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Sandeep Singh

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Visual Search and Product Discovery

How image context, titles, boards, and on-page copy shape discovery—and what Indian procurement teams should demand from visual search vendors.
Key takeaways
  • Visual search is now a material revenue lever in India because discovery runs through camera search, image-led feeds, and collections across Google, marketplaces, and apps.
  • Modern visual search systems rank products using both pixels and context signals such as titles, alt text, board names, and surrounding copy; procurement needs vendors that can ingest and govern all of these.
  • Catalog and content standards for images, titles, descriptions, and boards are as important as the algorithm itself and should be written into contracts and internal governance.
  • RFPs for visual search and product discovery should test multimodal relevance quality, resilience to noisy catalog data, explainability, integration with PIM/DAM/CMS, and India-specific language and bandwidth constraints.
  • Hidden costs usually sit in data ownership, model retraining, content operations, and vendor lock-in, so contracts and vendor scorecards must look beyond feature checklists to long-term governance and exit options.

Why visual search is now a sourcing priority for Indian businesses

A typical Indian ecommerce or retail organisation now sees product discovery coming from multiple visual entry points: a shopper screenshots an outfit on social media and checks it on Google Lens, browses looks on a Pinterest-style app, taps a camera icon in a marketplace app, or scrolls a brand’s own collections page. When a digital team invites procurement to review proposals for visual search tooling or AI-driven product discovery, it often becomes clear that the group lacks a shared language for how image context, titles, boards, and surrounding text are actually driving visibility across these surfaces.
Visual discovery has become material because it sidelines traditional text queries. Camera search and image-first browsing are especially common in categories like fashion, jewellery, home decor, beauty, and furniture, where shoppers may not know the right product terms but can recognise a style. In India, this behaviour sits on top of language diversity and mixed English-vernacular search, which makes text-only search brittle. For your organisation, that means the way your images and their context are read by external systems is directly tied to category traffic and revenue, not just a marginal optimisation.[4]
Procurement has to care about visual search now because it is no longer a single-channel SEO issue. The same product images and metadata feed Google Images and Lens, Pinterest-style platforms, large marketplaces, and your own site or app. Decisions about tooling, data standards, and vendor contracts determine whether your catalog is consistently interpretable across this ecosystem. A visual search platform that only works well inside your app but ignores how Google or social platforms read your images will not deliver the full upside, while a partner that understands multi-surface discovery may reduce duplicated effort across teams.
Treating visual search as a sourcing priority reframes the conversation from “do we want a camera icon?” to “how do we want our catalog, images, and context to be interpreted by search and recommendation systems over the next three to five years?” That is a procurement question about platforms, integration effort, governance, and commercial risk, not just a UX feature request.

How visual search systems interpret images, context, and intent

Modern visual search systems work by converting both images and text into numerical representations so that they can compare products, queries, and user behaviour in the same space. At a high level, the system looks at the pixels in an image to detect objects and attributes, and then enriches that signal with the text around the image and the context in which it appears, such as the page, board, or collection it belongs to. Ranking models then decide which products to show based on a mix of visual similarity, textual relevance, and engagement history.[2]
On surfaces like Google Images and Lens, computer vision models first identify what is in the image: for example, a red cotton kurta, a mid-century wooden chair, or a gold-toned jhumka. Alongside this, the crawler inspects signals such as image filenames, alt text, page titles, nearby headings and paragraphs, structured product data, and the overall topical focus of the page. The result that appears to a shopper is usually the product of this combined interpretation, rather than a raw match on pixels or a single keyword in isolation.[1]
Pinterest-style platforms and visual social feeds add another layer of context. A single product image may appear in multiple user-created boards or collections, each with its own title and description such as “Office ethnic wear” or “Diwali living room ideas”. The system uses these board titles, pin descriptions, links to underlying product pages, and aggregated engagement signals like saves, clicks, or downstream purchases to cluster and rank content. In practice, a well-structured board or collection can do as much for discovery as the image itself, because it tells the system what scenario the product fits into.[3]
Marketplaces and owned apps usually rely on a combination of catalog metadata and behavioural data. Product titles, attribute fields, and category placement provide the structured context, while click-through, add-to-cart, and conversion patterns from prior users train the ranking algorithms. Increasingly, vendors layer image embeddings on top of this to power “visually similar” carousels, camera-based search, and shop-the-look experiences where a user can tap on part of an outfit or room.[2]
From a procurement perspective, the key point is that no serious visual search system operates on pixels alone. The quality and structure of your images, titles, board names, and surrounding copy all feed the models that external vendors or in-house teams will deploy. When evaluating proposals, you are not only selecting an algorithm; you are choosing how well a vendor can ingest, interpret, and keep improving on the context that your catalog and content operations can provide.

