For sales and customer success teams, Avoma is an AI meeting assistant that records and transcribes calls, then uses AI to score deal health, coach reps, and forecast pipeline from the conversation data.

Presence & Market Position

17
SOV Score

#90 of 115
Traditional SaaS Going Headless

Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →

Avoma is positioning itself as the conversation-intelligence layer that turns calls into coaching, deal health, and forecast signals for revenue teams. Practitioner workflows place it alongside enrichment, outreach, and CRM systems, with current interest in making customer-call knowledge searchable inside agent workflows. The company’s recent product direction adds AI email templates, MCP connection, and alerts that identify deal and churn risks at the moment they appear in a conversation. That gives Avoma a clear role in carrying revenue context from calls into the systems teams use to act on it.

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Business Profile

Avoma was founded in 2017 in Palo Alto by Aditya Kothadiya, Devendra Laulkar, and Albert Lai, building an AI meeting assistant that has since broadened into what the company calls a Growth Acceleration Platform, bundling meeting capture, scheduling, conversation intelligence, and revenue intelligence into one system. Having raised $16M to date, including a Headline-led Series A, the company has grown to roughly 59 employees and reports $10.9M in revenue as of 2026, remaining independent.

Founded
2017
Employees
59
Funding
$16M
ARR
$10.9M (2026)
Status
Independent
Scale stage
Growth
GTM Categories
SaaS for GTMSelling

Agent Readiness

76A
Agent Readiness
APIMCPSDKCLI

Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →

Avoma's agent build-out runs through Ask Avoma, an AI query layer over meeting and deal data that added org-level prompts and web-search reach beyond internal data, plus automated MEDDICC-based deal scoring and coaching. That data is also exposed through an MCP server that practitioners have connected alongside other tools to pull call data into their own AI workflows — a two-click setup as of the most recent product update. Surfaces on file span API and MCP.

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