Finley: Reimagining The Advice CRM
Most CRMs are built around maintaining records.
Finley takes a different view.
Finley’s intelligent workspace places AI at the centre of the client relationship workflow.
From Create, Read, Update and Delete - To Context
Traditional CRM systems are fundamentally CRUD applications: create, read, update, delete.
That structure is useful for maintaining data, but it does not reflect how advisers actually work. Advisers are not thinking, “I need to update row 14 in the assets table.” They are thinking:
“Now I need to write my meeting file note?”
“By the time i write this paraplanning request, the SOA is already half done”
“I don’t have time to sit here entering the fact find into the CRM?”
Finley shifts the centre of gravity from data entry to context. The app still stores structured data, but the user experience is organised around the work an adviser is trying to complete.
That means the CRM becomes less about navigating modules and more about progressing client outcomes.
Finley’s SOA Workspace
Using Finley’s SOA Workspace, you upload all your supporting documentation and Finley will dynamically building your SOA.
Finley’s SOA workspace brings a unique balance between AI efficiency, with human oversight. Let Finley will do all the heavy lifting by structuring your SOA, based of your unstructured documentation.
FAQ’s
What AI Model is used?
Finley currently uses GPT 4.1.
What personal information is shared with OpenAI?
Finley has deployed it’s own safe AI in our own Microsoft Azure environment. This means that the information is not shared with OpenAI and the large ChatGPT model.
Prompts (inputs) and completions (outputs):
are NOT available to other customers.
are NOT available to OpenAI.
are NOT used to improve OpenAI models.
are NOT used to improve any Microsoft or 3rd party products or services.
are NOT used for automatically improving Azure OpenAI models.
The models are stateless: no prompts or generations are stored in the model. Additionally, prompts and generations are not used to train, retrain, or improve the base models

