AI app for product development · no coding needed
Product signal aggregation and reporting workspace
Reduce manual status gathering while keeping every recommendation tied to its evidence.
Made for: Product managers and product teams coordinating work across several connected tools
What it does for you
The problem
Work signals are scattered across trackers, chat, calls and repositories, so status, blockers and evidence are assembled by hand and reports go stale.
What it gives you
Reviewer-approved prioritized summaries and reports
What you give it
Connected tool datacheck-in messageswork artifactssales call notespost-deployment metrics
Build your own version of Samepage Signals, Cleo AI and more
One app with what these 5 AI tools do, yours to keep and change: Samepage Signals, Cleo AI, Sharpsana, Eodly, UniDeck.
Everything these tools do, in one app
- Multi-source data aggregation Collects data from several connected tools into one place.Found in Samepage Signals, Cleo AI, Sharpsana and 1 more
- Daily automated summaries Produces a regular summary or report without manual searching.Found in Samepage Signals, Sharpsana, Eodly
- Work status tracking Shows what has shipped, what is in progress, and what is blocked.Found in Samepage Signals, Eodly
- Prioritized recommendations Highlights the top bet or signal that needs attention.Found in Samepage Signals, Cleo AI
- Evidence chain Shows the supporting evidence behind a recommendation or flag.Found in Cleo AI, Eodly
- Draft specification Provides a draft spec for a recommended product change.Found in Cleo AI
- Post-deployment metric monitoring Watches metrics after a change and reports whether it worked.Found in Cleo AI
- Coding-agent failure summaries Summarizes failure traces from coding agents.Found in Cleo AI
- Task and message automation Creates tasks or sends messages based on findings.Found in Sharpsana
- Slack and Telegram bot Lets team members interact with the tool where they already work.Found in Sharpsana
- Privacy and security controls Offers encrypted credentials, granular syncing, and data deletion options.Found in Sharpsana
- Competitor tracking Tracks competitor pricing changes and new product announcements.Found in Samepage Signals
- Feature idea extraction Identifies new feature ideas from sales calls that are missing from the roadmap.Found in Samepage Signals
- Dynamic user profile Builds a profile from voice, tone, and inferred goals to personalize the feed.Found in Samepage Signals
- Product playbook Includes built-in articles on prioritization and PRD writing.Found in Samepage Signals
- Identity matching across platforms Matches each teammate's identity across tools for accurate attribution.Found in Eodly
- Check-in vs. artifact cross-check Compares check-in messages against real work artifacts over multiple days.Found in Eodly
- Silent vs. slipping distinction Separates no-signal silence from claims contradicted by evidence.Found in Eodly
- Absence suppression Marks calendar-based absences to suppress alerts.Found in Eodly
- Dismissible claim-evidence flags Surfaces each flag as a claim-evidence pair that can be dismissed in one click.Found in Eodly
- AI slide layout generation Generates slide layouts based on input content.Found in UniDeck
- Design and formatting suggestions Suggests automatic design and formatting improvements.Found in UniDeck
- Template library Provides customizable themes and templates.Found in UniDeck
- Cloud storage integration Connects to popular cloud storage services for file management.Found in UniDeck
- Team collaboration tools Supports team editing and feedback.Found in UniDeck
How it works, step by step
- Aggregate data from connected tools into one place
- Produce daily automated summaries without manual searching
- Track what has shipped, what is in progress and what is blocked
- Highlight the top bet or signal needing attention
- Show the supporting evidence behind each recommendation or flag
- Draft a specification for a recommended product change
- Monitor post-deployment metrics and report whether a change worked
- Summarize failure traces from coding agents
- Create tasks or send messages based on findings
- Answer team questions through a Slack and Telegram bot
- Apply encrypted credentials, granular syncing and data deletion controls
- Track competitor pricing changes and product announcements
- Extract feature ideas from sales calls missing from the roadmap
- Build a dynamic user profile from voice, tone and inferred goals
- Provide built-in articles on prioritization and PRD writing
- Match teammate identity across platforms for accurate attribution
- Cross-check check-in messages against real work artifacts over multiple days
- Separate no-signal silence from claims contradicted by evidence
- Mark calendar-based absences to suppress alerts
- Surface each flag as a dismissible claim-evidence pair
- Generate slide layouts from input content
- Suggest design and formatting improvements
- Provide customizable themes and templates
- Connect to cloud storage services for file management
- Support team editing and feedback
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewer-approved prioritized summaries and reports 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 Product signal aggregation and reporting workspace 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 Product signal aggregation and reporting workspace 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 Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data198 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 manual status gathering while keeping every recommendation tied to its evidence. For product managers and product teams coordinating work across several connected tools, convert connected tool data, check-in messages, work artifacts, sales call notes and post-deployment metrics into reviewer-approved prioritized summaries and reports. The benefit is a testable hypothesis, measured through reporting hours saved per week and accepted recommendations per review cycle; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect connected tool data, check-in messages, work artifacts, sales call notes and post-deployment metrics, then follow this sequence: 1. Aggregate data from connected tools into one place. 2. Produce daily automated summaries without manual searching. 3. Track what has shipped, what is in progress and what is blocked. 4. Highlight the top bet or signal needing attention. 5. Show the supporting evidence behind each recommendation or flag. Resolve uncertain cases with qualified reviewers, approve reviewer-approved prioritized summaries and reports, and measure reporting hours saved per week and accepted recommendations per review cycle against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the 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 fixed set of connected sources and one report format; final prioritization and roadmap decisions remain with the product owner. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, quotation accuracy and usage permissions. Product owners approve substantive changes and roadmap scope. One fixed set of connected sources and one report format; final prioritization and roadmap decisions remain with the product owner. 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 fixed set of connected sources and one report format; final prioritization and roadmap decisions remain with the product owner. Implement one approved input format, a bounded representative case set and the first two task modules: aggregate data from connected tools into one place; produce daily automated summaries without manual searching. Support the remaining modules with operator review: track what has shipped, what is in progress and what is blocked; highlight the top bet or signal needing attention; show the supporting evidence behind each recommendation or flag. 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
Connected trackers, chat tools, repositories, call recording tools and cloud storage. Start with file exchange and validate destination specifications before promising direct publishing. 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 sources and permissions, Evidence-backed analysis workspace, Report and delivery. Use a source list with sync state, a central feed of prioritized signals with claim-evidence pairs, and a right-hand panel for evidence, owners and comments. Let users compare a check-in claim against the matching work artifact. Display draft, changes requested and approved states. Provide a shareable report link with comments anchored to the relevant signal. Make the task-specific outcome reviewer-approved prioritized summaries and reports visible beside its evidence, review state and value baseline.
