Speak AI — Transcribe, Analyze & Deploy AI Agents

What

Speak AI is a modular platform for capturing, transcribing, analyzing, and sharing voice and video content, with an added layer for deploying AI agents grounded in a knowledge base. It serves teams that work with meetings, interviews, surveys, customer conversations, and other spoken content, including business, research, education, and enterprise use cases.

The core workflow starts with recording or importing audio and video, generating transcripts, analyzing themes or structured fields, and organizing outputs into searchable libraries or shareable assets. Speak appears positioned as both a self-serve voice analytics platform and a more configurable AI agent deployment option for teams that need white-label delivery, structured workflows, or higher-trust operational use cases.

Features

• AI meeting assistant: Automatically joins scheduled calls on Zoom, Microsoft Teams, Google Meet, and Webex to record, transcribe, summarize, and capture key takeaways.
• Automated transcription and translation: Transcribes uploaded or live audio/video with speaker labels, timestamps, editing support, and translation across 100+ languages.
• Knowledge-base-grounded AI agents: Builds voice, phone, and likely future video agents on top of documents, recordings, SOPs, and tagged conversation data to keep answers more consistent.
• Structured outputs and data collection: Extracts defined fields such as tags, attributes, scores, and summaries from conversations, and can actively ask for missing details during interactions.
• Embeddable capture and sharing tools: Provides iframe-based recorders, shareable widgets, and media libraries for collecting submissions and distributing transcripts, highlights, and evidence.
• AI chat and visualization: Lets users query transcripts and datasets across multiple files and create charts or dashboards for themes, sentiment, and trend reporting.

Helpful Tips

• Separate capture from extraction requirements: If your process needs guaranteed fields, use active data collection; if you mainly need post-conversation tagging or reporting, structured outputs may be enough.
• Organize the knowledge base carefully: Folders, intent tags, and escalation rules are likely important for keeping agent answers precise, especially across multiple teams or use cases.
• Start with a narrow workflow: Products in this category are often easiest to validate first in one use case such as meeting notes, intake, support triage, or research analysis before broader rollout.
• Check deployment maturity by channel: The site clearly supports voice and phone agents, while video agent mode is described as coming soon, so buyers should confirm current production readiness for their channel.
• Plan governance for client-facing use: White-label embeds and shareable libraries can be useful, but teams should define review, routing, and handoff processes before scaling external workflows.