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How to Turn Meeting Transcripts Into a Searchable AI Knowledge Base published: true

Cheetu AI — Tue, 02 Jun 2026 09:38:29 +0000

How to Turn Meeting Transcripts Into a Searchable AI Knowledge Base

Most meeting tools stop at the transcript.

They capture what was said.

That is useful.

But a transcript alone is not always easy to use.

A one-hour meeting can produce thousands of words. A team with daily calls can quickly generate hundreds of transcripts. A student may have dozens of lectures. A sales team may have hundreds of customer conversations.

The problem becomes obvious:

The information exists, but it is still hard to find.

At Cheetu AI, we think the next step is not simply better transcription.

The next step is conversation memory.

That means turning meetings, calls, lectures, and notes into a searchable knowledge layer that users can ask questions about later.

This post walks through how that kind of system can be designed.

The Problem With Raw Transcripts

Raw transcripts are valuable, but they have limitations.

They are often:

Too long to scan
Hard to search semantically
Missing useful structure
Disconnected from summaries
Difficult to compare across meetings
Not always clear about who said what
Not always connected to timestamps or source context A transcript tells you what happened.

But it does not always answer:

What did we decide?
Who owns the next step?
What risks were mentioned?
What did customers say about pricing?
Did we already discuss this topic before?

To answer those questions, we need to treat transcripts as structured knowledge.

Step 1: Capture Real-Time Transcript Segments
The first layer is real-time transcription.
Instead of storing one large transcript file, it is more useful to store the conversation as timestamped segments.
For example:

{
  "session_id": "meeting_2026_05_18",
  "segments": [
    {
      "segment_id": "seg_001",
      "speaker": "Speaker A",
      "start_time": "00:01:12",
      "end_time": "00:01:18",
      "text": "Let's prioritize onboarding improvements for Q3.",
      "language": "en"
    },
    {
      "segment_id": "seg_002",
      "speaker": "Speaker B",
      "start_time": "00:01:19",
      "end_time": "00:01:27",
      "text": "Agreed. The setup flow is still too manual for new teams.",
      "language": "en"
    }
  ]
}

This structure gives us the basic building blocks for memory:

Session ID
Segment ID
Speaker
Start time
End time
Transcript text
Language That makes it easier to retrieve not just the answer, but also the original source.

Step 2: Add Metadata Early
Metadata is what makes transcripts useful later.
Without metadata, everything becomes a wall of text.
Useful metadata may include:

Meeting title
Meeting type
Participants
Speaker labels
Language
Topic tags
Timestamp range
Customer or account name
Project name
Summary section
Action item owner
Due date

A segment with metadata might look like this:

{
  "segment_id": "seg_002",
  "session_id": "meeting_2026_05_18",
  "meeting_title": "Product Review",
  "meeting_type": "internal",
  "speaker": "Speaker B",
  "timestamp": "00:01:19",
  "text": "The setup flow is still too manual for new teams.",
  "language": "en",
  "topics": ["onboarding", "activation", "user experience"],
  "entities": ["new teams"],
  "importance": "medium"
}

This gives the system more ways to filter and retrieve information.

For example:

Find onboarding feedback from product meetings.

Show customer objections about setup complexity.

What activation risks were mentioned last month?

Step 3: Support Live Translation

For global teams, transcripts are not always in one language.
A meeting may include English, Spanish, Mandarin, Japanese, Korean, Arabic, French, or multiple languages in the same session.
If the product supports live translation, it is useful to store both the original text and the translated text.
For example:

{
  "segment_id": "seg_014",
  "speaker": "Speaker C",
  "timestamp": "00:08:42",
  "original": {
    "language": "es",
    "text": "Necesitamos una cronología clara para el lanzamiento."
  },
  "translation": {
    "language": "en",
    "text": "We need a clear timeline for the launch."
  }
}

This supports two important use cases.
First, users can follow the conversation in real time.
Second, users can search later in their preferred language.
A user might ask in English:

What did the team say about the launch timeline?

Even if the original discussion happened partly in Spanish.

That matters because conversation memory should not be limited by the language of the original meeting.

Step 4: Generate Structured Summaries

A summary should not just compress a transcript.
It should create structure.
For meeting workflows, useful summaries often include:

Key points
Decisions
Risks
Open questions
Action items
Owners
Due dates A structured summary might look like this:

## Key Points

* The team reviewed onboarding friction for new users.
* Setup complexity is affecting activation.
* Documentation may need to be simplified.

