Launching today
Radiq
Product intelligence for the autonomous coding era
162 followers
Product intelligence for the autonomous coding era
162 followers
PMs lose hours every week stitching signal from Slack, Jira, Confluence, and meetings. Not deciding. Just chasing context, citing evidence, and rewriting specs engineers question anyway. Radiq runs the customer-to-code loop on autopilot: surface the decision, prioritize by evidence, generate a developer-ready spec grounded in code, and push it into the developer IDE via MCP. What took a week now takes minutes.
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I'm Ritvik, co-founder of Radiq.
I spent years as a PM, working in B2B, B2C, 0 to 1 and Enterprise products, most recently scaling a new product line from $0 to $40M at Flutter Entertainment with a 50-person cross-functional team. Every Monday meant the same loop of hours stitching signal from Slack, calls, support tickets, and meeting notes, then writing specs that engineering came back questioning anyway.
Every PM tool used at scale today was built before AI coding agents existed. We're building Radiq for what comes next — when the artifact PMs produce stops being a document for humans to read and starts being a structured spec an AI agent can execute.
Radiq is the context engine that fixes this. We aren’t just a writing assistant; we automate the customer-to-code loop. We ingest signal across channels, build an intelligent knowledge graph grounded in your technical architecture, and push structured, developer-ready output straight into Cursor or Windsurf via MCP.
We’ve been validating this with design partners who realized that their dev speed increased. Radiq fixes the handoff so the developers get it right the first time.
We’re onboarding our next batch of design partners. If you’re tired of the "Monday Toil" and want to bridge the gap between your roadmap and your codebase, drop a comment below. 🙌
Reach us at myradiq.com — we'll be in the comments all day. Ask us anything 🙌
Radiq
Hey everyone, I'm Devansh, co-founder on the technical side at Radiq.
When Ritvik first walked me through this problem, the part that stuck with me wasn't the wasted hours. It was that PMs are doing this work manually in 2026, the same year coding agents are writing production code. Customer context sits across Slack, Jira, Confluence, meetings, calls, every team's tool stack.
PMs are the human glue holding it together, and even after all that effort, the spec they hand to engineering still doesn't carry the codebase context an agent needs to build the right thing the first time.
So we built the layer that holds it together.
Radiq's core is a knowledge graph that reads across signals AND your codebase. Not vector retrieval, not just clustering, an actual graph where customer evidence maps directly to the modules and dependencies it touches. Getting that to run reliably with real messy data, real customer Slack threads, real codebases, was the hardest and most satisfying thing I've built. It's been four years building production AI at Deloitte, then agentic systems with Citigroup and Enterprise Ireland, plus my own AI agent marketplace, and this one feels like the bet that matters most.
The MCP integration into Cursor, VS Code, and Windsurf is the part I'm most excited about. Specs are the wedge. But the real opportunity is what happens when product context stops living in silos. Every customer signal, every architectural decision, every spec connected, queryable, executable by an AI agent.
We're just getting started. Come talk to us in the comments 🙌
@devanshsingh2199 I walked through this project just now, you guys got an amazing project! And I got a question about konwledage graph, which maps customer evidence directly to the modules and dependencies in codebase, how do you ensure if it is a right mapping? And as far as I konw, claude co-work can intergrate with dashboard such as jira, and automating some simple tasks, but the problem is that the output is inconsistent sometime.
Radiq
@yiling_lei thanks for going deep on this!
Mapping accuracy: We cross-reference customer entities against the actual codebase (AST, dependency graphs, feature flags). If someone says "checkout flow," we find where that logic actually lives ,not just keyword matches. PMs validate the first batch, then those patterns become templates. Stale mappings auto-decay on the next codebase sync.
Consistency: Claude is amazing for exploration. Radiq is for execution. We don't ask an LLM to "write a spec." We generate rigidly structured PRDs grounded in your Jira/Slack/meeting data. When we push to Cursor via MCP, it's structured tasks not free-form text that might drift.
The real shift: Developers never lose context. They just ask Cursor or VS Code to do a task, and the knowledge base handles everything - dependencies, prior decisions, related code. When a PM wants to plan a feature, the system already knows what to build first, what comes next, and whether it's feasible given the current architecture.