Capital Flow Analysis

Use case: A blockchain analytics team wants to perform a capital flow analysis to understand how money moves through a particular DeFi ecosystem. For example, after a major event (like a token launch or a hack), they want to trace funds from the source through various paths and see where they end up.

What this means: Capital flow analysis is essentially “follow the money.” It involves picking a starting point (one or multiple addresses or a smart contract) and then tracking subsequent transactions to map out where those funds go. This often results in a flow diagram or graph of addresses with arrows showing transfers.

How DeepBlock helps:

• Graph queries for multi-hop tracing:

Using DeepBlock’s knowledge graph, the team can issue a query like: “from the address of interest, get all outgoing transfers above X amount, then for each of those destination addresses, get their outgoing transfers, and so on, up to N hops.” In GraphQL, this can be done with nested queries or by calling a pre-built flow analysis endpoint if available. The ability to do this recursively in one unified system is a game changer. Without DeepBlock, one might manually query a block explorer for each hop and paste things into a spreadsheet – very slow and error-prone.

• Filtering and focusing

The knowledge graph can filter out noise. For example, perhaps a particular address has thousands of micro-transactions (maybe a dust airdrop); these can be filtered out to focus on large, meaningful flows. Or maybe you only care about flows that eventually reach a certain type of address (like a centralized exchange). DeepBlock can incorporate those conditions directly in the query (e.g., stop tracing when you hit an exchange, or only follow paths that involve a certain token).

• Visualization

While DeepBlock provides data, it’s straightforward to pipe the results into a visualization tool. Many partners use the output (which could be in JSON) to plot flow diagrams. In fact, because the data is already graph-structured, integration with graph visualization libraries (like GraphViz or Gephi) is natural. The team might generate a diagram showing, say, funds moving from a hacked contract to intermediate wallets, then splitting into multiple paths, some going to exchanges, some to mixers. DeepBlock provides the coordinates for these diagrams – addresses and transactions and timestamps – all linked.

• AI-assisted summary

If the team wants a human-readable summary, they can leverage the RAG pipeline. After pulling the raw flow data, they could ask an AI agent (powered by DeepBlock) questions like: “Summarize how funds flowed from the exploit.” The AI might answer: “Approximately 80% of the stolen funds went through Tornado Cash in batches of 100 ETH, while the remaining 20% were sent to three exchanges (Binance, Kraken, Coinbase) in varying amounts. After mixing, a portion resurfaced on Chain B via Bridge XYZ.” This saves time in interpretation.

Outcome: With DeepBlock, a task that could take days of manual blockchain sleuthing can be done in a matter of minutes. The analytics team can produce a detailed report of the capital flow, complete with visuals and data-backed commentary. This can be used to inform law enforcement (in case of hacks), investors (to see how big players are moving money), or internal strategy (understanding user behavior patterns).