Avoca AI is Building the AI Workforce for the Services Industry, Raises Series B at a $1 Billion Valuation

Avoca AI, re-engineering the services industry with AI, led by Apurva Shrivastava, Tyson Chen, and the team, has raised Series B at a $1 billion valuation, bringing the total raise to more than $125 million across Seed, Series A, and Series B. The company is backed by Meritech, General Catalyst, Kleiner Perkins, Amplify Partners, Nexus Venture Partners, and Y Combinator.

The services economy is entering its software moment.

Even large local service businesses still run on fragmented tools and manual coordination, leading to missed calls, wasted ad spend, and lost jobs during peak demand.

Despite being one of the most cash-generative parts of the economy, it remains under-digitized. That gap is structural, and now starting to close.

What is actually different now

For the first time, contractors across America can run a concierge-grade revenue operation once reserved for large franchises with dedicated sales teams.

Every call is answered, even late-night and weekend inquiries. Every lead is followed up on within seconds. Every customer interaction is timed across the lifecycle. Every marketing dollar is tracked end-to-end, from click to job to invoice.

What previously required multiple vendors and internal coordination is now unified in a single system that improves continuously with usage.

This is what is being built at Avoca.

Where AI value actually accrues

The core thesis is that durable AI value will concentrate at the application layer, where real work is executed. While frontier labs like OpenAI and Anthropic have made strong progress in data-rich domains such as coding, many workflow-heavy industries remain underdeveloped due to fragmented, tacit knowledge and limited structured data.

These are the environments where Avoca operates.

By sitting inside the contractor workflow, the system not only automates tasks but also learns what actually drives outcomes, what converts leads, what follow-ups matter, and which operational patterns perform in the field. Over time, this consolidates previously scattered data into a unified system-level view, creating a compounding advantage built directly from real usage.

The early signs

This is still the early phase, but the impact is already visible in what operators can do today versus a year ago.

Leads are being captured at all hours. More website traffic is converting into booked jobs through scheduling systems that understand the business context. Call center coaching is based on full conversation data instead of small samples. Marketing spend is now traceable end-to-end, from click to completed job.

The pattern is consistent: operators adopting AI inside their workflows are steadily outperforming those who do not, and the gap continues to widen.

Beyond home services

The same structural dynamics, fragmented tool stacks, labor-constrained operations, and customer experience as a key moat repeat across roofing, restoration, auto, and other large service verticals.

Each represents a multi-billion-dollar market that requires an AI-native platform built for the specifics of the work, rather than a generic horizontal tool retrofitted for it. The path forward is vertical by vertical.