than a strategy?
Strategies stall when they are not grounded in real work. Starting with a workflow means we can show useful improvement quickly and let the wider picture build from working examples rather than slides.
How do you decide which workflow to start with?
We look for workflows where the impact is clear, the data is available, the team is willing and the risk is sensible. Reporting, admin, customer service, sales follow-up, product data and supplier data are common starting points.
How is data handled along the way?
We use the data already involved in the workflow. Cleaning, mapping or enrichment happens as part of improving the workflow rather than as a separate, upfront project.
Where does human review fit in?
Human review sits at the points that matter: approving outputs that affect customers, suppliers or money, or anywhere accuracy is not yet fully proven. The aim is useful automation, not unattended AI.
How do you measure that it is working?
Each workflow has a clear before-and-after measure: time saved, error rates reduced, response times improved or data quality lifted. Measurement is built in from day one, not bolted on at the end.
How does this scale beyond the first workflow?
Once a workflow runs well, we reuse the same pattern of inputs, controls and review for the next one. Over time, related workflows connect and the overall way the business runs everyday work improves.