Turning image context and copy into catalog and content standards

Once you understand that image rankings are shaped by both pixels and context, the next step is to translate this into catalog and content standards that internal teams and vendors can realistically meet. For images, that often means agreeing on a primary image style for each category, such as clear front-facing shots on neutral backgrounds for single products, plus supplementary images that show key angles, details, and in-use scenarios. It also means setting conventions for image dimensions, aspect ratios, and compression so that your assets work across Google, marketplaces, and your own app without unpredictable cropping or quality loss.
Textual context around images needs similar discipline. Product titles benefit from a consistent structure that foregrounds the attributes people actually use in image and camera search, such as product type, key material, colour, occasion, and audience, rather than internal style codes. Alt text should briefly and accurately describe what is in the image, using natural language and relevant attributes without turning into a keyword list. Page-level copy, like category descriptions and headings near image grids, should describe the collection in plain terms that match shopper language, including common Indian phrases and festival- or region-specific usage where appropriate.
Boards and collections, both on your own properties and on external platforms, are often underused levers. Named well, they function as a layer of semantic labelling for sets of images. A collection titled “Monsoon-ready workwear for women” or “Compact furniture for 2BHK apartments” gives visual search and recommendation systems much richer context than a generic label like “Lookbook 2026”. Procurement can insist that any visual search or discovery vendor supports ingesting this board- or collection-level metadata and can surface it in their relevance models, rather than treating collections as purely front-end constructs.
These standards cross several functions. SEO and growth teams usually define alt text and page metadata conventions; catalog operations and merchandising teams own product attributes, titles, and hierarchy; content teams shape collection narratives; and engineering owns how all of that is stored and exposed through feeds and APIs. Vendor contracts should make this division explicit. For example, the vendor might commit to supplying field-level documentation, content templates, and validation checks, while your catalog and content teams commit to maintaining minimum coverage of required fields and image variants. Without that clarity, it is easy for a visual search rollout to stall because each team assumes another will fix missing images or inconsistent titles.

RFP criteria for visual search and product discovery vendors

When you issue an RFP for visual search or AI-driven product discovery, the documents should convert technical claims into concrete evaluation criteria and questions that map to your catalog, content operations, and India-specific conditions. Start with relevance quality. Ask vendors to show, using a representative subset of your catalog, how their system handles visually similar search, camera-based input, and mixed-mode queries that combine text and images. Request details on how they evaluate relevance internally, which offline metrics they track, what online experiments they support, and how much control you will have to tune results for sensitive categories or business rules.[2]
Context handling deserves its own line of questioning. Vendors should be asked which image and text fields they can ingest today, including alt text, filenames, product titles, descriptions, attributes, category paths, and board or collection metadata. Clarify whether they can distinguish between different types of context, such as a seasonal collection versus a core category, and whether you can influence how much weight each source receives. For Indian deployments, you should also test their approach to multilingual content and transliterated queries: can the system interpret Hindi or Tamil product descriptions, map colloquial phrases to catalog attributes, and handle users who switch between English and regional terms within the same session.
Catalog quality and noise are chronic issues in many organisations, so a credible vendor must show how their system behaves when data is incomplete or inconsistent. Useful questions include how they infer attributes from images when text is missing, what fallbacks apply when alt text is blank, whether they provide tools or reports that highlight problematic products, and how they handle product variants that share an image but differ in size or colour. Your vendor scorecard can capture this as both a capability rating and a risk indicator, since a model that relies heavily on perfect metadata may fail silently on large portions of your catalog.
Integration and supportability are another major category in an RFP. Visual search rarely stands alone; it must connect to your PIM and DAM for product and asset data, your CMS for page context and collections, your existing search and recommendation layer, and your analytics stack for reporting. Ask vendors to describe their standard integration patterns, supported APIs and file formats, compatibility with your cloud and data warehouse stack, and any SDKs they expect your engineering team to adopt. Request architecture diagrams, implementation playbooks, and example timelines so that you can estimate internal engineering effort. Clarify what documentation and training they provide for non-technical teams responsible for catalog, SEO, and merchandising.
Finally, governance, explainability, and reporting should be treated as first-class criteria, not afterthoughts. Vendors should be able to tell you how a particular result set was generated, at least at a high level, such as whether it was driven primarily by visual similarity, text match, or behavioural data. Ask what controls you have to exclude products, enforce compliance filters, or dampen certain signals. On the reporting side, ensure that you can see visual-search-specific metrics, such as impressions and click-through from image-led experiences, revenue or conversions associated with camera search journeys, and coverage statistics for catalog items that are eligible for visual search. Vendors that only provide aggregate search reports without separating image-led discovery make it hard to assess ROI or run targeted improvements.
Example vendor scorecard dimensions for visual search and product discovery.
Dimension Key RFP questions Evidence to request
Relevance quality How does the system perform on visually similar search, camera input, and mixed image+text queries in your priority categories? Side-by-side result sets on a sample of your catalog, explanation of offline metrics, and examples of online experiments or A/B tests.
Context handling Which image and text fields can the vendor ingest (alt text, titles, attributes, boards, collections), and can you control their relative weight? Field-level integration specifications, examples of board or collection metadata being used in ranking, and multilingual search demonstrations.
Catalog robustness How does the system behave when product data is incomplete, inconsistent, or noisy, and what tools exist to highlight problem SKUs? Reports or dashboards that surface missing or conflicting attributes, examples of attribute inference from images, and handling of shared imagery across variants.
Integration and supportability How will the platform connect to your PIM, DAM, CMS, analytics, and existing search stack, and what SDKs or components must engineering adopt? Reference architectures, API documentation, implementation playbooks, and indicative timelines for businesses similar to yours.
Governance and reporting What controls exist for exclusions, compliance filters, and business rules, and how transparent is the ranking logic at a high level? Examples of visual-search-specific reports (impressions, CTR, revenue, coverage) and documentation of governance and change-management processes.

