A structured discovery
A transaction management team at a financial services firm was responsible for creating and managing contracts for trading activity. On the surface, the team functioned well, but underneath, processes were manual, undocumented, and deeply reliant on the institutional knowledge of a long-tenured team. Leadership recognised this created real exposure: not just operational inefficiency, but concentrated risk. They wanted to know whether the team’s workload could be restructured or redistributed to higher-value activities.
Zenitech began with a structured discovery engagement, using Business Process Model and Notation (BPMN) to map how work actually moved through the transaction management function, focused on the trade type representing the bulk of daily volume. The exercise identified roughly 30 manual tasks as candidates for automation, and surfaced a deeper issue: the team’s document management relied entirely on a Windows file system that had accumulated hundreds of gigabytes of files over 25 years, with no meaningful search capability and error-prone manual version control.
Rather than stopping at a written recommendation, Zenitech built a series of AI-powered proofs of concepts, automated contract generation from key fact sheets, semantic search across the document archive, and a modernised workflow interface; to give the client something tangible to react to. The client engaged seriously with the proposals, prioritised document search and organisation, and moved into a scoped implementation project.
The engagement gave the client something it lacked before: a documented, auditable understanding of how the team actually worked, where the risk sat, and a realistic view of what automation could address.
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