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Radiq

Updated July 24, 2026

Overview / Description

Radiq is an AI product management tool that automates the customer-to-code loop for product managers, turning scattered signal from Slack, Jira, Confluence, and meetings into developer-ready specs. Instead of manually chasing context, citing evidence, and rewriting specs engineers push back on, Radiq surfaces the decision to make, prioritizes it by the supporting evidence, and generates a spec grounded in the actual codebase. It then pushes that spec directly into the developer's IDE via MCP (Model Context Protocol), closing the gap between product intent and implementation. The pitch is a compression of time: what the company says took a week of stitching and rewriting now takes minutes. Radiq is aimed at PMs and product teams who spend hours each week reconstructing context across tools rather than deciding. It positions itself as "The Decision Engine for Product Teams" and lists developer-tools and artificial-intelligence among its focus areas (it is also a YC application-stage product). Pricing is not published on the site at the time of writing. Because Radiq is early and pulls signal from multiple connected systems, buyers should validate how well its evidence prioritization and code-grounded spec generation hold up against their own Slack/Jira/Confluence data before relying on it.

Used For

Product managers use Radiq to turn scattered Slack, Jira, and Confluence signal into evidence-prioritized, code-grounded specs pushed straight into the developer's IDE.

Pricing

Paid

Free

Pricing not published — visit the website for details.

View pricing

Pros & Cons

Pros

• Pulls signal from Slack, Jira, Confluence, and meetings so product context is assembled automatically instead of by hand • Generates specs grounded in the actual codebase, not just abstract requirements • Prioritizes decisions by supporting evidence and surfaces what to build next • Pushes finished specs straight into the developer's IDE via Model Context Protocol (MCP) • Aimed squarely at PM workflows, compressing context-gathering that the vendor says took a week into minutes

Cons

• Early, YC application-stage product, so it is unproven at scale • Pricing is not published, making cost hard to evaluate up front • Evidence prioritization and code-grounded spec quality need validation against your own data • Value depends on having Slack, Jira, and Confluence connected and well-maintained

Questions & Answers

Alternatives

Productboard, Zeda.io, ChatPRD, Dovetail

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Radiq | AI Tools Directory