BridgePort, the middleware coordination layer for off-exchange settlement (OES) in institutional crypto, has launched BridgePort Analytics, an exchange intelligence platform designed to support institutional trading firms operating in OES environments. The platform includes Bridget, an AI-powered assistant that allows users to query execution and venue data using natural language.
According to the company, BridgePort Analytics is intended to provide objective insight into how diverse crypto exchanges perform across a range of metrics. The launch extends BridgePort’s existing role as a middleware provider connecting execution venues, custodians, and settlement workflows in institutional crypto markets.BridgePort CEO Nirup Ramalingam confirms to TradingTech Insight that the new analytics capability is intended to sit alongside the firm’s existing OES infrastructure rather than replace it. “This is a deliberate move, although it is not a departure from our middleware layer for Off-Exchange Settlement. We deliberately designed BridgePort Analytics to complement the middleware in several ways, providing the market intelligence tools to enable traders to understand where to most effectively allocate their capital.”
Capital Optimisation
Off-exchange settlement has been adopted by institutional trading firms as a way to reduce operational and counterparty exposure associated with holding assets directly on crypto exchanges. Ramalingam notes that this shift has highlighted the capital utilisation challenges inherent in traditional exchange-based trading models. “Pre-funding on multiple exchanges results in dead capital when assets are parked but not traded,” he says. “Traders are always looking to optimise their capital for return on investment.”
Under OES models, assets remain at a custodian and are allocated dynamically to support trading activity across multiple venues, and firms are no longer constrained by where assets are physically held. Instead, execution decisions increasingly depend on assessing how venues perform under specific market conditions. According to Ramalingam, this has led firms to seek more granular information on execution outcomes. “Investors and traders frequently asked us to quantify the benefits of OES. Specifically, if trading firms hold assets at a central custodian and allocate them in real time rather than pre-funding, how does that impact their P&L and exchange liquidity?”
The newly launched BridgePort Analytics aggregates market data from multiple sources to provide a consolidated view of venue behaviour, focusing on execution outcomes rather than headline metrics such as volume or market share.
“We have trained our model to synthesize execution data into an easily consumable format, essentially creating a map of liquidity that highlights opportunities within market microstructure, such as slippage, spreads, bids, asks, and liquidity fragmentation,” explains Ramalingam.
The company positions the platform as a way for trading firms to compare execution conditions across venues using a consistent analytical framework, particularly in markets where liquidity is distributed across a large number of exchanges.
Execution Intelligence
As part of its roadmap, BridgePort plans to introduce a new metric, the ‘BridgePort Opportunity Index,’ to express the relative efficiency of deploying capital across different venues.
“Ultimately, we aim to create the BridgePort Opportunity Index to provide the opportunity cost of allocating credit to one exchange over another. Our goal is to maximise the impact of every dollar for which they extend credit,” says Ramalingam.
Such comparative measures are already well established in other electronic markets, including FX, where execution quality metrics are used to inform routing and venue selection decisions.
While BridgePort Analytics is positioned today as a decision-support tool, Ramalingam indicates that execution intelligence is likely to play a more direct role in automated trading workflows as firms integrate analytics more closely into their systems. “As the analytics product can be updated in real time, I expect it to become increasingly useful for algorithmic decisions regarding the best exchange for order routing.”
He compares this progression to earlier developments in other asset classes, where improved data availability contributed to greater automation: “We aim to bridge the gap between raw data and actionable insights. I believe the industry will head in this direction as soon as they access this data, similar to how better data catalysed the shift in FX from manual to fully electronified markets.”
The launch of BridgePort Analytics reflects a broader trend in institutional crypto markets, where execution, settlement, and risk management are becoming more closely linked. As OES models reduce the need to pre-position assets on individual venues, execution quality and venue behaviour become more prominent factors in trading decisions.
BridgePort’s approach places execution intelligence alongside its existing coordination infrastructure, with the stated aim of supporting how firms assess and manage trading activity across fragmented markets.
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