Finance ops · Production

UNETI AI Labs automates up to 7 hours per employee weekly with Davis and Nance AI agents on Google Cloud

The problem

Startups in UNETI's portfolio know what they want to build but struggle to execute quickly due to lack of time or resources, requiring operational support beyond financial investment alone.

Workflow diagram · grounded in source

Daily workflow triggers agents

trigger

“we use them every day internally to manage our own operations”

Davis analyzes investment opportunities

ai_action

“He quickly summarizes information, contextualizes a pitch, analyzes a market, and more”

Nance automates financial operations

ai_action

“Nance, for her part, automates financial and administrative operations, from bookkeeping to reporting. She can also manage rules specific to certain sectors, such as in healthcare where actors must manage specific workflows before obtain…”

Agents deliver outputs for critical processes

output

“capable of supporting critical processes, whether it's opportunity analysis, understanding documents already in our Drive, or generating operational summaries”

Reported outcome

Davis and Nance AI agents are already saving each employee nearly seven hours per week on average, with a goal to reach thirty-two hours weekly for finance and dealflow colleagues, while delivering more regular analyses, more accurate reports, and more homogeneous processes.

Reported metrics

Hours of work automated per employee per weeknearly seven hours per week on average

Target weekly hours automated for finance and dealflow colleaguesthirty-two hours weekly

Execution quality on critical processesEnhanced execution quality on critical processes

Operational loadReduced operational load

Reported stack

GeminiVertex AIAgentSpaceCloud RunCloud SchedulerAgent BuilderDrive

Source

https://cloud.google.com/customers/uneti-ai-labs

Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Davis and Nance AI agents are already saving each employee nearly seven hours per week on average, with a goal to reach thirty-two hours weekly for finance and dealflow colleagues, while delivering more regular analys…

What tools did this team use?

Gemini, Vertex AI, AgentSpace, Cloud Run, Cloud Scheduler, Agent Builder, Drive.

What results were reported?

Hours of work automated per employee per week: nearly seven hours per week on average; Target weekly hours automated for finance and dealflow colleagues: thirty-two hours weekly; Execution quality on critical processes: Enhanced execution quality on critical processes; Operational load: Reduced operational load (source-reported, not independently verified).

How is this finance ops AI workflow structured?

Daily workflow triggers agents → Davis analyzes investment opportunities → Nance automates financial operations → Agents deliver outputs for critical processes.