## Decisions

* Prioritize onboarding improvements in Q3.
* Move analytics improvements behind onboarding work.

## Risks

* Current setup flow may create drop-off during activation.
* New users may not understand how to invite teammates.

## Action Items

* Maya: Audit onboarding steps by Friday.
* Alex: Review activation data from the last cohort.
* Sam: Draft simplified setup documentation by next Tuesday.

## Open Questions

* Should enterprise users get a separate onboarding flow?
* Should onboarding guidance happen in-product or by email?

This kind of summary is easier to review.
It is also easier to index.
For example, action items can be searched differently from decisions.
Risks can be grouped across multiple meetings.
Open questions can be tracked over time.

Step 5: Chunk the Conversation

Large transcripts are difficult to retrieve accurately.
A better approach is to break the conversation into chunks.
Chunks can be based on:

Time windows
Speaker turns
Topic shifts
Summary sections
Question-and-answer pairs
Action item boundaries

A simple chunk object could look like this:

{
  "chunk_id": "chunk_001",
  "session_id": "meeting_2026_05_18",
  "title": "Onboarding friction discussion",
  "start_time": "00:10:12",
  "end_time": "00:16:48",
  "text": "The team discussed setup complexity, activation drop-off, and onboarding documentation.",
  "topics": ["onboarding", "activation", "documentation"],
  "speakers": ["Speaker A", "Speaker B", "Speaker C"]
}

Good chunks should be:

Small enough to retrieve accurately
Large enough to preserve context
Connected to timestamps
Connected to source transcript segments
Tagged with useful metadata

Chunking is one of the most important design decisions in a conversation memory system.
Bad chunks create bad retrieval.
Good chunks make answers more accurate and easier to verify.

Step 6: Build a Search Index

Once the conversation is chunked, it can be indexed.
A useful search system may combine:

Keyword search
Vector search
Metadata filters
Time filters
Speaker filters
Meeting-type filters
Language filters

A simplified search request might look like this:

{
  "query": "What did customers say about onboarding friction?",
  "filters": {
    "meeting_type": "customer_call",
    "date_range": "last_90_days",
    "topics": ["onboarding"]
  },
  "top_k": 5
}

The system should return answers with source context:

{
  "answer": "Customers said onboarding felt too manual, especially during workspace setup and teammate invitation.",
  "sources": [
    {
      "meeting_title": "Customer Call - Acme",
      "timestamp": "00:12:44",
      "speaker": "Customer",
      "text": "The setup process takes too long when inviting multiple teammates."
    },
    {
      "meeting_title": "Customer Call - Northstar",
      "timestamp": "00:27:10",
      "speaker": "Customer",
      "text": "We had to ask support for help during initial setup."
    }
  ]
}

This is important.
The goal is not only to generate an answer.
The goal is to help the user trust the answer.

Step 7: Make Answers Source-Grounded

AI answers can be useful, but they can also feel uncertain.
For conversation memory, source grounding is critical.

A good answer should show:

Which meeting it came from
Who said it
When it was said
What the original transcript said
Whether the text was translated
How the answer connects to the source

For example:

Answer:
Customers were concerned that onboarding required too many manual steps.

Sources:
1. Customer Call - Acme
   00:12:44
   Speaker: Customer
   "The setup process takes too long when inviting multiple teammates."

2. Customer Call - Northstar
   00:27:10
   Speaker: Customer
   "We had to ask support for help during initial setup."

This turns the system from a black box into an assistant users can inspect.
That matters for trust.
Especially when the content comes from meetings that affect product, sales, hiring, support, education, or strategy.

A Simple Architecture
At a high level, the system might look like this:

Audio Stream
    ↓
Real-Time Transcription
    ↓
Speaker Labels + Timestamps
    ↓
Live Translation
    ↓
Structured Summary
    ↓
Chunking + Metadata
    ↓
Search Index
    ↓
Source-Grounded AI Answers

Each layer adds value.

The technical challenge is making this pipeline reliable.
The product challenge is making it feel simple.

What This Enables
A searchable conversation memory system can support many workflows.
Product Teams
Product teams can ask:

What onboarding problems did customers mention this month?

Which feature requests came up most often?

What risks did we identify during the last roadmap review?

Sales Teams
Sales teams can ask:

What pricing objections came up in recent calls?

Which customers asked about enterprise security?

