AI app for customer support · no coding needed
Unified message reply coordination portal
Reduce missed and inconsistent replies while keeping every send under human review.
Made for: Support teams and professionals handling replies across chat apps
What it does for you
The problem
Replies are drafted and tracked across several messaging apps, so context, promises and pending messages are missed.
What it gives you
Reviewed reply drafts and tracked pending messages
What you give it
Connected chat threadscontact historycalendar availabilityapproved facts
Build your own version of ToneBird, Chalked for Mac and more
One app with what these 8 AI tools do, yours to keep and change: ToneBird, Chalked for Mac, Blueberry, RPLY, Interachat, Pally, folk, Tanka.
Everything these tools do, in one app
- In-app reply drafting Generates suggested replies directly inside the messaging app you are already using.Found in ToneBird, Chalked for Mac, Blueberry and 5 more
- Conversation context use Uses the current thread and past conversation history to inform the suggested reply.Found in ToneBird, Chalked for Mac, Blueberry and 5 more
- Review before sending Lets the user review, edit, or approve every draft before it is sent.Found in ToneBird, Chalked for Mac, Blueberry and 4 more
- Per-contact tone matching Adjusts the writing style and tone for each individual contact.Found in ToneBird, Blueberry, Pally
- Long-term relationship memory Remembers past promises, facts, and relationship details per contact across sessions.Found in ToneBird, Pally, folk and 1 more
- Cross-app context Pulls context from multiple platforms such as email and chat for the same person.Found in ToneBird, Pally, Tanka
- Pending reply tracking Keeps track of messages that still need a response so they are not overlooked.Found in ToneBird, RPLY
- Calendar-aware suggestions Uses live calendar availability to inform the reply.Found in Chalked for Mac
- Voice revision Lets the user correct or change the intended reply by voice instead of retyping.Found in Chalked for Mac
- Alternative reply variations Offers different draft options or tones to choose from before sending.Found in Blueberry
- Automatic replies Sends replies automatically to messages that have gone unanswered.Found in RPLY, Pally
- Natural language search Finds past messages across conversations using plain-language queries.Found in Interachat
- In-chat AI assistant Provides summaries, answers, and clarifications inline within the conversation.Found in Interachat, folk, Tanka
- Outbound phone calls Places calls on the user's behalf, such as booking tables or waiting on hold.Found in Pally
- Secure purchasing Buys items using locked one-time virtual cards without exposing real card details.Found in Pally
- Proactive alerts Sends reminders and flags things that need attention before the user asks.Found in Pally, folk
- Meeting notetaking Joins meetings, transcribes them, and surfaces follow-up items.Found in folk
- Task and decision capture Organizes key insights, decisions, and to-dos directly from chat groups.Found in Tanka
How it works, step by step
- Draft replies inside connected chat apps
- Use thread and history context for each draft
- Require review, edit or approval before sending
- Match tone per contact
- Remember promises and facts per contact
- Pull context from email and chat for the same person
- Track messages still needing a response
- Use calendar availability in suggestions
- Accept voice corrections to drafts
- Offer alternative reply variations
- Send automatic replies only to approved unanswered cases
- Search past messages in plain language
- Answer and summarize inline in the conversation
- Place outbound calls on request
- Buy items with locked one-time virtual cards
- Send proactive reminders and flags
- Join meetings, transcribe and surface follow-ups
- Capture tasks and decisions from chat groups
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed reply drafts and tracked pending messages with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Unified message reply coordination portal with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Unified message reply coordination portal with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data195 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce missed and inconsistent replies while keeping every send under human review. For support teams and professionals handling replies across chat apps, convert connected chat threads, contact history, calendar availability and approved facts into reviewed reply drafts and tracked pending messages. The benefit is a testable hypothesis, measured through replies sent per support hour and overdue unanswered messages; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, connect authorized chat apps, contact history, calendar availability and approved facts, then follow this sequence: 1. Draft replies inside connected chat apps. 2. Use thread and history context for each draft. 3. Require review, edit or approval before sending. Resolve uncertain cases with qualified reviewers, approve reviewed reply drafts and tracked pending messages, and measure replies sent per support hour and overdue unanswered messages against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One connected chat app set and approved contact list; final sending and purchasing remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve contact privacy, source attribution, message accuracy and usage permissions. Named owners approve substantive changes and sending scope. One connected chat app set and approved contact list; final sending and purchasing remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One connected chat app set and approved contact list; final sending and purchasing remain human. Implement one approved input format, a bounded representative case set and the first two task modules: draft replies inside connected chat apps; use thread and history context for each draft. Support the third module with operator review: require review, edit or approval before sending. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Authorized chat apps, email, calendar and contact records. Cloud message storage, CRM import/export and support destinations. Start with file exchange and validate destination specifications before promising direct sending. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Connected inbox and pending queue, Thread workspace with draft panel, Contact and relationship record, Review and send confirmation. Use a left-hand list of conversations needing replies, a central thread view, and a right-hand panel for drafts, contact memory, calendar slots and alerts. Let users compare draft variations side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant message. Make the task-specific outcome reviewed reply drafts and tracked pending messages visible beside its evidence, review state and value baseline.
