Radiq

Product Introduction

1. Definition: Radiq is an AI-native Product Intelligence and Decision Intelligence platform designed to bridge the gap between customer feedback and engineering execution. It functions as an autonomous "customer-to-code" orchestration layer that aggregates unstructured signals from communication silos (Slack, Zoom, Jira) and transforms them into actionable, developer-ready technical specifications via the Model Context Protocol (MCP).

2. Core Value Proposition: Radiq exists to eliminate "context debt" for Product Managers and Engineering leads. By automating the synthesis of fragmented feedback, it provides evidence-based roadmapping through RICE scoring and direct IDE integration. The platform’s primary goal is to shift PMs from manual data synthesis to high-level decision-making, reducing the time from raw customer signal to code-ready PRD from weeks to minutes.

Main Features

1. Multi-Agent Ecosystem (The Engine): Radiq utilizes a coordinated team of specialized AI agents to process the product lifecycle. This includes the Intent Orchestrator (Router) for analyzing user intent, the Requirement Architect (PRD Builder) for synthesizing workspace context into structured PRDs, and the VoC (Voice of Customer) Agent for sentiment analysis. These agents perform real-time indexing of workspace documents and meeting transcripts to maintain a "Knowledge Density" of over 12,000 nodes for high-precision decision support.

2. MCP Server Integration: Unlike traditional product management tools that stop at documentation, Radiq features a native Model Context Protocol (MCP) server. This allows product requirements, evidence, and context to be pulled directly into modern AI-powered IDEs such as Cursor, VS Code, and Windsurf. Developers can access the full PRD context, branch information, and Jira ticket data through CLI commands (e.g., radiq create-ticket), ensuring the "source of truth" is embedded within the coding environment.

3. Automated Evidence-Based Roadmapping: The platform features a proprietary RICE scoring engine (Reach, Impact, Confidence, Effort) that isn't just manually entered but derived from data. It tracks mention counts across Slack threads, meeting frequency, and Jira upvotes to quantify the weight of a feature request. This leads to a "Routing Confidence" score, ensuring that every item on the roadmap is backed by verifiable customer evidence rather than intuition.

4. Omni-Channel Meeting Intel: Radiq acts as an intelligent recording layer for both online (Zoom, Teams) and offline (in-person) meetings. Similar to advanced transcription tools but optimized for product workflows, it extracts tasks, auto-creates Jira tickets, and links specific audio/text segments as "evidence" to feature decisions, ensuring that the "why" behind a requirement is never lost.

Problems Solved

1. Pain Point: Signal Fragmentation and Context Stitching. PMs currently waste hours "stitching" information across disconnected tools like Slack and Confluence. Radiq solves this by centralizing raw signals into a single "Product Pipeline" where evidence is automatically linked to tickets.

2. Target Audience: The platform is built for Product Managers (PMs), Technical Product Managers (TPMs), Engineering Managers, and Product-led Founders. It is particularly valuable for teams using AI-assisted coding workflows (Cursor/Windsurf users) who require high-context prompts to generate accurate code.

3. Use Cases:

• Transforming a chaotic Slack thread about "slow PDF exports" into a high-confidence Jira ticket with p95 latency targets and acceptance criteria.
• Prioritizing a backlog of 100+ tickets by cross-referencing them against recent customer meeting transcripts to find the highest impact features.
• Syncing product requirements directly to a developer’s branch to minimize back-and-forth alignment meetings.

Unique Advantages

1. Differentiation: Traditional tools like Jira or Linear are passive databases of record. Radiq is an active intelligence layer. While competitors focus on task management, Radiq focuses on decision intelligence—quantifying the "Confidence" and "Impact" of a move before a single line of code is written. It transitions the PM role from a "writer of specs" to a "validator of outcomes."

2. Key Innovation: The integration of the Model Context Protocol (MCP). By treating the PRD as a live data source for the IDE, Radiq eliminates the "telephone game" between product and engineering. This represents a paradigm shift toward "Autonomous Product Management," where the documentation is machine-readable and actionable for both LLMs and human developers.

Frequently Asked Questions (FAQ)

1. How does Radiq automate Jira ticket creation? Radiq uses its PRD Builder agent to analyze meeting transcripts and Slack feedback. It then generates structured tickets with technical acceptance criteria, RICE scores, and linked evidence, and pushes them directly to Jira via API. Developers can then initialize these tickets in their IDE using the Radiq CLI.

2. What is the benefit of the MCP server for developers? The MCP server allows developers using Cursor or VS Code to query the PRD context without leaving their editor. This ensures that the AI coding assistant has the most up-to-date product requirements, reducing bugs caused by outdated specifications or missing context.

3. How does the RICE scoring engine gather data? The engine performs a multi-channel analysis, counting unique user mentions in Slack, recurring themes in Zoom recordings, and historical data in Jira. It calculates "Confidence" based on the volume and density of these signals across your entire workspace, providing a mathematical basis for prioritization.

4. Can Radiq record in-person meetings? Yes, Radiq includes a meeting recorder capability designed for both online and offline (mobile/laptop) use. This allows PMs to capture customer insights from coffee shops, conferences, or office whiteboarding sessions and immediately turn them into structured product signals.