Finance ops · Production

How Rogo evaluates frontier models for institutional finance

The problem

Finance requires highly bespoke tools, and general AI models often miss context-specific nuances—which ratios matter most, which peers are appropriate, how information should be presented—making the gap between 'looks right' and 'is right' enormous.

Workflow diagram · grounded in source
1
User submits inputs
trigger
“A user would typically start with some guidance and raw inputs: financials, documents, a banker brief, or other data room materials”
2
Research and data extraction
ai_action
“research is conducted within the platform, and financial data is pulled and substantiated directly from the inputs”
3
Supporting materials built
ai_action
“Supporting materials and backups are built alongside the core narrative”
4
Deck or model generated
output
“Rogo shells out the deck and starts populating it with the appropriate inputs. The output of this work is almost always a deck or a financial model, and we needed a system that could produce structured PowerPoint and Excel output at inst…”
5
Dynamic model routing
routing
“we dynamically route tasks to the systems that perform best for a given job”
Reported outcome

Claude Opus 4.7 and Sonnet 4.6 are integrated into Rogo's platform serving over 35,000 financial professionals running 50,000+ queries per day, with Claude showing strong improvements in PowerPoint generation on Rogo's internal finance benchmarks.

Reported metrics
Queries per day on platform50,000+
Financial professionals on platform35,000+
PowerPoint generation qualitystrong improvements
Reported stack
Claude Opus 4.7Sonnet 4.6FelixPowerPointExcel
Source
https://www.anthropic.com/customers/rogo
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Claude Opus 4.7 and Sonnet 4.6 are integrated into Rogo's platform serving over 35,000 financial professionals running 50,000+ queries per day, with Claude showing strong improvements in PowerPoint generation on Rogo'…

What tools did this team use?

Claude Opus 4.7, Sonnet 4.6, Felix, PowerPoint, Excel.

What results were reported?

Queries per day on platform: 50,000+; Financial professionals on platform: 35,000+; PowerPoint generation quality: strong improvements (source-reported, not independently verified).

How is this finance ops AI workflow structured?

User submits inputs → Research and data extraction → Supporting materials built → Deck or model generated → Dynamic model routing.

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