What follow-ups were promised to this account?

Students and Researchers
Students and researchers can ask:

Summarize the main points from my last three lectures.

Find the part where the professor explained reinforcement learning.

What open questions came up during the research discussion?

Global Teams
Global teams can ask:

What was decided in the multilingual planning meeting?

Show the original and translated version of that discussion.

Which regional launch risks were mentioned?

Why No Meeting Bot Matters
Many AI meeting tools use a bot that joins the call.
That can work.
But it can also create friction.

A visible bot can make participants feel watched.
External guests may ask who joined.
Some teams may restrict meeting bots.
Sensitive conversations may feel less natural.
The assistant can become another participant in the room.

A no-bot approach feels lighter.
The assistant supports the user without changing the meeting dynamic.
For Cheetu AI, this is an important design principle:

Help people capture, understand, summarize, and search conversations without requiring an AI bot to join the meeting.

The technology should support the conversation.
It should not become the conversation.

Design Principles for Conversation Memory
When building this kind of system, these principles are important.

1. Preserve source context
Every answer should be traceable back to the original conversation.

2. Keep real-time output readable
Live transcription and translation should be fast, clean, and easy to scan.

3. Structure summaries around decisions
Users need outcomes, not just paragraphs.

4. Make search natural
People should be able to ask questions in plain language.

5. Support multilingual knowledge

Users should be able to search and review across languages.

6. Avoid unnecessary meeting friction
The assistant should not make the meeting feel crowded or unnatural.

Final Thought

The future of meeting tools is not only about recording more conversations.

It is about making conversations easier to understand, reuse, and trust.

A transcript is a starting point.

A summary is a useful layer.

But the bigger opportunity is conversation memory:

Real-time transcription
Live translation
Structured AI summaries
Searchable archives
Source-grounded answers
A no-bot meeting experience

That is the direction we are exploring with Cheetu AI.

The question is no longer:

Can AI take better meeting notes?

The better question is:

Can AI help us remember the conversations that matter?

Learn more: Cheetu AI

Designing a Conversation Memory Layer for Real-Time Meetings

Cheetu AI — Tue, 26 May 2026 07:46:05 +0000

Designing a Conversation Memory Layer for Real-Time Meetings

Most AI meeting tools focus on one simple promise:

> Join the meeting, record the conversation, and generate notes afterward.

That workflow can be useful.

But it also creates a question:

> What if the most valuable part of a meeting assistant is not the note after the meeting, but the memory it creates during and after the conversation?

At **Cheetu AI**, we have been exploring this idea through four product layers:

* Real-time transcription
* Live translation
* AI summaries
* Searchable conversation memory

This post breaks down how these layers can work together to turn conversations into reusable knowledge.

---

## Why Meeting Notes Are Not Enough

A meeting is not just a temporary event.

It often contains:

* Customer feedback
* Product decisions
* Sales objections
* Project risks
* Technical discussions
* Hiring signals
* Action items
* Follow-up commitments

The problem is that most of this information is easy to lose.

It may exist in:

* Someone's memory
* A recording
* A transcript
* A chat message
* A manual note
* A follow-up email

But when you need the exact context later, it is often hard to find.

