IndiaBanks.org — trustworthy bank comparison built around evidence

1) The problem

Indian banking information is abundant but fragmented. Institution status, product terms, rate tables, fees, eligibility, waivers, reward conditions, and effective dates can live across different pages and PDFs. Users need a quick comparison, but a useful answer must preserve the context that makes each number meaningful.

• Discovery: find a bank or the right product category quickly.
• Comparison: compare only compatible fields and units.
• Trust: let users open the official source behind a material fact.
• Maintenance: distinguish current, reviewed information from incomplete or inactive records.

2) Evidence-first data model

The directory begins with institution records and RBI-oriented classification, then adds products only when a current official page or clearly scoped official document establishes them. Important facts carry their display value, normalized value where useful, unit, source URL, source type, context, and review date.

Unknown values remain unknown. Interest rates, APR, monthly finance charges, cashback, reward earn rates, annual fees, renewal fees, balances, benchmarks, and repayment periods stay separate rather than being flattened into misleading generic fields.

3) Comparison logic that understands financial values

Comparison tables parse values as more than strings. Sorting and favourable-value signals consider currency, percentages, durations, thresholds, and ranges, including whether a lower or higher number is beneficial for that particular field. Overlapping ranges and incompatible units remain neutral.

4) A professional research experience

The interface uses a restrained Indian financial-document visual language: warm paper, deep green, precise borders, compact data surfaces, and purposeful motion. Bank logos and product imagery aid recognition, while responsive cards adapt to varied image proportions and keep related products visually aligned.

• Focused comparison routes reduce irrelevant columns.
• Product pages pair a strong identity area with facts, provider context, and source evidence.
• Official external pages open separately so users do not lose their research state.
• Mobile layouts keep search, filters, tables, and cards usable without fixing the first column.

5) SEO, AEO, and machine-readable access

Discovery is part of the product architecture rather than a final metadata pass. Canonical pages, structured data, crawl controls, social imagery, and machine-readable descriptions share the same identity and describe only visible, supported content.

• Organization, WebSite, WebPage, CollectionPage, BreadcrumbList, ItemList, and image entities are linked through stable identifiers.
• Dynamic sitemap records indexable banks, products, categories, and subcategories.
• llms.txt and llms-full.txt explain scope, provenance, limitations, and useful routes.
• A read-only MCP surface exposes discovery, tools, and resources for compatible clients.
• OpenSearch and public JSON indexes support direct search and data discovery.

6) Cloudflare delivery and quality controls

The application uses Next.js-compatible tooling with Cloudflare-oriented production delivery. Automated validation checks data integrity, evidence, assets, duplicated product imagery, generated indexes, and builds before publication. Temporary extraction files stay outside the deployed product and are cleared as research moves forward.

• Optimized local brand, bank, and product imagery avoids unnecessary repeated downloads.
• Validation prevents unsupported records from silently entering public pages.
• Accessibility, responsive behavior, reduced-motion preferences, metadata, and machine endpoints are treated as release requirements.

7) What the product demonstrates

IndiaBanks.org demonstrates how Azonova combines research operations, structured data design, document handling, domain-aware comparison logic, frontend product design, technical SEO, AI-readable interfaces, and production deployment into one maintainable public product.