Avoma builds an AI-first platform that captures meeting intelligence and feeds it into CRM systems for sales and customer success teams. The tech stack reveals a product-focused org: React + Redux on frontend, Django + Python on backend, GPT + Gemini + TensorFlow for NLP and scoring, and tight integrations with HubSpot and Salesforce. Active projects span real-time insights, a collaborative notes editor, and an insights engine overhaul—suggesting the company is moving beyond meeting recording into actionable deal and customer health signals. Engineering-heavy hiring and stated pain around scaling and user adoption indicate they're building toward higher-volume, multi-team deployments.
Avoma is an AI-powered platform for customer-facing teams at startups and scaleups, headquartered in Palo Alto and founded in 2017. The product automatically records and transcribes meetings, generates AI notes, and surfaces actionable insights about customer interactions and deal health directly in Salesforce and HubSpot. It combines three capabilities: meeting assistance (scheduling, recording, transcription), conversation intelligence (call scoring, customer data capture), and revenue intelligence (deal health scoring, win/loss analysis, pipeline forecasting). The company operates at 51–200 employees and is actively hiring across engineering, support, and marketing, with a seniority distribution skewed toward senior and mid-level individual contributors.
Frontend: React, Redux, Tailwind CSS. Backend: Django, Python, Flask. AI/ML: GPT, Gemini, TensorFlow, PyTorch. CRM/analytics: HubSpot, Salesforce, Mixpanel, Amplitude. Design tools: Figma, Sketch, Adobe suite. Deployment: AWS.
Key projects include a real-time insights engine, a collaborative notes editor, core data processing improvements, third-party CRM integrations, and playbooks for customer success teams. They are also scaling their analytics infrastructure (Amplitude) internally.
Avoma's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.