AI commerce
Most companies asking about AI have a process problem, not an AI problem. Automating a process nobody has fixed gets you the same bad outcome, faster and at higher cost. That sentence is most of the consulting.
What it means
The applications that hold up in commerce are unglamorous and specific: catalogue enrichment at a scale nobody can staff, semantic and retrieval-based search that understands a query the keyword index cannot, ranking and recommendation fed by real behaviour, and operations automation across quotes, approvals and data reconciliation.
What separates those from the demos is where they sit. An AI feature is only worth building if it is wired into a platform already carrying order volume, with the catalogue, pricing and permission rules it has to respect. Retrieval that ignores the price scope, or an agent with no defined tool access, is a prototype with a support burden.
This is the newest practice area on the site and I would rather say that than dress it up. The automation and integration record behind it is real, including taking 90% of B2B order and quote processing off people at Nxtby, and the AI layer is where that work is now going. If you want a delivered AI case study with a published metric, I do not have one to show you yet.
Problems solved
The symptom as the person with the problem describes it, then what is usually underneath it.
The operation runs on manual work
Quotes, approvals, catalogue QA and data reconciliation absorbing people who should be doing something else. This is where automation pays, provided the process underneath it is worth keeping.
On-site search does not understand the catalogue
Customers searching in language the keyword index has never seen, and leaving with no results on products you stock. Retrieval and semantic ranking fix this in a way that another synonym list will not.
Product data is too thin to rank or convert
Tens of thousands of SKUs with supplier descriptions and no attributes. Enrichment at that scale is either an AI pipeline with human review or it does not happen at all.
AI answers describe the business incorrectly
Language models increasingly sit between a buyer and a brand. Whether they can state what you sell, to whom and on what terms is now a structured data and content structure problem.
When it applies
Signals it is time
- A repeatable process with high volume and low judgement per instance
- A catalogue too large to enrich or QA by hand
- Search logs full of queries returning nothing
- Data reconciliation that people do on a schedule
- A defined place for a human to check the output before it matters
And when it is the wrong call
Automating a process nobody has fixed. You get the same bad outcome, faster and at higher cost. Fix the process, then decide whether the remaining work is worth automating, and quite often it is not.
Every one of the six pages under expertise carries one of these. A consultant who recommends everything for everyone is a vendor with a wider catalogue.
Approach
Fix the process first, on paper
Map the current steps and remove the ones that exist because of a system limitation nobody has revisited. Most of the saving is here, and it costs nothing.
Choose applications with a measurable edge
Enrichment, retrieval, ranking, reconciliation. Each one has a number attached before it starts: coverage, no-result rate, conversion on search sessions, hours off the rota.
Wire it into the real system
Against the live catalogue, pricing rules and permission model, through the platform APIs and workflow tooling. An integration that runs beside the platform rather than inside it drifts within a quarter.
Keep a human checkpoint where it matters
Explicit scope, explicit tool access, and a review step wherever the output touches price, stock or a customer commitment. Agentic systems that plan and execute need this more than generative ones, not less.
Stack
OpenAI APIsRetrieval & semantic searchCatalogue enrichmentn8nWorkflow automationStructured data
Related work
Full case studies are published on the Raulji Technologies site, which is where delivery lives.
Related writing
Questions
What is the most reliable AI use case in eCommerce today?
Catalogue enrichment with human review, and retrieval-based on-site search. Both have a number attached before you start, both fail visibly rather than silently, and neither requires the customer to trust the model directly.
Do you build AI agents for eCommerce?
Where the task genuinely needs planning across multiple steps, and with explicit scope, defined tool access and a human checkpoint before anything touches price, stock or a customer commitment. Most requests described as agents are better served by a deterministic workflow, and I will usually say so.
Is there an AI case study with published results?
Not yet. The automation and integration record behind this practice is real and is documented in the experience timeline, but no AI engagement on the list has a published measured outcome. Writing one up before it exists would be the fastest way to lose the argument that this page is trying to make.
Whether you are scaling an existing commerce platform, planning a migration, exploring headless architecture or looking at AI-driven transformation, the first conversation costs nothing and usually shortens the second one.