Enhancing Digital Interactions with Natural Language

AI assistants are becoming the default interface for accessing digital information. We must rethink how websites are structured. This isn’t just for search engines but also for machines that converse. Over the past few weeks, I’ve been exploring and prototyping a concept I call NLWeb. It is a new layer of the web designed from the ground up. This layer supports Natural Language interactions with product data, content, and commerce experiences.

🧠 What is NLWeb?

NLWeb (Natural Language Web) is a machine-readable, semantically rich layer on top of product and content websites that enables intelligent systems (like AI assistants, bots, or voice interfaces) to:

Understand product features, variants, and use cases
Answer questions using structured and contextual data
Deliver personalized or filtered results conversationally

Think of it as the evolution of SEO, Product Feed, and Content Management System(CMS) content. It is purpose-built for LLMs and search interfaces of the future.

🧱 The Building Blocks

I began by analysing real-world e-commerce websites— to understand product catalogs, content structures, and underlying metadata. The goal was to create a unified product ontology and CMS schema that will feed into a machine-readable, queryable interface.

The key components include:

1. Deep Product Ontology using Schema.org

Each product is defined using:

Product, Offer, and Review schema extensions
additionalProperty for customization options
isVariantOf to handle blank/sample/custom products
Enrichment with BlogPosting, HowTo, and FAQPage schema for content integration

2. Flattened, Query-Optimized JSON-LD Feeds

I generated tab-separated product feed files where:

The key is the product page URL
The value is a fully enriched and flattened JSON-LD block.
This structure allows fast parsing and indexing. It enables direct natural language query matching per product.

3. Broken URL & Media Fixing

One of the early challenges was that many product image URLs and even product pages were returning 404s. So I developed validation scripts to:

Detect unreachable links
Fix image references or substitute from alternate CDN sources
Flag missing or inconsistent metadata

🔍 What Can NLWeb Answer?

Once the ontology was stable, I started designing natural language query patterns NLWeb should support. These include:

“Show me a custom printed mailer box for cosmetics under $2.”
“What is the difference between sample and blank display boxes?”
“Give me eco-friendly packaging options for a DTC skincare brand.”
“Are there any how-to guides for assembling tuck top boxes?”

Each query is backed by enriched metadata, blog content linkage, and attribute-level understanding—something traditional search and filters don’t deliver.

🌐 Use Cases for NLWeb

Voice Commerce & Smart Assistants
Allow Alexa/Google Assistant-style voice ordering interfaces using structured product feeds.
LLM-Driven Customer Support
Train LLMs using enriched product schema to handle FAQs, comparison queries, and onboarding content.
Next-Gen B2B Product Discovery
Let procurement teams search complex custom product catalogs using natural language, not rigid dropdowns.
Semantic SEO & AI-Ready Feeds
Supercharge your site’s discoverability for AI-powered crawlers and answer engines.

🚀 What’s Next?

Automated Feed Generation: Building scripts to scrape, validate, and generate JSON-LD for large catalogs.
Blog ↔ Product Mapping: Enriching product pages with structured content from how-tos and industry guides.
Realtime Query Layer: Deploying a conversational UI layer that queries the structured feed in real time.

🛠️ Tech Stack and Tools Used

Follow local development instruction here . I was working with Chatgpt , Open AI API and Qdrant local for vector DB.

schema.org vocabularies
Custom JSON-LD generators
Python for scraping and feed validation
RSS Feed creation for product and blog integration
Mac-hosted JSON-LD local development setup

🧩 Final Thoughts

NLWeb isn’t just a new format—it’s a new mindset. It’s about making the web understandable to machines in the way humans use it. It combines the best of SEO, PIM, CMS, and AI to make data fluid, intelligent, and conversational.

I’ll continue building NLWeb as an open prototype. I will be sharing updates. This will happen as I integrate more content sources. I will refine the ontology and launch the conversational layer.

Everyday Reflections