Rogo

Today we're announcing a strategic investment in Rogo from Barclays, BNP Paribas, Citi Ventures, MUFG Innovation Partners, and Societe Generale — joining J.P. Morgan Growth Equity Partners and Truist Ventures among others.

That brings Rogo’s strategic investors to nine global banks across New York, London, Paris, and Tokyo, representing nearly $20T in combined assets.

Rogo now serves 50,000+ professionals across 350+ firms globally. Grateful to our clients, team, and investors for the conviction, and excited to keep building.

What if better context is the key to cheaper AI?

We set out to study exactly that.

At Rogo, we've built a proprietary context layer called Rogo Intelligence, which gives our agents structured memory on you and your firm's work.

We wanted to understand how our context layer impacts the economics of AI, particularly compared to agents relying on traditional connector-based searches across systems like email and CRMs.

Our initial results are promising: agents using Rogo Intelligence consumed 35% fewer tokens on complex tasks, with fewer steps and faster execution.

This is just the beginning of our exploration into the broader returns of institutional intelligence. Lower AI costs are one dimension, but the much larger opportunity is turning decades of accumulated intellectual capital into an asset that compounds for our customers.

Today, we're introducing Rogo Notes, the AI notetaker purpose-built for finance.

Conversations are among the most valuable sources of firm knowledge, yet capturing and preserving meeting insight has long depended on manual note-taking and individual recollection.

With Rogo Notes, agents handle meeting prep and follow-ups, teams collaborate from shared context, and every conversation deepens your firm’s understanding of the people, companies, and projects it works with.

Rogo Notes was built to meet the regulatory standards of financial institutions, so firms never miss a valuable meeting insight again.

We built Big Finance Bench to help define what good looks like for AI in finance. Now, we’re extending that evaluation framework to financial data.

We’re focused on two questions: how can LLMs use individual financial data sources more effectively, and how should multiple sources work together to surface the right data for each task?

Early results from these optimizations show 75% fewer serious hallucinations, 40% lower latency, and a 4x reduction in token consumption.

Rogo’s proprietary financial benchmark, built around the real work financial professionals deliver, is becoming the industry standard for evaluating AI in finance.

Rogo’s Big Finance Bench (BFB) is on the Specialized Intelligence Index from @FireworksAI_HQ, bringing a rigorous measure of financial intelligence to model comparisons across the industry.

Explore the results: fireworks.ai/specialized-in…