Read AI

Meetings create useful information, but that information is often difficult to capture and even harder to find later. A customer makes an important request. A manager assigns a task. A team agrees on a deadline. Someone mentions a problem that matters three weeks later. Without proper records, all of that context can disappear into a calendar history.

Read AI is designed to solve that problem by turning meetings and other workplace conversations into searchable information. It can generate meeting transcripts, summaries, action items, key questions, and other insights while also measuring aspects of meeting participation such as sentiment, engagement, talking speed, filler words, and talk-time distribution.

What makes Read AI more interesting today is its broader role. The platform is not limited to meeting recordings. Its Ask Read system can search across meetings and supported connected services, while newer AI actions can perform tasks such as updating CRM records, sending emails, and scheduling meetings.

That puts Read AI in a different category from a simple automatic note-taking application. It is increasingly trying to become a layer between workplace conversations and the work that follows them.

Read AI Starts With the Meeting, But Does Not Stop There

The basic Read AI workflow is straightforward. Connect the service to your calendar and supported meeting platform, allow the Read Assistant to join when appropriate, and the system can capture the conversation and generate a report.

Read currently supports Zoom, Microsoft Teams, and Google Meet for direct meeting capture. The company also provides desktop and mobile applications for Windows, macOS, Android, and iOS, with mobile capture available for in-person meetings and other conversations.

After a meeting, Read can create a report containing the transcript, summary, action items, key questions, topics, and other insights. Depending on the account and plan, users can also access audio and video playback and automatically highlighted moments from the conversation.

This is the familiar part of the AI meeting assistant market. Plenty of products can record a meeting and summarize it.

Read adds another layer through meeting analytics.

Its Meeting Tools can provide live metrics during supported meetings, including sentiment, engagement, overall Read scores, filler-word usage, speaking speed, and the distribution of talk time between participants.

Those measurements are intended to give participants more information about how the conversation is progressing. A user can see when they are speaking too quickly, relying heavily on filler words, or dominating the discussion. After meetings, Read’s personalized Meeting Coach can turn these signals into recommendations around clarity, inclusion, and communication style.

That makes Read AI unusual among meeting assistants because it does not only ask, “What happened?”

It also asks, “How did the meeting go?”

The Real Difference Is Read’s Cross-Platform Search

The most significant part of Read AI’s current product is arguably not the transcript. It is what happens when the transcript becomes part of a broader information system.

Ask Read is designed as an AI-powered search and discovery layer. It can search meeting reports and, where connected and supported, information from Gmail or Outlook, Google or Outlook calendars, Slack or Microsoft Teams, Google Drive or Microsoft OneDrive, Notion, Confluence, HubSpot, and Salesforce.

This matters because modern work is scattered across applications.

A customer might send an email before a meeting, discuss the issue during a Zoom call, mention the same requirement in Slack, and then have the resulting opportunity stored in Salesforce. Looking at the meeting transcript alone gives you only one piece of the story.

Read’s current direction is to connect those pieces.

The company describes this as a searchable knowledge base that brings together conversations and workplace information. The user can ask a question instead of manually opening each application and looking through individual records.

Imagine a salesperson preparing for a follow-up meeting. Instead of opening the previous meeting report, searching Gmail, checking Slack, and then looking at Salesforce, the user can ask Read to surface the relevant context.

The same approach can help project managers. A question about a missed deadline may require information from a meeting, an email thread, and a project-management system. Cross-platform search is useful because the answer may exist across several sources rather than in one document.

This is also where Read AI becomes more relevant to teams than to casual meeting-note users.

A simple note-taking tool helps you remember a meeting. A searchable work context layer can potentially help you recover information across many interactions.

Meeting Preparation Is Part of the Workflow

Read AI is also designed to help before a meeting starts.

For recurring meetings, the platform can provide a recap of previous conversations, including earlier action items and unresolved questions. The goal is to reduce the amount of time people spend manually reviewing old reports before the next conversation.

That sounds like a small feature, but it can become useful when meetings repeat every week.

Consider a customer success manager with a recurring account call. The previous discussion might include three tasks, one unresolved question, and a decision that needs an update. Having that context available before the next call can reduce the need to search manually through previous reports.

Read also uses connected calendar and workplace information to improve context. Its current homepage describes a workflow that moves from preparation into the meeting and then into follow-through, with information from calendars, email, previous meetings, and CRM systems contributing to the process.

That approach changes the expected role of an AI meeting assistant.

Instead of being a tool you open after a call, it becomes something that participates in the entire meeting lifecycle.

Before the call, it helps provide context.

During the call, it can capture the conversation and surface live information.