For example:

```text
What did the customer say about onboarding?

Who owned the follow-up after the product review?

Did we already discuss the Q3 launch risk?

Where did we mention Spanish caption support?

A good meeting system should help answer these questions without forcing users to manually search through recordings or notes.

Layer 1: Real-Time Transcription

The first layer is transcription.

But timing matters.

A transcript generated after the meeting is useful as a record.

A transcript generated during the meeting becomes part of the meeting interface.

Real-time transcription helps users:

Follow fast conversations
Recover missed details
Stay focused instead of taking manual notes
Review who said what
Capture timestamps automatically

A simple transcript segment might look like this:

{
  "session_id": "meeting_001",
  "speaker": "Speaker A",
  "start_time": "00:04:12",
  "end_time": "00:04:18",
  "text": "Let's prioritize onboarding improvements for Q3.",
  "language": "en"
}

This structure may look basic, but it is already useful.

It gives the system:

Speaker context
Time context
Language context
Searchable text
A foundation for summaries and retrieval

Layer 2: Live Translation

Global teams often share one working language.

But a shared language does not mean everyone participates equally.

Some people may understand most of the conversation but miss nuance.

Some may need more time to process what was said.

Some may hesitate to ask questions because they are still translating mentally.

Live translation can reduce this gap.

A useful translation experience should support:

Original captions
Translated captions
Speaker labels
Low latency
One-click language switching
A clean reading experience

A translated segment could be represented like this:

{
  "speaker": "Speaker A",
  "timestamp": "00:04:12",
  "original": {
    "language": "en",
    "text": "Let's prioritize onboarding improvements for Q3."
  },
  "translation": {
    "language": "es",
    "text": "Prioricemos las mejoras de incorporación para el tercer trimestre."
  }
}

The goal is not just translation.

The goal is participation.

Translation after the meeting helps people review.

Translation during the meeting helps people join the conversation.

Layer 3: AI Summaries

Many AI summaries feel too generic.

They compress the conversation, but they do not always make the outcome clearer.

A weak summary might say:

The team discussed onboarding improvements and next steps.

That is readable, but not very actionable.

A better summary should separate the meeting into useful sections:

Key points
Decisions
Risks
Open questions
Action items
Owners
Due dates

For example:

## Decisions

* Prioritize onboarding improvements for Q3.
* Keep analytics improvements in scope, but make onboarding the first priority.

## Risks

* The current setup flow may reduce activation.
* Documentation may not be clear enough for new users.

## Action Items

* Maya: Audit onboarding steps by Friday.
* Alex: Review activation metrics from the last cohort.
* Sam: Draft updated setup docs before next Tuesday.

## Open Questions

* Should enterprise users get a separate onboarding path?
* Do we need in-product guidance during setup?

This format is more useful because it connects the conversation to execution.

A strong AI summary should answer:

What happened?
What matters?
What changed?
What is still unresolved?
Who needs to do what next?

Layer 4: Searchable Conversation Memory

Transcription captures the conversation.

Translation makes it easier to understand.

Summaries make it easier to review.

But search makes it reusable.

Once conversations are structured, they can become a searchable memory layer.

Instead of asking:

Where is the recording?

A user can ask:

What did the customer say about onboarding friction?

Instead of asking:

Did we already talk about this?

A user can ask:

What decisions did we make about the Q3 roadmap?

Instead of searching manually through notes, a user can ask:

Summarize all open risks mentioned in meetings this week.

A simplified search request might look like this:

{
  "query": "What did customers say about onboarding friction?",
  "filters": {
    "date_range": "last_90_days",
    "conversation_type": "customer_call"
  },
  "top_k": 5
}

A useful response should include source context:

{
  "answer": "Customers mentioned that onboarding felt too manual, especially during workspace setup and team invitation.",
  "sources": [
    {
      "session": "Customer Call - Acme",
      "timestamp": "00:12:44",
      "speaker": "Customer"
    },
    {
      "session": "Customer Call - Northstar",
      "timestamp": "00:27:10",
      "speaker": "Customer"
    }
  ]
}

Source context is important.

Without it, AI answers can feel uncertain.

With it, users can verify where the answer came from.

Why Source Context Matters

Conversation memory should not feel like a black box.

If an AI system gives an answer based on past meetings, users should be able to inspect the source.

A good source reference may include:

Meeting title
Speaker
Timestamp
Transcript segment
Original language
Translated text
Summary section

For example:

Source:
Customer Call - Acme
00:12:44
Speaker: Customer

"The setup process takes too long when inviting multiple teammates."

This makes the answer more trustworthy.

It also helps users return to the original moment instead of relying only on the generated response.

Why No Meeting Bot Can Improve the Experience

Many AI meeting assistants work by sending a bot into the call.

That approach is common, but it can create friction.

A visible meeting bot may cause issues when:

External guests do not recognize it
