Summarise WhatsApp Chats with AI (Step-by-Step) threads move fast. Important decisions, tasks, and emotional cues can disappear in the scroll. Chat analysis brings them back to the surface, so you can act with clarity instead of guesswork.
The 7 insights teams and couples use most
Action items:** Automatically extract tasks and next steps so nothing slips through.
Decision recap: Capture the final call when a long discussion ends in a single line.
Tone shifts: Spot when the conversation changes from calm to tense, or from uncertain to resolved.
Key contributors: See who drives progress and who needs follow-ups.
Meeting summaries: Convert chat planning into a clear agenda and recap.
Follow-up reminders: Surface open questions and unresolved threads.
Relationship signals: Highlight supportive moments and stress points in personal chats.
WhatsApp is not a project management tool, but millions of teams and families use it as one anyway. The result is that critical information gets buried under memes, voice notes, and reaction emojis. The seven categories above represent the signal that consistently matters most, whether you are running a small business group chat or reviewing a months-long personal conversation.
When a conversation contains hundreds of messages, manually scanning for tasks is unreliable. People phrase commitments differently: "I'll handle it," "can you send that over?", "remind me Thursday." A language model trained on conversational text recognises these patterns regardless of phrasing. ThreadRecap identifies task language, associates it with the person who made the commitment, and surfaces it in a structured list. The output is directly usable: copy it into a project tracker, share it back to the group, or attach it to a meeting note.
Decision recap: finding the conclusion in a long thread
WhatsApp discussions often meander before landing on a conclusion. The decision recap insight isolates the moment the group converged, even when the final agreement appears in a single short message after fifty lines of debate. This is particularly useful for remote teams who use WhatsApp as an async channel: anyone who joined late or missed the thread can read the recap without wading through the full history.
Tone shifts: reading the emotional arc of a conversation
Tone-shift detection identifies when a conversation moves from tense to resolved or from uncertain to decisive. This is more nuanced than simple sentiment scoring. A conversation can start with conflict, move through negotiation, and end with consensus — and each of those phases has a different emotional signature. Knowing where the shift happened helps you understand what changed the dynamic: a specific message, a concession, or the introduction of new information. For managers reviewing team chats, tone-shift data can flag conversations that ended unresolved and may need a follow-up.
Key contributors: content and ownership, not message volume
A common misconception is that the most active person in a chat is the most important contributor. Key-contributor analysis is based on message content and task assignment, not raw message count alone. Someone who sends two messages — one that frames the problem and one that proposes the accepted solution — contributes more meaningfully than someone who sends thirty messages of acknowledgement. ThreadRecap weights contribution by the decisions and tasks that can be traced back to each participant, giving you an accurate picture of who is actually driving outcomes.
Meeting summaries: from planning chat to structured agenda
Many teams plan meetings entirely inside WhatsApp before moving to a call. By the time the meeting starts, the agreed agenda is buried in the chat history. The meeting summary insight extracts the agreed points, the time and participants mentioned, and any pre-meeting decisions, and formats them as a structured recap. After the call, you can run the same analysis on any follow-up messages to capture what was actioned.
Follow-up reminders: open loops that need closing
Not every question in a WhatsApp thread gets answered in that thread. Follow-up reminders surface messages that contained a question or a request that was never explicitly resolved. This is especially valuable in long group chats where a question gets posted, the conversation moves on, and the original sender assumes someone else handled it. Surfacing those open loops prevents tasks from falling through the cracks between what was said and what was done.
Relationship signals: the insight that goes beyond work
Relationship signals are relevant outside of professional contexts. In personal chats, they highlight moments of expressed support, recurring stress points, or patterns of one-sided communication. For couples reviewing a period of difficult communication, or for someone trying to understand the health of a friendship, these signals provide a structured way to see patterns that are hard to notice message by message.
How WhatsApp exports work
Before running any analysis, you need a WhatsApp export file. WhatsApp's built-in export function produces a ZIP archive. A WhatsApp export .zip always contains a _chat.txt file alongside any shared media attachments. The _chat.txt file holds the full message history in a structured plain-text format, with timestamps, sender names, and message content. Media files — images, documents, audio — are included in the ZIP but stored separately from the text log.
ThreadRecap accepts this ZIP directly. There is no need to unzip the file or convert any formats before uploading. ThreadRecap processes WhatsApp exports containing 60,000 or more messages and ZIP files up to 2 GB, which covers even the largest long-running group chats. Most personal and small-team chats fall well within those limits.
Voice notes: a frequently overlooked source of information
Voice notes are a significant part of WhatsApp communication, particularly in cultures and teams where typing long messages is impractical. They are stored in the export ZIP as .opus or .m4a files, depending on the platform the sender used. ThreadRecap transcribes voice notes in .opus and .m4a formats using OpenAI Whisper at approximately 95% accuracy on clear audio. This means the content of voice notes is included in the analysis alongside the text messages, so a decision made verbally in a voice note does not get missed by the action-item or decision-recap extraction.
The 95% accuracy figure applies to clear audio with a single speaker and minimal background noise. In noisier recordings the accuracy will be lower, but the transcript is still useful for understanding the gist of what was said. ThreadRecap includes the raw transcript alongside the analysis, so you can verify any transcription that looks uncertain.
Export size and what it means for your analysis
A 60,000-message export sounds large, but it is not unusual for active WhatsApp groups. A group of ten people exchanging twenty messages per day will accumulate 60,000 messages in about ten years. A more active group of thirty people can reach that figure in under two years. The ability to process exports at this scale means you can analyse the full history of a project or relationship without needing to split the export into chunks.
Chat analysis results are typically available within minutes of uploading a WhatsApp export file. The exact time depends on the size of the export and the number of voice notes that need transcription, but for a typical export of a few thousand messages with a handful of voice notes, the wait is short enough to review the results in the same session you started the upload.
Make insights actionable
The best analysis turns into actions: a shared task list, a summary sent to the team, or clarity on what to address next. The chat analyzer makes it easy to upload your export and get these insights in minutes.
Turn your WhatsApp chat into an action plan with ThreadRecap.
The value of chat analysis is not in the report itself but in what you do with it. An extracted action item list is only useful if it gets into the hands of the people responsible for those actions. A tone-shift report is only useful if it prompts a conversation or a follow-up. ThreadRecap is designed to make the output shareable: you can copy sections, export summaries, or use the results directly in a follow-up message to your group.
Choosing the right insights for your use case
Different use cases call for different combinations of insights. A small business owner reviewing a supplier negotiation chat will care most about decision recaps and action items. A team lead reviewing a project group will want key contributors, follow-up reminders, and meeting summaries. Someone reviewing a personal chat will find tone shifts and relationship signals most relevant. You do not need to read every section of the analysis; the structure is designed so you can go directly to the insight that matters for your specific question.
Combining WhatsApp analysis with other records
WhatsApp analysis works well alongside other sources of project or relationship information. If your team uses WhatsApp alongside email or a project tracker, the chat analysis gives you the informal record — the quick decisions, the verbal commitments, the context that never made it into the official system. Combining the two gives you a more complete picture of how a project actually progressed, not just how it was officially documented.
For personal use, WhatsApp is often the most complete record of a relationship's day-to-day communication. Analysing it does not replace direct conversation, but it can provide context that is difficult to reconstruct from memory alone, especially when reviewing a period of time that was stressful or complex.
The practical starting point is the same in every case: export the chat from WhatsApp, upload the ZIP to ThreadRecap, and review the structured insights that come back. The seven categories covered here represent the most consistently useful outputs across the widest range of situations, which is why they are the foundation of any serious WhatsApp chat analysis.
