For the back of the room: • production-grade reranking for sales data with Jev • 20x faster • 10x cheaper • 12% more accurate Are you getting it yet?

We used Jev to retrieve sales data 20x faster, 10x cheaper, and 12% more accurate than GPT-5 Mini. Every time a Rox agent answers a query, it pulls from relevant transcripts, emails, CRM notes, news, and documents. We benchmarked two ways of retrieving them: LLM-based reranking Jev classification The results speak for themselves. Jev was faster, cheaper, and more accurate. Revenue agents require thousands of points of context to understand accounts, chart relationships, and execute on sales. This research will help us serve that context with frontier-level performance for our customers.

Rox has been powering Global 2000 enterprises with $5T+ in combined market cap. Today, we’re putting it in everyone’s hands. Over the last 2 years, companies like @MongoDB, @togethercompute, and @Xbow shifted investment from legacy CRM and SaaS to Revenue Agents. But most teams have been locked out. They lacked in-house technical talent or FDEs required to set up the revenue-specific context, harnesses, and agent systems needed to run Revenue Agents in production. Today, we launched Rox Teams to remove that barrier. With Rox Teams, businesses of every shape, size, and vertical can activate Revenue Agents on their own. Set up in minutes. No FDEs required. rox.com/teams & Nolaned this hilarious explainer video 👇

We spent 11x more tokens on reasoning and got 0% better answers on revenue data ... until we added a knowledge graph. TLDR: our latest research shows that improving data representation increases agent retrieval accuracy more than upgrading the model. We ran 3,100 runs across 8 models answering revenue questions, like deal amounts, contacts, and identifying customer champions. We compared two ways of storing the data: 1. a normal database (SQL) VS. 2. a knowledge graph (relationships pre-mapped) Frontier models hit 8.9% accuracy on the questions using SQL over a relational schema. Cranking Claude Opus 4.8’s reasoning effort from minimum → maximum accuracy did not help. However, swap raw Salesforce data for a knowledge graph built on lakehouses like @databricks, @Snowflake, @googlecloud's Big Query, or @Azure Data Fabric ... and accuracy jumps from 8.9% to 99.9% - even using a 27B open-weight model at 1/20th the cost. This research shows throwing more compute at your agent cannot fix bad data structure. And is proof a revenue-specific knowledge graph is key to making revenue agents work at scale.

@AnthropicAI and @OpenAI want your product living inside Claude or ChatGPT. @Google wants the model to redraw your UI from scratch. Both are wrong about who should own the pixels, since it diminishes beautiful product experiences to basic text. We measured a 3rd option: up to 100x cheaper, and your product stays whole. → Introducing Rox Tether: An alternative to MCP Apps, A2UI, and ChatGPT Apps. TLDR: the product manages its own pixels, and the agent operates it via reference. The results: - Up to 100x token reduction - No new protocol - No browser API needed - No cooperation from the chat host Breakdown below:

Today, we're releasing our open-weight, auto-routing model @daridotdev, built for coding agents. We're state-of-the-art on the Pareto Frontier, w/ 70% cost reduction + comparable coding performance to Fable. Bring your own evals, choose your models, or use our defaults.

Introducing ask-web: Rox’s in-house web search agent. ask-web sits on the cost-per-accuracy pareto frontier of the hyper-parameter grid when compared to frontier labs and commercial search agent providers. The agent delivers 91.3% accuracy at 1.03 cents per query on real production prompts. It has been running in production for more than 6 months with continuous evals. Inference partners: @togethercompute, @baseten, @modal Commercial Search vendors benchmarked: @perplexity_ai, @ExaAILabs, @p0. Frontier Search vendors benchmarked: @OpenAI, @AnthropicAI Exa, OpenAI and Anthropic excel on accuracy. Parallel and Perplexity are cost-efficient. Here’s the breakdown:

Agents now Run Revenue, End-to-End. Rox Autopilot is here 🚀 The world’s largest enterprises now run their critical revenue systems on Autopilot. Coding and support agents had their moment. Now it’s time for Revenue Agents No SaaS. No enrichment. No CRM. Just an Agent. We’re offering 2× free agent actions for the next 30 days. Start Autopilot at rox.com Thank you to @sequoia, @generalcatalyst, @googleventures, @marcbhargava, @vedantsuri, @davemuni, @MongoDB, @cloudsoftware, @togethercompute, @Microsoft, @databricks, and all of our other customers & partners!

6 months, 25 million revenue agents & 3 trillion tokens later... Rox is now globally available 🌎 Just as coding agents 10x’d engineering, revenue agents 10x customer work. With Rox, humans are evolving to orchestrators while agents manage the end-to-end customer lifecycle. Even in Beta, Rox powered Global 2000 leaders in banking, hardware, construction, and sovereign AI, while serving dominant AI winners like @tryramp and @cognition. Rox delivers ROI in 90 days and is built with the best. Thank you to @OpenAI, @nvidia, @perplexity_ai, @awscloud, @vercel, @Snowflake, and @stripe for helping us scale.

We built a B2B SaaS sales company and here’s what it taught us about B2B SaaS sales 🧵👇 (but actually) Today we’re launching  Rox, the first publicly available AI agent swarm for the top sales teams, and in the private beta it already helped reps grow their books 30%. 2025 is going to be a huge year for growth. Enterprises are doubling next year’s revenue goals, but no one is doubling team sizes. Every rep will have to bring in more, and AI can help them do that. But there’s a wrong way and a right way. Much of today’s AI aims to replace low-value work. But sales follows a power law: 90% of revenue comes from the top 15% of enterprise sales reps. The greatest gains will come from supercharging the highest value work — raising the ceiling, not the floor. Rox equips the very best with a swarm of AI agents, acting as an army of analysts to help them plan, prioritize, research, engage, and keep up with their customers. Over 35 of the best-performing enterprise sales teams have adopted Rox virally. For example, Ramp has rolled it out to their AE and AM teams, and we are now integrating their internal data systems with Rox. The Enterprise AE team alone gains 225+ hours per week to boost pipeline execution activities. Rox is now in public beta. No barriers. No need to request a demo. Try it now for free → rox.com