Participants feel monitored
Teams have strict recording policies
Sensitive conversations require more privacy
The bot changes the natural meeting dynamic

A no-bot approach can feel lighter.

The assistant supports the user without becoming another participant in the room.

For Cheetu AI, this is an important design direction:

Help users capture, understand, summarize, and search conversations without requiring an AI bot to join the meeting.

A Simple Architecture View

At a high level, a conversation memory layer could look like this:

Audio Stream
    ↓
Real-Time Transcription
    ↓
Speaker Labels + Timestamps
    ↓
Live Translation
    ↓
Structured AI Summary
    ↓
Chunks + Metadata
    ↓
Search Index
    ↓
Conversation Memory

Each layer adds a different kind of value.

Layer	Main Purpose
Transcription	Turn speech into text
Translation	Make conversations understandable across languages
Summary	Turn long conversations into structured outcomes
Metadata	Preserve speaker, time, topic, and language context
Search	Retrieve useful information later
Source context	Make AI answers verifiable

The product challenge is making this pipeline feel simple.

Users should not need to think about the system.

They should simply feel that their conversations are easier to follow, review, and search.

Example Use Cases

Product Teams

Product teams can use conversation memory to:

Search across user interviews
Find repeated customer pain points
Review product decisions
Track open questions from roadmap discussions

Sales Teams

Sales teams can use it to:

Find customer objections
Review commitments made during calls
Generate follow-up notes
Track account risks

Students and Researchers

Students and researchers can use it to:

Search lecture notes
Ask questions across past sessions
Summarize long discussions
Return to exact source moments

Global Teams

Global teams can use it to:

Follow meetings in a preferred language
Review original and translated captions
Reduce misunderstandings
Make participation more equal

Design Principles

When designing a conversation memory system, these principles matter.

1. Keep the Meeting Natural

The assistant should support the conversation without changing the room dynamic.

2. Make Real-Time Output Useful

Live transcription and translation should be readable, fast, and easy to scan.

3. Structure Summaries Around Action

Summaries should highlight decisions, risks, open questions, and action items.

4. Make Search Source-Grounded

Answers should include where the information came from.

5. Respect User Control

Conversation data should feel personal, private, and manageable.

Final Thought

The next generation of meeting tools should not only generate better notes.

They should help people understand conversations as they happen and reuse that knowledge afterward.

That means combining:

Real-time transcription
Live translation
Structured AI summaries
Searchable conversation memory
Source-grounded answers
A low-friction meeting experience

That is the direction we are exploring with Cheetu AI.

The interesting question is not only:

How do we record more meetings?

It is:

How do we help people remember the conversations that already matter?

Learn more: Cheetu AI

Building a Personal Conversation Memory Layer Without Adding a Meeting Bot

Cheetu AI — Wed, 20 May 2026 09:52:16 +0000

Most AI meeting tools follow the same pattern:

1. A bot joins your meeting.
2. It records the conversation.
3. It generates a transcript.
4. It sends a summary afterward.

That workflow can be useful, but it also creates friction.

A visible meeting bot can make people feel watched. Some teams have strict meeting permissions. External guests may not know who the bot is. And in many cases, the value of the tool only arrives after the meeting is already over.

At **Cheetu AI**, we have been exploring a different question:

> What if conversations became useful in real time, and searchable afterward — without adding another participant to the meeting?

That idea led us to think about meetings not just as events, but as personal and team knowledge streams.

---

## Meetings Are Knowledge Streams

A meeting is usually treated as a temporary event.

People join, talk, decide, assign tasks, and leave. Afterward, the useful information often gets scattered across recordings, transcripts, chat messages, notes, or someone’s memory.

But from a product design perspective, a meeting contains structured knowledge:

- Who said what
- When something was said
- What decisions were made
- Which questions are still open
- Which action items were assigned
- Who owns each next step
- What risks or objections were raised
- What language each participant was most comfortable using

If we treat conversations as knowledge streams, the goal becomes bigger than “generate meeting notes.”

The goal becomes:

> Capture the conversation, make it understandable in real time, summarize it clearly, and make it searchable later.

---

## 1. Real-time Transcription: The Foundation

The first layer is real-time transcription.

Transcription is not only useful because it creates notes. It changes the experience while the conversation is happening.

For example, live transcription helps when:

- A participant misses a sentence
- A non-native speaker needs text support
- A host wants to focus instead of taking notes
- A student wants to listen instead of typing everything
- An interviewer wants to stay engaged with the speaker

The key design factor is latency.

If transcription arrives too late, it becomes a record.

If transcription arrives in real time, it becomes part of the meeting interface.

A simplified transcript structure might look like this:

