Rezonant - Talk, spec, ship: get your product ideas into production
Rezonant helps product teams turn messy ideas into code-ready specs, tickets, and engineering tasks. Collaborate with PMs, engineers, designers, and AI agents in one shared workspace. Ground decisions in your actual codebase, keep everyone aligned on the same version, and create work that humans and coding agents can confidently ship.
Replies
The Prohuman AI
@abi_church Congratulations on the launch
Rezonant
@abi_church @hasatoor Thanks Hasan!! really appreciate it :)
@hasatoor thanks Hasan, appreciate the support!
for me the move is the google chrome extension, then using that to screenshare and narrate directly into Rezonant to get that turned into tickets and specs. Loving it so far, like a vibe-product platform :)
Thanks @jaryd_hermann1 - great to hear you're enjoying it! Vibe-product product platform sums it up 👌
Rezonant
@jaryd_hermann1 thanks jaryd!
"Turn messy ideas into structured specs" is a deceptively hard problem because the messiness is where domain knowledge lives — strip too much of it out and the spec becomes generic; leave it in and the agent gets confused. I work on ModeLoop in financial modeling and the same pattern shows up there: the difference between a model an agent can build and one a human can defend is usually in the assumptions, not the cells. Curious how you've handled assumption capture in Rezonant.
Rezonant
@samir_asadov hey -- that's a great point and question!
Managing context and feeding the agent with the right context at the right time is indeed a hard problem to solve. And because it's hard, it's the main differentiator of Rezonant vs custom-built workflows on top of Claude Code/Cursor/Codex with GitHub repos and .MD files to manage context.
The way Rezonant solves that is by capturing both technical (codebase) context from GitHub and additional product context from Granola, docs, Figma etc. These are then organized in teams so that each agent and member in the workspace can access the right context at the right time.
Hope that makes sense?
@vincenzo_bianco2 Makes sense — and worth noting that "context at the right time" is the part most agent products underestimate. You can have all the right context loaded and still ship the wrong output if the agent gets it at step 3 when it needed it at step 1. The differentiation usually isn't context window size; it's the orchestration that decides what gets surfaced when. Curious how you're handling the failure modes where the user provides context that the agent technically has but doesn't weight properly — that one always seems to be where the trust breaks.
Is this for beginner teams that don’t have business analysts? What you described is usually done by a business analyst. Also, now everyone uses Claude Code, Claude Design, Figma, and other tools, so often you need not just a comment but specifically a comment in, for example, Figma, so that Claude Code can later understand it and apply the change. So at the current stage of AI development, I can’t imagine how to do this without a human.
Rezonant
@natalia_iankovych thanks for the question, natalia :)
We actually do work with some larger teams with business analysts! The value for them is typically to make sure that all the work items that are sent to Engineering (in the form of JIRA or Linear tickets) are grounded in technical reality and include technical context. This facilitates eng teams working with Claude Code and reduces back-and-forth between eng and business analysts to gather feedback.
Re: your point of commenting in Figma so the context is accessible in Claude -- we're launching our own MCP server so that Rezonant comments will be accessible to Claude too!
Rezonant
@natalia_iankovych stay tuned for the MCP release :)
Spec-to-code via voice is a workflow I didn't know I wanted until now. Product teams burn so much time translating verbal ideas into structured tasks. We've been building in the ops-heavy SaaS space, and this kind of frictionless spec creation is something PMs ask about constantly. How does Rezonant handle ambiguity in voice input, does it ask clarifying questions?
@shivam_jaiswal36 Totally, so much can get lost in translation. Rezonant understands your product and codebase, and it'll ask clarifying questions or flag missing/ambiguous requirements before anything gets sent to your coding agents. Hope that answers your question?
Fruitful
We're drowning in 'quick ideas'/proof of concepts/unclear specs - great to have something that can streamline and get the right context all together!
Rezonant
@henryforshort thanks! very keen to hear your feedback once you had the chance to try it out :)
@henryforshort Glad to hear you're finding it useful, and hope you drown a little less! 🏄
Revolte
Congrats on the launch!
ChatGPT-vs-Rezonant comparison on the landing page is the best pitch I've seen this week, where Rezonant pulls the actual src/services/integrations/ path instead of giving a generic playbook.
We're building in the SDLC execution space at Revolte (the agent runs from spec through deploy), so the spec-quality problem hits us directly downstream. Curious how you're handling the codebase index — persistent semantic index per repo, or retrieval at refinement time? We went persistent and the freshness problem is harder than I expected.
Thanks @rajagopalanar ! Definitely, context makes all the difference. On your codebase question, it's technically both. We run code research at refinement time, which solves the freshness problem, but only when the persistent index isn't enough. What was it in particular about persistent that was harder than you thought?
Revolte
@abi_church yeah hybrid definitely seems promising. The tricky part for us was when someone changes a function signature, every file that imported it has a stale embedding and nothing in the commit tells you which ones. We ended up building a small call graph just to track what to re-embed.
Does your refinement-time retrieval usually catch that or do you handle it on the index side?
Stripo.email
Congrats on the launch! Very timely idea. More teams are building with AI coding agents now, but the messy part is still aligning product context, specs, and execution. Rezonant seems to tackle exactly that gap. Love the workflow-first approach.
Rezonant
@alina_tyslenok_ thanks alina! :) how's your team currently filling that gap? keen to hear how Rezonant will help solving that problem!!
Grounding specs in the actual codebase is the real differentiator here, but codebases move every day — does the grounding re-run, or is a spec a point-in-time snapshot that quietly goes stale the moment someone merges a refactor? The failure mode I'd watch for is a confident spec citing a service or path that got renamed last week.
The “talk to PRD to coding agents” flow is the interesting part for me. Where do you draw the line between a product spec and an implementation plan?
I’d be curious whether Rezonant keeps decisions, assumptions, and rejected options as first-class context when handing work to coding agents, or whether it mostly generates a clean final PRD.