AI Agents & Workflows for Retail and Consumer Brands
Every new SKU triggers the same week of copywriting, market adaptation, channel formatting and approval chasing. We build the workflow that produces the launch pack, stops at your brand guardrails, and waits for a human before it publishes.
The work we take off the desk
Named processes, not categories. If none of these are your bottleneck, the assessment will say so.
Launch content does not scale with the catalogue
Descriptions, feature bullets, SEO fields, and a different format for every channel — multiplied by every market you sell in. The work is identical each time and it still takes a week.
Front-line volume across time zones and languages
Pre-sale product questions, order status, returns. Most of it is answerable from material you already have; the cost is that someone has to be awake to answer it.
Real defect signals buried in review noise
A genuine batch problem shows up as four reviews scattered across two marketplaces and a support ticket. By the time anyone connects them, the batch has shipped.
Campaign assets rebuilt per channel
The same creative resized, reformatted and re-copied for each placement and market. Mechanical work that still needs brand judgement at the end.
What we actually build
Every step below is a real step in the build. Where a person still decides, it is marked — those gates are designed in, not bolted on.
New SKU to omnichannel launch pack
A fixed trigger, a fixed output set and a fixed approval gate. Textbook workflow territory, and usually the fastest payback in a brand business.
- 1
Pull attributes, imagery and category from the PIM
Source of truth stays the PIM. The workflow reads from it rather than becoming a second place where product data lives.
- 2
Generate the copy set per channel
Long description, feature bullets, SEO title and meta description, and the short-form variants each channel expects.
- 3
Adapt per market rather than translate
Sizing conventions, units, claim wording that is permissible in one market and not another, and the seasonal framing that only makes sense in the right hemisphere. Translation of a US listing is not a Japanese listing.
- 4
Run the brand guardrail check
Banned claims list, tone rules, mandatory disclaimers, and per-channel character limits. This step is deterministic and rule-based, not a judgement call handed back to the model.
Queue for brand approval, then publish via channel APIs
Human decidesAuto-publish is available and switched off by default. Approval is per SKU or per batch, whichever matches how your team already works.
Front-line service with escalation
Customers do not follow a script. Deciding which tool to reach for, and recognising what it must not touch, is agent work.
- 1
Identify intent and language, and resolve the order if possible
One conversation may contain a product question, a delivery complaint and an upsell opportunity. It is treated as all three.
- 2
Answer only from the approved knowledge base
Product and policy answers come from material your team has signed off. Outside that material, the agent does not improvise.
- 3
Use tools for the real work
Order lookup, carrier tracking, return authorisation within the policy limits you configure. Answering questions is table stakes; completing the task is the value.
- 4
Recognise what it must not handle
Damage claims, safety issues, anything outside policy, and any customer who asks for a person. These are hard boundaries, not model preferences.
Escalate with full context and its own summary
Human decidesYour agent receives the conversation, what was already attempted, and what it believes the customer actually needs — not a cold handoff.
Review & UGC triage
The signal is already in your feedback. The workflow is the part that connects four scattered complaints into one batch problem.
- 1
Classify each item
Product defect, logistics failure, expectation gap, praise, or spam. Defect and logistics failure are the two that cost money if missed.
- 2
Cluster defect signals by SKU and batch
Across every source at once, so a pattern spread thin over several channels still surfaces as one pattern.
- 3
Route clusters to product and QA with the evidence attached
The original reviews, order references and dates, so the team receiving it can act rather than start an investigation.
Draft public responses in brand voice; a human posts them
Human decidesPublic replies carry reputational weight. They are drafted here and posted by your team.
How you will know it worked
We are a young practice and we do not have a wall of client logos to point at. So instead of asking you to trust results you cannot check, here is exactly how the result gets measured on your data — and how you check it yourself.
The metric is agreed before anything is built
You and we write down what is being measured and what counts as good, in advance. If we cannot agree a measurable definition, that is a signal the process is not ready to automate — and we would rather find that out in week one.
The baseline comes from your records, not ours
The "before" figure is drawn from your own system logs for the period preceding go-live. No industry benchmark, no vendor-supplied comparison, no number we brought with us.
Measurement runs in production, on your data
Live operation over an agreed window, against the documents and cases you actually receive. Not a curated test set, and not a demo environment where the inputs were chosen by us.
You get the raw log, not a summary
Per-item results including every case the workflow got wrong and why it went wrong. You can recompute our headline number yourself, and we would rather you did.
The result is reported either way
Including when it falls short of the target we agreed. A supplier who only reports the wins is not measuring anything — they are selecting. You will see the misses in the same document as the hits.
It runs inside the systems you already have
Nobody logs into a new tool. Where an API exists we use it; where one does not, we use supervised UI automation against the same screens your staff use — and we tell you which is which before you commit.
How a build runs
Discovery
We sit with the people doing the work and map the process as it actually runs, including the exceptions nobody wrote down. We come back with a shortlist of candidates ranked by volume, error cost and how cleanly they can be automated.
Pilot
One workflow, built end to end and put into production against your live systems. We agree an accuracy target up front and measure against your data, not a benchmark set. If it misses, you see the number.
Deploy
Integration hardening, access control, audit logging and the human escalation paths. Your team is trained on the runbook and owns the operating procedure before we step back.
Operate & extend
Monitoring, drift review and tuning as your documents and edge cases change. Once one workflow is trusted, the next one costs far less than the first.
Start with one workflow
Not a platform rollout and not a strategy deck. One process, in production, measured on your own data — so the decision to do the next one is made on evidence.
What the pilot includes
- Discovery workshop: we map the process as it actually runs, not as the SOP describes it
- One workflow built end to end and integrated with your live systems
- An accuracy baseline measured on your own documents and data, published to you
- Handover documentation and an operating runbook your team owns
- 30 days of tuning after go-live
Built on our own stack
We are not assembling someone else's components. The engines underneath these workflows are the products we already build and run.
AIGC Engine
The copy and creative production layer underneath the launch workflow, tuned to your brand voice rather than a generic model default.
Learn moreAI Digital Humans
Where the front desk should have a face — livestream hosting, guided selling, and multilingual video service.
Learn moreAI Knowledge Base
The approved answer set the service agent is allowed to draw on, so support answers stay consistent with what marketing published.
Learn moreRetail & Brand: common questions
Will it write claims we are not allowed to make?
The guardrail step exists specifically to stop that. You supply the banned-claims list and the mandatory disclaimers per market, and the check is rule-based rather than left to the model’s judgement. Nothing publishes without approval unless you deliberately switch that off.
Can it publish without us looking at it first?
It can, and by default it does not. Most brands run approval per batch for the first few months and relax it for low-risk categories once they have seen the output quality. That decision stays yours and is a configuration change, not a rebuild.
Our brand voice is specific. Generic AI copy is worse than nothing.
Agreed, and that is why voice is set from your existing guide plus examples of copy you consider good, not from a prompt describing an adjective. The guardrail check that runs afterwards is deterministic. If the output still does not sound like you at the end of the pilot, you have not bought anything.
How is this different from your AIGC SaaS platform?
The platform is a self-serve subscription: your team logs in and generates content themselves. This is a built workflow: it triggers off your PIM, applies your guardrails, and publishes to your channels without anyone opening a tool. Many brands run both — the platform for ad-hoc work, a workflow for the repeating pipeline.
What does a pilot cover?
One workflow in production in 4-6 weeks. SKU launch content is the usual first choice because the volume is predictable and the quality bar is easy to agree on before we start.
Tell us the process that hurts most
The assessment is a working session, not a pitch. If the honest answer is that your bottleneck is not worth automating yet, that is the answer you will get.