Reliance brands are catalog-bound. Enrichment used to be slow (days to weeks per brand), expensive (~₹100 per SKU when vendor-led), and entirely manual. AI Cataloging removes that bottleneck.
Powered by the AI Cataloging Agent → · 7-step pipeline · 15 days → 5 hours per shoot · 3-layer eval · /agents/ catalog.
A single pipeline — Google AI + Web Grounding — measured against the existing manual processes at Netmeds and Reliance Digital, then deployed to production for Tira × Akind. Below is the chain of evidence in three steps: measured, shipped, how we know.
Netmeds + Reliance Digital · same pipeline, two operating models, consistent uplift.
First release-noted deployment · 89% accuracy · enriched attributes consumed live by the Tira team.
Catalog enrichment with Gemini · same pipeline featured on the keynote stage, 29-Apr-2026.
The verdict bucketing that produces every accuracy number on this page.
AI-powered catalog creation for faster, scalable, and consistent commerce. Pipeline: Mannequin Image → AI Photoshoot Studio Visual → AI Enriched Catalog → AI Generated Video.
Web Grounding was measured against two existing catalog-enrichment processes — one fully outsourced to vendors (Netmeds), one run by an in-house team (Reliance Digital). Different starting points, different bottlenecks, but the pipeline delivers in both: time collapses, marginal cost approaches zero, first-pass coverage jumps.
~95%+ enrichment-time reduction · ~5 days → ~2 minutes per 1,000 SKUs · ₹100/SKU → near-zero marginal cost.
Catalog enrichment at Netmeds was fully outsourced — ~40 hours to enrich 1,000 SKUs across 6 attributes, ~₹100 per SKU. Web Grounding compresses the same job to ~2 minutes, automated. Vendor dependency goes away; cycle time goes from days to minutes.
| Metric | As-Is (Vendor-led) | With Web Grounding | Impact |
|---|---|---|---|
| Time / SKU | 40 hours per 1,000 SKUs for 6 attributes · ₹100 per SKU · fully dependent on 3rd-party vendors | ~2 minutes per 1,000 SKUs | ~95%+ time reduction · near-zero marginal cost · vendor dependency eliminated |
| Days to clear 1,000 SKUs* | ~5 days | ~2 minutes | Cycle time reduced from days → minutes |
* Days to clear 1,000 SKUs assumes the prior vendor-led pace.
35–40% faster per SKU · +50–64% throughput on the same team hours · 75% first-pass attribute auto-fill.
Reliance Digital runs enrichment in-house — a 5-person team at 90 minutes per SKU. Web Grounding cuts per-SKU time to 55–60 minutes, lifting daily throughput by ~half without adding people. The same hours either unlock more capacity or take cost out — the choice is the operator's.
| Metric | As-is (Manual) | With Web Grounding | Impact |
|---|---|---|---|
| Time / SKU | 90 min | 55–60 min | 35% to 40% time saved |
| Team throughput / day* | 15–20 SKUs | 28.5–32.7 SKUs | +50% to +64% additional SKUs enriched |
| Days to clear 100 SKUs* | 5.0–6.5 days | 3.5–4 days | Faster cycle time |
| Attribute fill rate by web grounding | — | 75% | High first-pass coverage |
* Team throughput / day and Days to clear 100 SKUs assume current team strength.
Team strength of 5 members. Each member ships more.
Same throughput, fewer person-hours.
What both studies show: the pipeline collapses time and cost regardless of the operating model. The next step is whether it actually ships into a live storefront. ↓ See the Tira × Akind release.
The same Web Grounding pipeline benchmarked above is now live inside Tira's production environment for the Akind brand, in collaboration with Google. The Tira team consumes the enriched attributes in production, coordinated with the Akind brand. This is the first release-noted milestone; it validates that the pipeline doesn't just measure well — it ships.
Tira maintains catalog data in production with the AI enrichment pipeline · less manual effort · validated reliability · scope to extend to full Tira catalog in the next phase.
Next phase · expand AI enrichment to the entire Tira catalog.
The 89% accuracy claim above is measured against a controlled ground-truth set per attribute (see §04 Methodology). The same pipeline that produced it was featured on the Google Cloud Next '26 keynote stage — the catalog-as-intelligence-layer foundation behind JIIA on JioMart. ↓ §03 Showcased.
Catalog Cloud now combines Gemini multimodal vision with web grounding and CAM sheet intelligence to process millions of product images and generate structured attributes that source systems didn't reliably carry — colour, style, material, usage context (occasion, intent fit), and deeper product semantics beyond titles and descriptions.
A SKU stops being a database row and becomes a queryable entity an AI can reason over. This is the foundation that makes systems like JIIA on JioMart work at scale.
Gemini multimodal vision (product images) · web grounding (open-web reference data) · CAM sheet intelligence (vendor catalog masters).
Colour · style · material · usage context (occasion, intent fit) · semantic enrichment beyond title/description fields.
Designed to feed Reliance Digital · JioMart · Tira · AJIO · Netmeds with the same attributes, vocabulary, and channel-agnostic API.
Live Tira × Akind · Measured Netmeds, Reliance Digital · Build at-scale rollout
Reliance Retail was selected as one of a handful of customer deployments featured on the Google Cloud Next '26 opening keynote alongside Vodafone, CME, Macquarie Bank, Colgate-Palmolive, and Citi. The catalog-enrichment layer is what makes JIIA's natural-language commerce work — without rich, queryable attributes the agent has nothing to reason over.
Every accuracy number on this page (including the Tira pilot's 89%) comes from this method: each enriched attribute is compared to a ground-truth value and bucketed into one of three verdicts.
Ground truth and enriched value both have the same data.
AI response has correct values but is missing some values from ground truth. Specifically useful for multi-select attributes.
AI response is missing all values in ground truth.
| Ground truth (attribute · value) | Enriched value | Verdict | Explanation |
|---|---|---|---|
| Primary_colour · Gold | Primary_colour · Gold | Exact match or similar | Ground truth and enriched value both have the same data, 'Gold'. |
| Concern · Pores, Dullness, Blemishes | Concern · Dullness | Partial Match | AI response only mentions Dullness, missing Pores and Blemishes. |
| Benefits · Lightweight, Moisturising, Nourishing, Smoothening | Benefits · Sun Protection | No Match | AI response is missing all of the benefits. |