// FAQ
Frequently Asked Questions
Questions we get asked in every discovery call. Each has a longer answer in the blog if you want the full picture.
What is catalogue automation?
Catalogue automation is the process of generating, enriching, and maintaining product content at scale using AI — without manual copywriting for each SKU. The AI takes raw product data and produces structured, SEO-optimised descriptions, titles, and metadata, publishing them directly to your platform without human intervention in between.
Full answer →How long does AI content integration take?
A well-scoped project reaches production in 10 to 30 days. The 10-day end applies when product data is clean and the platform has a usable API. The 30-day end applies when data cleanup or non-standard integrations are required. The AI model configuration itself is rarely the constraint.
Full answer →Can AI automatically translate product descriptions into multiple languages?
Yes — and modern LLMs do this differently from translation APIs. The output is a localised rewrite, not a word-for-word transfer, with market-specific keyword targeting and brand voice preserved. We have run this across EN, FR, NL, and DE for clients in Belgium, France, the Netherlands, and the UK.
Full answer →What is end-to-end ecommerce implementation?
It means every step from data entry to live page is handled by the pipeline without manual intervention. New SKU added to your ERP or PIM — description generated, SEO-validated, translated if needed, and published to your platform automatically. No export, no copy-paste, no review queue.
Full answer →Should I use Anthropic or xAI, or build a proprietary AI model?
For a first integration: a commercial hosted model (Anthropic Claude or xAI Grok). Faster to deploy, strong output quality, no infrastructure overhead. A proprietary open-source model makes sense when volume is large enough that per-token costs matter, or when data sensitivity requires on-premise deployment. We build the integration layer to be model-agnostic so you can switch later.
Full answer →How much cheaper is AI content generation than IBM Watson?
Substantially. Watson's NLG was template-driven and expensive per API call, which limited it to enterprise budgets. Current LLMs produce better output at a fraction of the cost per piece of content. Open-source models on your own infrastructure have zero per-token cost. We migrated from Watson in 2018 and the economics have only improved since.
Full answer →How do you train an internal team to own the AI content model?
We run coaching in parallel with the build, not after it. Team members see the prompt structure being set up, participate in output review during the build, and finish the project with weeks of context rather than a two-day handover. The specific skills we develop: prompt evaluation, data hygiene, escalation judgment, and monitoring interpretation.
Full answer →What does change management look like for an AI content project?
It starts before go-live. We name an internal owner, revise the content workflow to remove the manual steps the pipeline replaces, set up a ninety-day review cadence, and define escalation paths. The most common failure mode is not the technology — it is an unchanged process that the team continues filling with manual work after the system goes live.
Full answer →Still Have Questions?
Book a 30-minute call. We run a live demo on your data — no slides, no NDAs required for the first conversation.
Book a Demo