Letter AI Raises $40M for AI-Native Sales Agents

Why Letter AI’s AI-Native Approach Beats Legacy Sales Enablement

Letter AI co-founders Ali Akhtar (CEO) and Armen Forget (CTO) joined YC Root Access to announce their $40 million Series B, led by Battery Ventures. Just two and a half years after Y Combinator — where they pivoted from a completely different company — they’ve built an AI-native revenue enablement platform serving Fortune 100 customers like Lenovo, Adobe, and Novo Nordisk alongside fast-growing startups like Plaid and Kong.

The light-bulb moment came from pain: “There’s this really expensive tool, which I couldn’t even get a license to because it was so expensive, that’s getting very low adoption.” Ali Akhtar experienced this firsthand at Samsara, where as Director of Engineering for ML, sellers would constantly ping him for product knowledge instead of using legacy enablement tools. The insight: legacy tools require armies of humans to curate content and still get less than 50% adoption. AI can do this automatically — and sellers will actually use it.

The pivot tells the real story. Letter AI started as Tractatus, a developer tools company for generative AI. During the YC batch, they discovered the space was saturated and the product wasn’t sticky — developers would prototype on the platform, then go build it themselves. The pivot to AI-native sales enablement, combined with closing Lenovo during the batch, proved the market pull was real.

Near 100% adoption is the killer metric. “If Letter disappeared, there would be a line of folks outside the door of the enablement team banging and asking for it to come back.” Legacy enablement tools struggle to hit 50% adoption. Letter AI achieves close to 100% because it’s not a content repository — it’s a personalized AI companion that surfaces deal-specific intelligence in real time.

How Letter Compass Redefines Deal-Level Enablement

The most forward-looking announcement is Letter Compass, a product that automatically personalizes enablement content to each seller’s specific book of business. Instead of generic product training, sellers get insights tailored to the deals they’re pursuing today, powered by conversational intelligence and CRM data.

The enterprise speed proof point is striking: A Fortune 100 company acquired another company, announced it internally on a Friday, and by Monday had a full certification program for hundreds of new sellers. “Pre-Letter, an exercise like this would have taken them at least a month and tons of folks to get done. With Letter, they were able to do it over a weekend with just about two or three folks online.”

On the technology front, Letter AI is building MCP servers and an agent-to-agent protocol that enables their customers’ AI systems to communicate with Letter’s for distributed thinking. Armen Forget notes that their customers are increasingly AI-centric: “Their salespeople now are sometimes in Cursor and they’re doing their own research and now they can use a Letter AI MCP server to get the content or get answers to the questions they need.”

5 Takeaways from Letter AI’s AI-Native Enablement Playbook

• AI-native beats AI-bolted - Building from scratch with AI as the foundation produces fundamentally different adoption rates than adding AI to legacy tools
• Speed is the new moat - The ability to onboard hundreds of sellers in a weekend versus a month is a 10x improvement that justifies the platform
• Personalization drives adoption - Generic training gets ignored; deal-specific intelligence becomes essential to daily workflow
• MCP and agent-to-agent protocols are going enterprise - Letter AI building MCP servers signals these protocols are crossing from developer tools into enterprise SaaS
• “Never sell alone” is the vision - Letter AI aims to be the single most important tool in every seller’s daily workflow, with AI agents as constant companions

What Letter AI Means for the Future of Enterprise AI Agents

Letter AI’s trajectory validates a pattern we’re seeing across enterprise software: AI-native tools that are built around agents — not just augmented with AI features — achieve fundamentally different adoption and value. When your AI actually knows the specific deals a seller is working, the specific content that’s relevant, and can practice with you before high-stakes conversations, it stops being a nice-to-have and becomes the essential co-worker. The “never sell alone” vision is really a vision for how all knowledge workers will operate: with AI agents as deeply integrated partners, not bolted-on assistants.