
A leading global online platform, with a user base exceeding several billion accounts, faced a critical challenge: combating sophisticated criminal activities like selling illicit goods, human exploitation, fraud, and money laundering. Investigators were forced to contend with massive, complex data, making it difficult to uncover the true scope of these illicit networks.
The crimes they were facing were purposefully obfuscated, making them complex to investigate within such a vast dataset. Following a lead required traversing through many intermediaries, and the data volume grew rapidly with each step. Their existing solution couldn’t cope with this combination of depth and data size and left investigators with two broken workflows:
During a joint workshop, DataWalk's scalable Graph & AI platform demonstrated an unprecedented capability to visually explore complex and deeply hidden connections while maintaining the full scope of the multi-billion entity data universe.


With a user base of several billion accounts and managing billions of daily messages, this platform was a constant target for illicit activity. Their dedicated team of hundreds of investigators frequently responded to law enforcement requests, but their methods were slow and limited. A single complex case could take weeks to resolve.
The core issue was not just the volume of data, but the inability to analyze relationships across multiple degrees of separation. While direct connections were easy to find, the real challenge was in tracing indirect relationships several hops away without hitting application data limits.
This meant investigators could not effectively explore the full scope of illicit networks. They were often stuck, unable to even formulate the right questions because the true extent of the activity was unknown.
DataWalk redefined how the customer analyzed vast, interconnected datasets. During a joint engagement, DataWalk rapidly built a unified knowledge graph from billions of records, enabling investigators to analyze data contextually and uncover hidden relationships using its powerful inference engine. A single suspicious entity could connect to seemingly unrelated networks through a chain of intermediaries, but identifying this required sophisticated graph traversal and pattern recognition to uncover hidden risk pathways, and this was beyond the scope of their existing tools.

A critical differentiator was DataWalk’s fuzzy matching engine, purpose-built for large-scale entity resolution. It accurately linked similar usernames and aliases—even with anagrams, symbols, or nickname variations—across billions of data points.
Instead of writing complex custom code, investigators could build modular, step-by-step queries without defining full paths in advance. This "brick-by-brick" method leveraged DataWalk’s optimized computation model to maintain low latency, even across massive graphs.
For example, using the entire >100-billion-element graph, investigators could instantly expand their view from a single suspicious account to reveal:
— all without hitting graph size or traversal limits. This end-to-end capability enabled the client to move beyond legacy constraints and expose entire criminal networks with unmatched speed and precision.


