Product Image Tagging & Cataloguing
Automated product tagging reads your product photographs and generates the attributes, categories, alt text and descriptions that your catalogue is missing. For a retailer with thousands of SKUs and a data team of two, it turns a job measured in months into one measured in days.
Who it's for
E-commerce sellers and marketplaces with thousands of SKUs.
What changes
A catalogue that is actually searchable, built in days.
- Starting at
- ₹1,50,000
- Timeline
- 6–12 weeks
- Category
- AI Vision Systems
- Built from
- Vashi, Navi Mumbai
Key takeaways
- A 10,000-SKU catalogue can be enriched in a few days rather than several months of manual work.
- Colour, pattern, material appearance, style and category extract reliably from a decent photograph.
- Price, exact fabric composition and care instructions cannot be seen and must not be guessed.
- Better attributes improve search, filtering and marketplace acceptance immediately.
- Projects run 4–8 weeks from ₹2,00,000 depending on catalogue size and attribute schema.
The catalogue problem in plain terms
Most Indian retailers have product data that was entered fast, by different people, over several years. Titles are inconsistent, half the attribute fields are empty, categories are approximate and alt text does not exist.
The consequences are concrete. On-site filters return nothing because the attributes are blank. Marketplace listings get rejected or buried for incomplete data. Search does not surface products that exist. Accessibility audits fail on missing alt text.
Fixing it manually means a person looking at each product and typing twelve fields. At 10,000 SKUs that is months of work nobody has budget for, which is why it never gets done.
What can and cannot be read from a photograph
The boundary here is straightforward, and respecting it is what separates a useful enrichment run from one that fills your catalogue with confident errors.
| Attribute | From image? | Confidence |
|---|---|---|
| Primary and secondary colour | Yes | Very high |
| Pattern (stripe, floral, solid) | Yes | Very high |
| Category and sub-category | Yes | High |
| Sleeve length, neckline, fit | Yes | High |
| Apparent material (denim, silk-like) | Partly | Moderate |
| Occasion and style tags | Yes | Moderate to high |
| Exact fabric composition | No | Must come from data |
| Care instructions | No | Must come from data |
| Dimensions and weight | No | Must come from data |
Generating text that is not visibly machine-written
Descriptions and alt text are the outputs clients are most nervous about, reasonably so — a catalogue full of identical-sounding paragraphs is worse than empty fields.
The approach that works is grounding every sentence in extracted attributes plus your existing product data, writing in a defined brand voice with varied sentence structure, and keeping descriptions short. Two tight sentences that say something specific beat a generated paragraph of adjectives.
Alt text is different and easier. It should be plainly descriptive for a screen reader user — "navy blue cotton kurta with mandarin collar and half sleeves" — not marketing copy. Getting this right also happens to help image search.
Where the enrichment shows up commercially
The benefit is not the tagging. It is what better data enables downstream.
- Filters that work. Customers filtering by colour and sleeve length find products, instead of an empty results page.
- Marketplace listings. Amazon, Flipkart and Myntra all reward complete attribute data with better placement and reject incomplete listings outright.
- Site search. More searchable text per product means more queries that return something.
- Recommendations. Similarity between products depends entirely on the attributes you have recorded.
- Accessibility and image SEO. Alt text on every image, which is both a legal expectation and a source of image search traffic.
How a run actually works
We start by defining your attribute schema — which fields, which permitted values, which are mandatory. This is the step clients want to skip and the one that determines whether the output is usable. A free-text colour field produces "navy", "navy blue", "dark blue" and "midnight" for the same shade, and your filters remain broken.
Then a calibration batch of 200–500 products with full human review, threshold tuning against that, the full run, and a review pass on flagged items.
Afterwards the pipeline stays connected to your product feed so new SKUs get enriched on arrival. That is what stops the catalogue drifting back into its previous state within a year.
Scope and pricing
Four to eight weeks from ₹2,00,000 for a typical catalogue up to around 20,000 SKUs, including schema definition, the enrichment pipeline, integration with your commerce platform and the review interface.
Processing cost per product is small — usually under ₹2 per SKU including multiple images and generated text — so the one-time run on even a large catalogue is a modest line item next to the setup.
You receive the pipeline on your infrastructure, the schema, the review tool and full source. The pipeline runs on your schedule against new products without us.
FAQ
Product Image Tagging & Cataloguing — your questions
How good does the photography need to be?
Reasonable, not professional. Clean product shots on a plain background give the best results. Lifestyle images with models work well for style and occasion tags but less well for exact colour, because lighting shifts it. Poor photographs — dark, cluttered background, product at an angle — produce unreliable attributes, and the honest answer is that fixing the photography helps more than any model change. Where a product has several images we use them together, which improves accuracy considerably.
Can it handle Indian apparel categories properly?
Yes, with schema work. General models know kurtas, sarees and lehengas but tag them loosely. Defining your own taxonomy — the specific categories, work types, drape styles and occasions your business uses — and constraining the output to those values is what makes the results usable. This is a day or two of work with your merchandising team and it is the highest-value part of the whole project.
Will the generated descriptions hurt our SEO?
Not if they are specific and genuinely different per product. What Google penalises is bulk-generated near-identical content, which is exactly what you get from a template with variables swapped. Grounding each description in that product's real extracted attributes produces text that differs meaningfully between products. We measure similarity across the generated set before publishing, and if it is too high, the generation approach changes rather than being published anyway.
What about products where the image is misleading?
It happens — a colour that photographs differently, a product shot that shows an accessory not included. This is why the review queue exists and why extracted attributes should be treated as a strong draft rather than truth. Where your existing data contradicts the image, existing data wins; the model fills gaps rather than overwriting known values. That rule is built into the pipeline rather than left to judgement.
Can we run it on our own without you afterwards?
Yes, that is the intent. The pipeline is deployed on your infrastructure with a simple interface for running a batch, reviewing flagged items and adjusting thresholds. Adding a new attribute to the schema is a configuration change rather than development. We would expect to be involved only if you change commerce platforms or want a substantially different output format.
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Next step
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