After the call, it creates a report and helps turn the conversation into searchable knowledge and follow-up work.

Read AI Can Turn Search Into an Action

Search is useful, but there is a point where finding information is not enough.

Read AI’s Ask Read Actions are designed to take the next step.

The company currently documents actions for CRM updates, email, calendar scheduling, and recurring scheduled updates. For example, a user can ask Read to identify next steps from a meeting and update a deal in HubSpot. Another workflow can generate a follow-up email, present the draft for approval, and send it after the user confirms.

This is an important distinction.

A standard AI search system gives you an answer.

An action-oriented assistant can give you an answer and then do something with it.

That could reduce the number of manual steps between conversation and execution. A meeting produces an agreed deadline. The assistant can help schedule the next meeting. A sales discussion produces new deal information. The assistant can update the relevant CRM record. A project meeting creates a follow-up requirement. An email can be drafted around the discussion.

Read is effectively trying to move from “meeting notes” to “meeting-driven workflow.”

The company has also introduced Ada, its AI assistant, which is positioned as a digital assistant that can handle tasks, scheduling, questions, and follow-up workflows. Read introduced Ada in early 2026 and later described its broader Digital Twin direction.

Some of these capabilities are still developing, so users should distinguish between features available in the core product today and newer agent-style functions being rolled out.

Live Meeting Analytics Give Read a Different Personality

A lot of meeting assistants focus on factual capture: who said what, what was decided, and what needs to happen next.

Read AI adds a behavioral layer.

Its live meeting tools can show metrics such as talk speed, filler words, talk-time distribution, sentiment, and engagement. Its post-meeting reports can highlight moments where participant reactions changed or where a particular statement appeared to drive engagement.

Read’s Meeting Coach then uses meeting data to provide personalized communication feedback. The official pricing page lists measurements involving talking speed, filler words, bias, and charisma.

This can be relevant for presentations, sales calls, interviews, management meetings, and client conversations where communication style matters.

It is also where Read becomes more opinionated than a basic transcription application.

A transcript can tell a manager that an employee spoke for 60 percent of a meeting. An analytics layer can put that information alongside engagement or conversation metrics. The goal is not merely to archive the discussion but to help users improve how future conversations are handled.

These signals should still be treated as indicators rather than objective measurements of a person’s complete communication ability. Metrics such as sentiment or engagement are generated from meeting data and should be interpreted in context rather than treated as unquestionable judgments.

Recording Is Not Limited to Scheduled Video Calls

Read’s support for different capture methods is another area worth considering.

For online meetings, direct integration currently covers Zoom, Microsoft Teams, and Google Meet. If a meeting happens on another service, Read says users can record it with the Windows or Mac desktop application or upload an MP4 or M4A file for processing.

This gives the product more flexibility than a system that only operates when its bot joins a calendar meeting.

Read also has mobile apps for Android and iOS. The company says the apps can capture in-person meetings, access meeting reports and transcripts, provide notifications, and use Search Copilot. The mobile app is free to use, and recorded meetings can run for up to four hours.

That makes Read practical for meetings that happen away from a desk.

A consultant could record an in-person client conversation. A researcher could capture an interview. A manager could use the mobile app for an off-site discussion and later review the generated report.

The platform therefore covers a wider set of conversations than the phrase “Zoom notetaker” suggests.

Integrations Put Read AI Into the Existing Stack

Read AI’s integrations are built around the idea that meeting information should reach the tools people already use.

Premium integrations include services such as Notion, Salesforce, HubSpot, Jira, Confluence, Zapier, and webhooks. The company also provides integrations for Slack and other workplace applications.

The Chrome extension adds another layer by bringing Read functionality into Gmail and Google Calendar while also providing ways to add Read to a meeting. Read’s native Zoom, Teams, and Google Meet apps can also provide live meeting dashboards inside those platforms.

For organizations already using multiple business systems, this integration approach can reduce the need to manually transfer information.

There is also a developer side.

Read AI currently provides both a REST API and an MCP server. The company says these capabilities can provide programmatic access to meeting data such as transcripts, summaries, and action items, while MCP can connect Read with AI clients such as ChatGPT, Claude, Codex, and Microsoft Copilot Studio. Both the API and MCP server are currently described as open beta.

The MCP capability is especially relevant to people building AI workflows. A connected AI assistant can retrieve meetings, inspect transcripts, work with meeting details, and use Read data as context. Read’s September 2026 documentation describes workflows for finding meetings, analyzing conversations across meetings, retrieving action items, and deploying the Read agent to supported calls.

That is a much more technical use case than ordinary meeting transcription, but it shows where the platform is heading.

