AI Content & Localisation Practice
We build the AI content and localisation infrastructure that commerce businesses run on — attribute-intelligence extraction, multilingual generation, and quality routing that puts humans on exception only. Proven at marketplace scale.
Proven at India's largest marketplaces
The reframe
The reason most catalogue content operations fail at scale — even well-run ones — is that they optimise for one or two of these jobs while inadvertently neglecting the others. The architecture we build is designed from the ground up to serve all four.
01 / Search Filtering
Attributes power the filter panels shoppers use to narrow a category. Missing or incorrectly mapped attributes make the filter rail useless — a customer filtering for "cotton" never sees a product filed as "natural fibre."
02 / SEO
Structured attributes build rich, differentiated pages that rank for long-tail queries. A search for "blue cotton kurta for women under ₹800" resolves to a specific listing only if that listing's attributes are complete and correctly mapped.
03 / Customer Decision
Attributes written to help a real person decide — not just populate a database — reduce return rates and lift conversion. Fabric composition and care instructions do that. Internal SKU codes do not.
04 / AI Discovery → the new moat
LLMs and shopping agents retrieve and recommend products based on how well-structured and well-described they are. A product with sparse attributes is effectively invisible to agentic commerce. Most catalogues built before 2024 fail this job entirely.
We don't write product descriptions. We build the attribute intelligence that makes a product findable by a filter, a search engine, a shopper, and an AI.
How it works
The pipeline is modular and runs on top of your existing taxonomy, translation memory, and editorial team. You keep the linguistic assets; we add the AI engine that makes them produce more per editor-hour.
A tiered extraction approach: fast rule-based matching for structured fields, embedding-similarity for semi-structured fields, LLM extraction for unstructured prose and images. Each tier is applied only when cheaper tiers fail — keeping cost per product low at scale.
Translation memory runs first — high-confidence TM hits bypass generation entirely and are essentially free to localise on repeat passes. LLM generation produces from the structured attribute set (not English prose), eliminating an entire class of translation error.
Every listing exits with a confidence score. Above threshold: auto-publish. Below threshold: prioritised human review queue, sorted by business impact (sales rank, category margin). Every human correction retrains the confidence model.
Published listings are tracked for search appearance rate, filter-click rate, SEO rank, and add-to-cart conversion. Category-level signals feed back into the attribute model: attributes with conversion lift are elevated; dead-weight fields are removed.
Partnership models
We don't compete with your moat. We build the engineering layer underneath it. Here is how the partnership looks for the two types of business we most commonly work with in this space.
The proof sprint is the de-risking step. We take one client catalogue, run the full pipeline for 4–6 weeks, and deliver a cost-per-SKU delta and throughput comparison against your current workflow. If the numbers work, we build. If they don't, you've spent a contained discovery budget and have a clear data-driven picture of what the model would need to look like to make sense.
Proven at scale
The pipeline described above is not a reference architecture. It is what we built and operate. The flagship engagement spans tens of millions of SKUs across India's largest fashion and general merchandise marketplace — covering 8+ Indian languages, hundreds of category schemas, and a content operations workflow that handles more SKUs per editor-day than any comparable manual operation.
The same pattern scales down. The attribute model changes per category and market; the pipeline does not. A mid-size retailer running 200,000 SKUs in three languages needs the same architecture at a different parameter set — and the cost-per-product economics actually improve at smaller volumes because TM coverage matures faster.
Start the conversation
We take one client catalogue, run the full pipeline for 4–6 weeks, and deliver a concrete cost-per-SKU delta and throughput comparison. No long commitment, no bespoke build before you see the numbers.
More from the TrueLeaf Tech engineering portfolio.
Let's build
Whether you're testing a hypothesis or scaling an established product, we'd be glad to spend a half-hour helping you think through the next step.