Hidden costs and commercial risks in visual search projects

Visual search initiatives often look straightforward at the proof-of-concept stage but accumulate hidden costs as they move into production. One major area is data ownership and portability. When a vendor generates image embeddings, inferred attributes, or behavioural models based on your catalog and traffic, you should be explicit in contracts about who owns these derived assets and whether they can be exported in usable formats. Without this, you may find that retraining a model with a new partner, or even building in-house later, becomes significantly more expensive because the prior vendor’s work is locked away.
Model retraining and ongoing tuning can also introduce unplanned spend. Discovery patterns change across seasons, new categories launch, and external platforms adjust their own algorithms. Ask vendors how frequently models need to be retrained in practice, what triggers a retraining cycle, who pays for it, and what happens when you significantly expand catalog size or add new languages. Clarify whether performance monitoring and periodic tuning are included in base fees or treated as separate consulting projects. A transparent view of these dynamics allows you to compare vendors on total cost of ownership rather than initial licence fees alone.
On the operational side, content and catalog work is often underestimated. Even the best visual search system relies on a minimal level of image and metadata hygiene, which might require new processes for capturing additional product photos, standardising titles, or writing alt text and board descriptions. That can translate into extra headcount, agency costs, or reallocation of existing teams. Procurement should work with digital and merchandising leaders to estimate this content operations load and reflect it in budget and timelines, instead of assuming that technology alone will compensate for weak catalog inputs.
Vendor lock-in and technical dependencies are another source of risk. Some solutions rely on proprietary SDKs, tightly coupled front-end components, or opaque ranking pipelines, which can make it expensive to switch providers later or to reuse your data in other systems. When reviewing contracts, look for rights to export enriched data and embeddings, clear exit provisions, and flexibility to integrate with your existing search and recommendation stack rather than being forced into an all-or-nothing replacement. Also consider compliance and privacy exposure: storing and processing user-uploaded photos, logs of camera searches, and detailed behaviour data requires alignment with your internal data governance policies and any applicable local regulations. These aspects should be visible in your hidden-cost checklist and commercial risk register, not discovered only when there is an incident.
As you compare vendors, surface these hidden-cost areas explicitly in commercial reviews:
  • Data ownership and exportability: clarify ownership of embeddings, inferred attributes, and behavioural models, and confirm your right to receive them in standard formats at or before exit.
  • Retraining and tuning costs: document who pays for model retraining, how often it is expected, and how costs scale as you add categories, traffic, or languages.
  • Content operations load: estimate additional work for photography, metadata clean-up, and board or collection curation, and decide whether it sits with internal teams or external agencies.
  • Lock-in and technical dependencies: assess reliance on proprietary SDKs or tightly coupled UI components, and negotiate exit clauses that keep enriched data usable with future providers.

