What we solve
Workflows that are too manual, too slow or too dependent on scattered knowledge
Reporting that takes too long. Admin that is repetitive and fragile. Customer service answering the same questions repeatedly. Sales follow-up relying on memory and spreadsheets.
Finance workflows that depend on manual checking. Product data that takes weeks to clean. Supplier data that arrives messy. Pricing changes that are slow or inconsistent. Stock visibility that is fragmented across systems.
AI tools exist, but most teams have no clear view of where to apply them safely. WithPraxis helps teams identify and improve the everyday work that AI is genuinely useful for.
Why organisations choose WithPraxis
Employee-owned, not VC-backed
No pressure to upsell services or chase quarterly targets. Long-term relationships over short-term revenue.
Work on real workflows, not AI theatre
We focus on the workflows and tasks already slowing people down. No abstract roadmaps that sit on shelves.
Platform-agnostic and independent
No vendor partnerships, no technology bias. We integrate with what you already have.
Built from operational experience
This work comes from repeated experience inside complex organisations, not academic theory or consulting frameworks.
The problems we see
Across many organisations, the same issues appear again and again:
- Work gets stuck between teams, systems and responsibilities
- Data exists in many places, but people don't know what to trust
- Different teams work from different numbers
- Accountability is unclear or shared
- The same questions get revisited instead of resolved
- AI and automation get discussed before the workflow, data and ownership are clear
These are not just technology problems.
They are workflow, data and ownership problems.
The kinds of work we help improve
We work on practical workflow and data problems such as:
- Knowing where to look for reliable information
- Bringing together data that lives across multiple systems
- Understanding who customers actually are
- Identifying gaps, duplication or missing data
- Making sense of performance without trawling reports
- Supporting tasks and workflows that need to happen quickly and repeatedly
How we solve them
We design and build focused, practical AI tools and workflow support that fit into the work already happening.
This includes:
- Dashboards that bring together fragmented data into one clear view
- Tools that identify gaps or inconsistencies in customer and product data
- Custom middleware that anchors customer identity across systems
- Applications that support campaigns and personalised journeys with reliable data
- Intelligent search and discovery that understands products, images and intent
- Visual tools that help people see outcomes before they commit
- Automated enrichment of product data, content and supporting narratives
This is not workshops, roadmaps or platform selection. It happens inside live operations, not alongside them. One workflow at a time.
Each tool is built around a specific workflow or task, not generic reporting.
See what this looks like in 'practice'.
Real examples of workflow tools and practical AI we've built.
View examplesWhat makes this different
We don't start with platforms, tools or AI for its own sake.
We start with:
- A real workflow or task that matters
- The people responsible for it
- The data and systems they need to do it well
Technology is used only where it genuinely helps.
Where this work happens
We began in commerce and digital operations because:
- Workflows are frequent, visible and measurable
- Data already exists, but is fragmented
- The impact of better workflows is immediate
From there, the same approach applies to workflows in:
- Finance
- Branches and stores
- Operations and logistics
- Marketing and commercial planning
One workflow at a time.
How we work
In practice, this means:
- Identifying a workflow or task that genuinely matters
- Understanding how it is done today
- Clarifying ownership and success criteria
- Bringing the right data together
- Designing and building a focused tool to support it
- Testing it in real use and improving it over time
This work happens alongside real operations, not in workshops.
A practical place to start
Most organisations begin with one workflow and one focused tool.
That is usually enough to prove value, build confidence and decide what comes next.
What this means in practice
What this means in practice
What problems does WithPraxis solve?
Repetitive manual work, fragmented data, disconnected systems and slow internal handovers. The pattern shows up in reporting, admin, customer service, sales follow-up, finance workflows, ecommerce operations, product data and stock visibility.
Why are these problems hard for teams to fix on their own?
The work usually sits between systems and teams rather than inside any single one. Data lives in different places, ownership is shared, and the gaps get patched with spreadsheets and people doing the same checks again and again.
Why does better reporting not solve this?
Reporting tells you what already happened. It does not change how the next task gets done, who picks it up or which system has the right information. The fix is improving the workflow itself.
Is this only relevant to distributors and merchants?
No. It applies to any business with messy workflows, fragmented data or repetitive operational tasks. Distributors, merchants, trade suppliers and commerce teams are good examples because they often have several systems that need to work together better.
Where does a typical engagement start?
Most projects start with one workflow or task where AI could save time, improve visibility or reduce manual effort without creating unnecessary risk. We make that workflow better before extending to others.
Do we need clean data first?
No, but the data needs to be understood. Part of the work is identifying what data exists, where it lives, what shape it is in and whether it is good enough for the workflow being improved.
Will this replace our existing systems?
No. WithPraxis works around the systems you already use, including spreadsheets, CRM, ERP, PIM, WMS, ecommerce platforms, reporting tools and supplier data.
How do you keep AI controlled?
We focus on practical workflows with clear inputs, human review, sensible approvals and measurable outcomes. The aim is useful automation, not uncontrolled AI.
What kind of impact should we expect?
Time saved on repetitive tasks, faster reporting, cleaner data, better customer responses and fewer manual handovers between teams. The impact comes from making everyday work run better, not from launching new technology.