The impact
Data Points Processed Daily
0M+
real-time ingestion at zero latency and zero data loss, at a scale manual processing could never touch
Retailers on Automated Refresh
0+
synced across 20+ categories on a 24-hour cycle that once took days
Market Opportunity Unlocked
$0T
reframing a fragmented pricing problem as North America's largest e-commerce advantage
Zero Manual Intervention at Scale
0%
autonomous spiders and pipelines replacing a model that was breaking under its own weight
Overview
In North America’s e-commerce battlefield, a single dollar separates a conversion from an abandoned cart and the shopper isn’t just comparing products, they’re racing against an algorithm. A price comparison platform serving North American and Canadian shoppers across 500+ retail brands and 20+ categories had one mandate: become the single most trusted source of real-time pricing intelligence for millions of shoppers making decisions in seconds.
NeoSOFT’s diagnostic exposed the gap between that vision and the platform’s reality. Every one of 500+ retailers ran on a different architecture, a different data format, and a different definition of “real-time” and prices shifted hourly across millions of SKUs with no automated mechanism to keep pace. A manual processing pipeline was producing stale data, the single biggest destroyer of trust in a price intelligence product. There was no personalization layer; every shopper saw identical data regardless of intent or context. And the scaling model was already buckling before the platform had even reached its growth phase.
The real crisis wasn’t data volume, it was the absence of intelligence behind the data. Raw price aggregation without context isn’t a product. It’s a spreadsheet.
NeoSOFT didn’t come in to patch the existing model. We came in to rebuild the platform’s decision-making core from the ground up, replacing manual chaos with an autonomous, self-sustaining intelligence engine that reads shopper intent, refreshes itself, and scales without a ceiling.
The objective
From Reactive Price List to Proactive Shopping Advisor
The client’s goal was to close the gap between a sharp market vision and a fragile execution model, transforming a manually-dependent price aggregator into a real-time, intelligence-driven platform shoppers could trust without hesitation.
That meant re-architecting the platform at every layer: from how data was acquired across 500+ disparate retailer systems, to how it was processed at scale, to how it was ultimately served — not as a static price list, but as a personalized, decision-grade recommendation.