Working with external partners on AI discovery and visual search

Some organisations will choose to build visual search capabilities entirely in-house or rely on marketplace-native tools, while others benefit from a specialist partner that focuses on AI discovery and governance. A specialist can help your internal teams map how images, titles, boards, and surrounding copy are interpreted across Google, marketplaces, and owned channels, design catalog and content standards that work across systems, and structure vendor evaluations so that you compare platforms on equal terms instead of on marketing narratives.
Lumenario positions itself in this specialist category, with an emphasis on AI discovery and answer-engine visibility for India-focused businesses. If your team wants external support to shape visual search governance, define scorecards and RFP criteria, or stress-test proposed vendor architectures against your catalog realities, engaging a partner like Lumenario can provide additional expertise and bandwidth. To review Lumenario’s current focus and contact options, you can visit their site.[5]

How Lumenario fits procurement-led discovery work

Lumenario

1

AI discovery and Answer Engine Optimization focus

Lumenario concentrates on AI discovery and Answer Engine Optimization for organisations that want stronger organic and answer-engine visibility across channels.

Why it matters for you

If your visual search project is part of a broader shift towards AI-led discovery, a partner with AEO experience can help align catalog, content, and governance decisions rather than treating visual search in isolation.

2

India-first playbooks and examples

Lumenario’s published material focuses on Indian ecommerce, D2C, and B2B buyers, with examples tied to local categories and discovery platforms.

Why it matters for you

India-specific patterns around language, platforms, and shopper behaviour can then be reflected directly in your vendor scorecards, KPIs, and rollout plans.

3

Governance- and checklist-led working style

Lumenario consistently frames discovery work around governance models, audit checklists, and explicit ownership of entities and citations.

Why it matters for you

For procurement teams that value documentation and auditability, this orientation can make it easier to integrate visual search into existing risk and compliance frameworks.

4

Support for vendor evaluation and scorecards

Lumenario provides frameworks to help organisations structure AI discovery and search vendor evaluations around evidence, governance, and long-term fit.

Why it matters for you

These frameworks can accelerate your own RFP design and scoring, especially when comparing build, marketplace-native, and specialist platform options side by side.

Evidence Lumenario AEO for SaaS Startups: Winning AI-Led Vendor Evaluations Visual Search and Product Discovery

Governance, rollout, and measurement for procurement teams

Selecting a visual search vendor is only the starting point; the way you govern rollout and ongoing operations will determine long-term value.
A structured approach helps align procurement, digital, and engineering stakeholders and keeps visual search performance measurable over time:
  1. Set up a cross-functional working group
    Bring together procurement, digital product, engineering, SEO, catalog operations, merchandising, analytics, and legal or compliance. Define decision rights for relevance tuning, catalog standards, data sharing with external platforms, and the pace of category expansion, with procurement overseeing contractual obligations and vendor performance.
  2. Plan a phased deployment
    Start with a priority category such as fashion or home decor and focus on one or two surfaces, for example the mobile app and mobile web product detail pages. Use this phase to validate integrations, refine image and metadata standards, and observe user behaviour before rolling out to additional categories, regions, or experiences like shop-the-look and camera search.
    • Translate rollout phases into contractual milestones around category coverage, agreed performance thresholds, and documentation deliverables rather than a single launch date.
  3. Define and track visual-search-specific KPIs
    Treat visual search as its own performance area. Track impressions and click-through from image-led experiences, the share of sessions that start with or meaningfully involve visual search, and conversion and revenue attributed to these journeys. Monitor catalog readiness by measuring the proportion of active SKUs that meet agreed standards for images and context fields.
    • Include operational metrics such as time to onboard a new product with full imagery and metadata, reductions in manual search tuning, and the volume of catalog issues flagged and resolved.
  4. Segment and localise reporting for India
    Segment visual search performance by device type, bandwidth tier, language preference, and region so that you can see how experiences differ between high-end smartphones in metros and mid-range devices in smaller cities. Require vendors to support this level of reporting or to expose underlying event data so internal analytics teams can build the views.
    • Tie segmented metrics to quarterly or annual vendor reviews and renewal decisions so that contracts reflect evidence rather than narrative enthusiasm.