```json
{
  "session_id": "meeting_123",
  "segments": [
    {
      "speaker": "Speaker 1",
      "start_time": "00:03:12",
      "end_time": "00:03:18",
      "text": "Let's move the launch date to next Tuesday.",
      "language": "en"
    }
  ]
}

This gives the system something useful to work with later: speaker labels, timestamps, language metadata, and searchable text.

2. Live Translation: Making Global Conversations Easier

Transcription answers:

What was said?

Translation answers:

Can everyone understand it comfortably enough to participate?

In global teams, language ability is rarely equal.

One person may be fluent. Another may understand most of the conversation but need extra processing time. Someone else may avoid asking questions because they are still translating mentally.

Live translated captions can reduce that gap.

A useful live translation interface should support:

Original captions
Translated captions
Fast language switching
Minimal interruption
Clear speaker context
Real-time readability

For Cheetu AI, the goal is to show both original and translated captions on screen, while allowing viewers to switch languages with one click.

The product goal is not to turn every meeting into a formal interpretation session.

It is to make multilingual collaboration feel natural.

3. AI Summaries Should Be Structured

A common mistake in AI meeting tools is treating a summary as a shorter transcript.

But users usually do not want the same meeting in fewer words.

They want structure.

A useful meeting recap should answer:

What were the key points?
What decisions were made?
What risks came up?
What questions are still open?
Who owns the next step?
When is it due?

For example, instead of this:

The team discussed onboarding and agreed that improvements were needed.

A more useful summary looks like this:

## Decisions

- Simplify the onboarding checklist before launch.
- Prioritize user guidance for first-time users.

## Action Items

- Maya: Remove duplicate setup steps by Friday.
- Alex: Review activation metrics from the last cohort.
- Sam: Prepare updated help docs before next Tuesday.

## Open Questions

- Should enterprise customers get a separate onboarding flow?
- Do we need in-product tips for the setup process?

This is the difference between a passive summary and an execution layer.

Good AI summaries should help teams move from:

We talked about this.

to:

Here is what we decided, what is still unresolved, and what happens next.

4. Searchable Memory Is the Most Valuable Layer

Transcription captures the conversation.

Translation makes it understandable.

Summarization makes it reviewable.

But retrieval makes it reusable.

Most teams have valuable knowledge trapped inside conversations:

Customer calls
Sales demos
Product reviews
User interviews
Internal meetings
Lectures
Research discussions
Support calls

The problem is not that this knowledge does not exist.

The problem is that it is hard to find.

A searchable conversation archive changes the interface from:

Find the recording.

to:

Ask the knowledge base.

For example:

What did the customer say about pricing?

Which action items did we assign in the last product review?

Summarize all open risks mentioned in meetings this week.

Find the part where we discussed Spanish captions.

The most important design requirement here is source context.

If an AI answer comes from a past conversation, users should be able to see where it came from.

That source might include:

Meeting title
Speaker
Timestamp
Transcript segment
Original language
Translated text

A simplified retrieval result might look like this:

{
  "answer": "The customer was concerned that onboarding required too many manual setup steps.",
  "sources": [
    {
      "session": "Customer Call - April 18",
      "timestamp": "00:14:32",
      "speaker": "Customer"
    }
  ]
}

This helps users trust the answer and return to the original moment when needed.

5. Why No Meeting Bot Matters

Many AI meeting assistants rely on a bot joining the meeting.

That can be convenient, but it can also create social and operational friction.

A meeting bot may cause problems when:

Participants feel monitored
External guests do not recognize the bot
Meeting platforms restrict third-party bots
Organizations have strict security policies
The bot distracts from the conversation
Permissions become complicated

A no-bot approach feels lighter.

The assistant supports the user without becoming another participant in the room.

That is one of the core product ideas behind Cheetu AI: real-time transcription, live translation, AI summaries, and searchable memory without requiring an AI bot to join the meeting.

6. What This Unlocks

Once conversations become structured, translated, summarized, and searchable, many workflows become easier.

For product teams

Search across user interviews
Track repeated customer pain points
Review product decisions
Find risks mentioned in past meetings

For sales and customer success

Review objections from customer calls
Find commitments made during meetings
Generate follow-up notes
Track account risks

For students and researchers

Search lecture notes
Summarize long discussions
Ask questions across past sessions
Return to exact source moments

For global teams

Collaborate across languages
Review original and translated context
Reduce meeting misunderstandings
Make participation more equal

Final Thought

The next generation of meeting tools should not only create prettier notes.

They should help people understand conversations as they happen and reuse that knowledge afterward.

That means combining:

Real-time transcription
Live translation
Structured AI summaries
Searchable conversation memory
Source-grounded answers
A low-friction meeting experience

That is the direction we are exploring with Cheetu AI.

If your work depends on meetings, calls, lectures, interviews, or multilingual conversations, the real opportunity is not just to record more.

It is to remember better.

Learn more at Cheetu AI.