Track product ideas, bugs, and enhancements efficiently while keeping your product roadmap firmly aligned with customer needs.
This system natively connects product managers, customers, and specific feature requests. It allows you to group approved requests into upcoming product releases without relying on duplicate data entry.
Built-in AI automatically sorts new requests into categories based on their description, pulls corporate data directly from customer websites, and instantly drafts user-facing release notes.
As user feedback piles up, relying on spreadsheets quickly turns into scattered rows, hidden columns, and lost context.
Instead of using fragile VLOOKUPs to tie feature requests to customers and release dates, a database naturally connects related information. When a customer's domain or contact details update, every linked feature request reflects the change instantly.
This means you can finally maintain a reliable single source of truth for your product roadmap. This is exactly what Softr Databases are designed for—giving you the foundation to scale feedback collection without the typical chaos.
Stop manually sorting and organizing recurring user feedback. You can track every request from initial submission through to final release with clear statuses, priority levels, and assigned product managers in one clean view.
Thanks to native Database AI agents, this system practically manages itself. It automatically researches customer details, categorizes incoming requests by reading the raw description, and summarizes completed features into clean release notes.
Manage system accounts, product manager roles, and track user-specific submissions
Store client organizations with AI-generated business summaries for better context
Track product ideas and bugs using AI to automatically categorize each submission
Schedule product updates and generate AI-powered release notes based on features
Built specifically for product teams and customer-facing departments who need a structured, reliable way to handle incoming feedback.
Start by customizing the database to fit your exact product workflow. You can easily modify the status dropdown options, add new priority levels, or adjust the AI prompts to better suit your industry's specific terminology.
Already have a massive backlog? You can import your existing CSV files to instantly populate your new tables and get right to work without losing any historical data.
When you're ready to scale your process, use an interface builder to turn this database into a robust external portal. By defining specific users and permissions, customers can securely log in to submit requests, while your internal team maintains complete oversight of the broader roadmap.
It is a structured system used to capture, organize, scale, and track product ideas, bugs, and enhancements. It helps teams prioritize their roadmap by connecting isolated feedback directly to the clients requesting them and the releases they belong to.
A no-code database gives product teams complete autonomy to launch internal tools without waiting on engineering resources. It provides a production-ready system that is vastly more robust than spreadsheets, while remaining entirely adaptable as your prioritization frameworks evolve.
Database AI agents can automate the most tedious parts of product management. In this template, they automatically categorize incoming requests by reading user descriptions, enrich customer profiles by searching the web, and draft public-facing release notes by summarizing finished features.
Yes, you can connect your database to an interface builder to create a custom feedback portal. This allows you to give actual clients and internal stakeholders a self-serve platform to submit requests, view real-time statuses, and track scheduled release dates safely.
Yes, this template is entirely free to copy and start using right away. Databases are included in Softr's free plan, mapping perfectly to standard startup usage, with higher-tier plans offering increased record limits as you scale.
Excel struggles with rapid scalability because columns can easily become mixed with different data types, such as text mixed into a date field. A structured database actively enforces clean data formats and uses strict relational links so assigning complex features to multiple customers never breaks.
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