Practical AI you can actually put to work.
There's no shortage of tools or advice. What's missing is a way to decide what's worth doing for your specific business. We help you work that out, and then we build it.
At R3, the people doing the strategy are the same people who can do the data work and the engineering. That's why our AI projects ship instead of stalling after the workshop.
Most AI projects fail because they start with a tool and look backwards for a problem it can solve. The ones that get past that fail because nobody owned the data and integration work.
We start with your business goals and identify where AI can do useful work. From there, the build mixes standard AI products with the custom integration that off-the-shelf options can't handle. Then there's the governance, which is what makes any of it safe to put into production.
You end up with a plan that fits what your team and your systems can actually take on.
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AI is only as useful as the data it can reach. R3's strength is the integration work behind that: connecting the systems that matter, including legacy platforms and whatever was left tangled after a merger.
We regularly work with complex stacks (ERP, CRM, PIM, bespoke platforms) and build the integration layer that makes AI useful across the business.
Most AI consultancies stop at advice. We do the engineering too, which is why the work actually ships.
AI starts doing useful work once it's connected to the data and systems that matter. These are examples of that, anonymised by sector.
A hospitality group's web chat was being used by suppliers, guests and curious visitors, leaving the sales team to filter for actual customers. The replacement is a purpose-scoped assistant: it answers operational questions deterministically, holds a real planning conversation with prospects, builds a structured profile as it goes, and writes a qualified lead into the CRM only when the signal is real. The noise gets absorbed; the sales team only sees what matters.
Where the money is
Sales coordinator capacity returned to the business, plus a faster lead-response time. In this sector, that response time is the metric that actually decides conversion.
A retailer serving hundreds of customer organisations, each with bespoke product requirements. Manually defining "PE kit" or "back-to-season essentials" by hand across every variation is a multi-month effort that's already stale by the time it's finished. The pipeline does the heavy lifting differently: statistical clustering on real order data identifies what gets bought together, an AI labels and explains each cluster, and staff approve in minutes via a purpose-built review screen. Customers never see an AI guess. They see human-validated kits, with a full audit trail behind them
Where the money is
Weeks of manual curation compressed into minutes; better conversion through guided shopping; structured data the rest of the platform can build on.
A brand operating across nine territories. Every page change kicks off translation work, alt text rewrites, meta descriptions and OG tags. Done manually it's a constant drag on regional teams; done badly it's a brand and SEO liability. The fix is a drafting layer baked into the CMS: the moment global content changes, locale-specific drafts appear in the relevant editor's queue. They edit, approve, or reject. Nothing publishes without a human. The boring work disappears; the editorial judgement stays where it belongs.
Where the money is
Hours back across regional teams every week, consistent SEO depth across markets, and no quality cliff between launch markets and second-tier ones.
Less visible than a chatbot or search bar, but often easier to ship. All drawn from current client work.
Most customer questions are things your platform already knows: where's my order, has my refund gone through, is this in stock. Connect a chat to those systems and the routine queries handle themselves. Your team picks up the awkward ones..
Anyone who's installed something has scrolled through a long PDF for one number. A product-page assistant pinned to your official documentation gives that number in a sentence, without making anything up.
Service teams often Google their own website or copy answers from old Word docs. A private search across public help articles and internal notes pulls the answer faster, and the gaps in your public content become visible.
A weekly report grouping failed on-site searches into themes: products people are searching for that you don't sell, and gaps where your content doesn't use the words your customers do. It tells you what to add next.
Returns data on its own is a back-office number. Cross-referenced with product and customer data, it starts telling you which lines are returned far more than the rest, and why.
Catalogue issues are the kind of work nobody owns. A weekly AI sweep flags missing images, inconsistent product names, prices out of step with the range, descriptions that contradict the specs. Staff fix what matters; the rest is logged for later.
Off-the-shelf AI tools only stretch so far. xFlo is our own agentic AI platform, used when the job needs a workflow built specifically around how your business works, with the data and rules you've already got.
More than a single prompt. Full processes with branching logic and decision points built in.
Works with your existing stack, pulling from and pushing to the platforms you already use.
Built for production. Human approval at the moments that matter, with a full audit trail behind everything the agent does.
Marketing, sales, operations, service. Automations built around the way each team actually works.
AI opportunity and readiness assessment. Where AI can help, what's feasible, what to prioritise first.
Use case design and ROI modelling. For each opportunity, we estimate the build effort and the value before anything is built.
Tool selection, data engineering and integration. We're vendor-neutral on what you choose, and we do the work to make it fit your stack.
Prototype to production. We prove the value first, then scale, with xFlo handling the custom workflows that justify it.
If you want a clear view of where AI fits in your business and how to implement it properly with the systems you already have, let's talk.
