Startup profile for Toma: latest funding, latest known valuation, total disclosed funding, investors, status, sources, and why the company is interesting. Part of the Venture Capital Tracker startup directory.

Startup profile · funding coverage

Toma funding, valuation and investors

AI voice agents for auto dealerships — scheduling, parts orders, and sales calls.

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Funding, valuation & investors

Answer-first snapshot

Latest funding

Series A

$17M · June 2025

Latest known valuation

Not publicly disclosed

Total disclosed equity funding

$17M

Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.

Current status

private

series a

Investors in latest funding

Lead: Andreessen Horowitz

Sources: latest funding Last verified: 2026-07-25

Overview

Toma builds AI voice agents that handle inbound phone calls for auto dealerships — service appointment scheduling, parts orders, sales inquiries, and status updates. In June 2025, Andreessen Horowitz led a $17M Series A with Y Combinator and Flex Capital participating. Toma reports deployment at more than 100 dealerships processing real repair-order and parts workflows, not demo lines. The company targets a industry where missed calls directly convert to lost service revenue and poor customer satisfaction scores.

Why Toma is interesting

Dealerships miss thousands of inbound calls — a16z-led $17M Series A puts agents live at 100+ stores handling real RO workflows.

Product & use cases

Toma voice agents integrate with dealership DMS systems to answer calls 24/7, book service appointments, quote parts, and route complex issues to human staff with context.

  • Service departments capturing after-hours appointment bookings
  • Parts counters automating availability and order calls
  • Sales lines qualifying leads before human handoff

Key facts

  • Series A (Jun 2025): $17M led by a16z; Y Combinator, Flex Capital participated (TechCrunch)
  • Live at 100+ dealerships with real RO and parts workflows
  • AI voice agents for service scheduling and sales calls
  • Automotive vertical focus with DMS integrations

Funding history (newest first)

Investors in our directory

Funds linked from Toma's profile — open a fund page for stage focus and related deal articles.

Competitive landscape

Edge: Deep dealership DMS integrations and automotive-specific call flows — vertical focus beats generic voice AI platforms.

Dealership software sales are relationship-heavy. Toma wins on measurable recovered appointments; must navigate DMS vendor partnerships and dealer group rollouts.

  • PolyAI adjacent

    Enterprise voice assistants; less auto-DMS native.

  • Receptionist BPO services incumbent

    Human answering services; higher cost, inconsistent quality.

  • CDK / Reynolds native tools incumbent

    DMS vendors adding basic automation slowly.

Notable stories

  • Toma's wedge is the call nobody picks up at 6pm in service — a16z Series A funded expansion after 100 dealerships proved agents book real appointments, not just FAQ (TechCrunch, 2025).

Industries

AI & Machine Learning Enterprise SaaS

Related funding articles

Venture Capital Tracker pieces that cover Toma's financing or category context.

FAQs about Toma

Practical answers founders, operators, and investors typically search for.

Toma provides AI voice agents that answer dealership phone calls for service scheduling, parts, and sales.
Andreessen Horowitz led a $17M Series A in June 2025. See /fund/andreessen-horowitz.
100+ dealerships cited in 2025 Series A coverage with live deployments.
Toma integrates with dealership DMS and automotive call workflows; horizontal voice bots lack auto-specific training.
June 2025 — $17M led by a16z.
Toma handles routine calls and booking; complex issues route to staff with captured context.
Not publicly disclosed.
Integrates with major dealership systems — verify current list on toma.com.

By Venture Capital Tracker

Last updated:

Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.