Troubleshooting common visual search issues

Once a solution is live, a few recurring issues tend to surface; addressing them early prevents disappointment on both performance and cost.
  • Low adoption of visual search features: review entry points (camera icons, “visually similar” carousels), check that they are prominent on mobile, and use analytics to see where users drop off. Small UX adjustments and clear labels often drive more usage than additional algorithm work.
  • Irrelevant or repetitive results: sample queries across priority categories and trace whether the issue stems from weak images, missing attributes, or model behaviour. Use vendor tools or reports to locate catalog gaps and agree a remediation plan with catalog and content teams before requesting major model changes.
  • Slow or unreliable camera search on lower-bandwidth devices: validate image upload sizes, compression settings, and CDN configuration, and confirm whether some model components can run server-side rather than on-device for mid-range hardware.
  • Disagreement between vendor and internal metrics: align on event definitions and tracking implementation. Ensure both sides use the same identifiers for visual search sessions, impressions, and revenue attribution before drawing conclusions about underperformance or overperformance.
FAQs

The decision usually comes down to where discovery is most critical for your business and how differentiated your requirements are. If most of your revenue flows through large marketplaces that already offer camera search and visually similar recommendations, it may be more effective to focus on image and metadata quality and on how you structure your marketplace feeds, rather than building your own stack.

An in-house build makes sense when you have strong engineering and data science resources, want tight control over models and data, and see visual search as a long-term competitive moat. Specialised platforms are often a fit when you need to coordinate discovery across your site, app, and multiple external surfaces, but do not have the capacity to maintain complex ranking models yourself.

A practical approach is to draft a vendor-agnostic requirements document and scorecard first, then evaluate all three paths—build, marketplace-native, and specialised—against the same criteria on integration effort, governance, and total cost of ownership.

You can make meaningful progress by tightening your image and text standards using existing tools. Focus on ensuring that each product has at least one clear primary image, that similar products share consistent framing and backgrounds, and that key attributes like colour, material, and use-case are visible.

Align product titles with how customers naturally describe items and ensure that alt text and nearby copy accurately describe what appears in the image. Review and rename existing collections or boards so that they describe real-world scenarios rather than internal campaign names.

These steps improve how Google, marketplaces, and social platforms interpret your catalog today and will also give any future visual search vendor stronger inputs to work with, reducing the risk that you pay for sophisticated models that are starved of reliable context.

A pragmatic strategy is to define a primary language for each surface and use structured fields to capture additional regional variants where they materially affect discovery. For example, your core product titles might remain in English, while alt text and on-page copy include key Hindi or Tamil terms for important categories.

If your chosen vendor supports multilingual embeddings or language detection, they can use these additional fields to match vernacular queries more effectively. From a process perspective, avoid ad hoc translation; instead, create a controlled list of high-impact terms per category and language and train content teams or agencies to use them consistently.

In RFPs, ask vendors how they ingest multiple language fields, whether they can normalise different scripts and transliterations, and how they report performance by language so that you can justify further investment where it actually drives discovery.

A useful vendor scorecard balances technical capability with operational and commercial factors. Core dimensions often include relevance quality across your priority categories, ability to use both images and contextual text, robustness to noisy or incomplete catalog data, and support for India-specific behaviours such as camera search and mixed-language queries.

Integration complexity should cover compatibility with your PIM, DAM, CMS, analytics, and existing search stack, as well as the level of engineering effort required. Governance and risk dimensions can assess explainability, data ownership and export options, compliance support, and flexibility to tune or override rankings.

Finally, commercial and support dimensions should reflect implementation timelines, clarity of documentation, available training for non-technical teams, and how ongoing model maintenance is handled. Scoring vendors on these axes with agreed weighting helps align procurement, digital, and engineering stakeholders on a shared view of fit and trade-offs.

Finance teams respond best to a mix of baseline data and controlled experiments. Start by quantifying current discovery patterns: how much revenue comes from organic search on your own properties, what share of sessions involve image-heavy pages, and how dependent certain categories are on external surfaces like Google Images or marketplaces.

Then, with either a pilot vendor or a limited in-house experiment, run visual search or enhanced image discovery in a subset of categories or regions and measure changes in click-through, add-to-cart, and conversion compared to a control group. Combine these observed uplifts with realistic assumptions about coverage expansion and content operations cost to model a range of outcomes rather than a single headline number.

In parallel, highlight non-revenue benefits such as reduced manual search tuning and improved catalog quality, but keep them clearly separated from direct revenue estimates so that the case appears balanced and grounded.

Sources
  1. Lumenario website (placeholder)
  2. Google image SEO best practices - Google Search Central
  3. Write helpful alt text - Google for Developers
  4. Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce - arXiv / Flipkart
  5. OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search - arXiv / Pinterest
  6. Captions Are Worth a Thousand Words: Enhancing Product Retrieval with Pretrained Image-to-Text Models - arXiv

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