Read AI Pricing Is Simple at the Top Level

The pricing structure currently has four levels: Free, Pro, Enterprise, and Enterprise+.

The Free plan costs $0 and includes five meeting transcripts per month, unlimited enterprise search, summaries for meetings, email and messaging, personalized meeting coaching, basic integrations, Topic Readouts, support for 20+ languages, and Windows, macOS, Android, and iOS applications. The free plan does not require a credit card.

Pro is currently listed at $15 per user per month with annual billing, compared with $19.75 per user per month when billed monthly. It adds unlimited meeting transcripts, unlimited storage, priority report processing, 100 file-upload credits per month, premium integrations, and workspace access.

Enterprise is listed at $22.50 per user per month annually or $29.75 monthly. It adds audio and video playback, video highlights, 200 file-upload credits per month, and premium support.

Enterprise+ is listed at $29.75 per user per month annually or $39.75 monthly. It includes Enterprise functionality plus HIPAA compliance, SAML and SCIM, domain capture, custom data-retention policies, 300 file-upload credits per month, and workspace onboarding. The plan currently requires five or more licenses.

One important difference between Read and some competitors is that the free plan has a relatively clear meeting-count limit rather than unlimited meeting capture. The current pricing page specifies five meeting transcripts per month and a one-hour maximum meeting length on Free. Paid plans increase the maximum meeting duration to four hours, while Enterprise+ allows up to eight hours.

The pricing structure therefore makes the Free plan useful for light personal use, while frequent meeting users will quickly need to consider Pro or higher.

Security Matters Because Read Stores Conversation Data

Meeting assistants handle information that can be sensitive, confidential, or commercially important.

Read AI’s current security materials say the company is SOC 2 Type II compliant, supports HIPAA with the relevant Enterprise+ configuration, supports GDPR requirements, and uses AES-256 encryption for data at rest and in transit.

Read also states that it does not sell meeting, transcript, or personal data to advertisers or third parties. Its current privacy materials say AI training is off by default and requires explicit opt-in for participation in its customer experience improvement program.

Enterprise customers also get additional controls, including SAML-based SSO, SCIM provisioning, domain capture, configurable data retention, and workspace-level controls.

This does not remove the need for an organization to review its own privacy and compliance requirements. Recording a meeting involves more than selecting a software plan. Teams should consider participant consent, company policy, data retention, access permissions, and the regulations that apply to their meetings.

Read itself emphasizes notification and host approval when its meeting assistant joins a call.

Where Read AI Fits Best

Read AI has a broad audience because its features extend well beyond basic note-taking.

An individual professional may use it for transcripts, meeting summaries, coaching, and meeting search. A sales representative may use it to prepare for client calls, retrieve previous customer context, and connect information to Salesforce or HubSpot.

Customer success teams can use recurring meeting recaps and searchable history to preserve account knowledge. Managers can use coaching and meeting analytics to understand participation patterns. Recruiters can use transcription and structured meeting reports for interviews. Consultants can capture both online and in-person conversations.

Larger organizations have another reason to look at Read: administration.

Enterprise+ adds controls around SAML, SCIM, domain capture, data retention, and HIPAA configuration, which are designed for organizations with stricter governance requirements.

The product may be less suitable for someone who only needs occasional transcription and does not care about search, analytics, integrations, or workflow automation.

That is the central point with Read AI.

Its value is not just the transcript.

It comes from everything built around the transcript.

Read AI Is Evolving Into a Workplace AI Layer

Read AI has moved considerably beyond the traditional idea of an automated meeting notetaker.

Its current product connects meetings with email, messaging, calendars, CRM records, documents, and search. Ask Read can retrieve information across those sources. Ask Read Actions can turn answers into actions. Meeting Coach can analyze communication habits. Mobile apps can capture in-person conversations. API and MCP support can connect Read data to developer-built workflows and external AI assistants.

The company’s product releases during 2026 reinforce that direction. Read introduced Ask Read Actions in January, expanded integrations and its Ada assistant in March, and continued developing MCP and AI-agent functionality throughout the year.

That wider scope is both the product’s biggest attraction and something buyers need to understand.

Someone looking for a clean, simple meeting notetaker may not need all of these layers. But a team trying to connect meeting knowledge with CRM systems, communication tools, documents, email, search, and AI workflows may find the broader approach more relevant.

Read AI is ultimately trying to answer a bigger question than “What did we say?”

It is trying to connect what happened in a conversation with what needs to happen next.

For meeting-heavy professionals and organizations, that makes Read AI much more than a transcript generator. It is becoming a system for turning conversations into searchable context, communication insights, and follow-up work.