Sixfold's 100 Billion Token Milestone

Sixfold was featured on the big screen at Open AI’s DevDay 2025, recognized for surpassing 100 billion tokens processed. To make it even better, Senior AI Engineer, Drew Das, was honored among global developers for his contributions.

This is a huge milestone for Sixfold, a reflection of the real impact our AI is making in underwriting. But what does 100 billion tokens actually mean in practice? We sat down with Drew to find out.

Can you start by explaining what exactly OpenAI recognized Sixfold for at Dev Day, and what it felt like to see your name up on the screen?

Seeing my name on stage felt like public validation of the engineering team’s hard work. Sixfold is operating at the frontier of large-scale AI in insurance underwriting. We’re not a pilot anymore; we’re in production.

It also hit home the responsibility that comes with it. Yes, we’ve built scale, but now we have to make sure what we produce is high quality and meaningfully used. Sixfold isn’t experimenting with LLMs; we’re running them reliably where accuracy, latency, and auditability really matter.

We’re not a pilot anymore; we’re in production.

What does “100 billion tokens” actually represent in Sixfold’s day-to-day work?

Each token is a fragment of understanding, a piece of text, data, or context that our models interpret and turn into something underwriters can act on.

It signals the volume of underwriting data, documents, broker submissions, and risk information that our platform analyzes and processes through generative AI workflows.

Hitting 100 billion means we’ve moved many workflows off manual underwriting review and into AI. When repetitive work is reduced, underwriters spend more time on relationships, strategy, and higher-value decisions. 

Hitting 100 billion means we’ve moved many workflows off manual underwriting review and into AI. When repetitive work is reduced, underwriters spend more time on relationships, strategy, and higher-value decisions. 

So… what’s next, another 100 billion tokens?

Token count is becoming table stakes. What matters now is how those tokens are applied and the outcomes they create. 

We’re focusing on deeper workflow integration, bringing AI agents that go beyond risk assessment to support decisions, automate workflows, and deliver real-time underwriting intelligence. Our Research Agent is a great example of that.

Considering the high-stakes nature of insurance, every risk insight and decision generated by Sixfold must be auditable and traceable. Our solution isn’t just built for scale, but for governance.

On the engineering side, what does it take to run something at that scale?

From an engineering perspective, scale brings complexity. We’re constantly balancing latency, cost, and accuracy.

Many AI projects stop at the “cool demo” stage, but we’re pushing through the messy engineering: hybrid search, re-ranking, prompt tuning, and evaluation,  all happening under the hood.

Scaling means better signals, more edge cases surfaced, and faster model learning. It’s a flywheel that keeps getting smarter.

Scaling means better signals, more edge cases surfaced, and faster model learning. It’s a flywheel that keeps getting smarter.