Did you know ChatGPT, Gemini, and other AI agents still struggle with accurate real-time product information?
Not because the models are bad - but because commerce data itself is extremely difficult to process in real time.
Most AI systems and shopping agents still rely on:
1) Traditional scrapers
2) Static page parsing
3) Generic web extraction tools
The problem? They often miss how e-commerce actually works.
For example: The same product can show different prices, delivery times, availability, sellers, and discounts - based purely on location or pincode.
But most scraping systems are not location-aware.
Another major issue: product variations. A single iPhone listing may contain different colors, storage options, prices, and availability statuses.
Most systems treat this as one static page. Reality is far more dynamic.
And finally - many traditional scrapers still return raw HTML or markdown instead of structured commerce intelligence.
That makes life harder for ChatGPT, Gemini, AI agents, shopping copilots, analytics systems, and pricing engines alike.
We believe a large percentage of commerce data consumed by AI today is still incomplete or inaccurate.
So we started building infrastructure to solve this - properly.
Turns out, commerce intelligence is much harder than it looks.
