GoHighLevel has 10,000+ customer requests.
Now we know which ones actually matter.
Automations alone has 3,000+ posts. Across all boards, customers are filing feedback faster than any team can manually read it. Roadmap Radar turns that noise into ranked, grouped, execution-ready intelligence — so every sprint starts with the highest-impact work, not the loudest requests.
Webhook support for automations
PRD readyEnables enterprise workflow integrations — blocking expansion deals.
Custom domain for funnels
Critical for white-label agencies — affects plan upgrades.
SMS reply threading
Affects 30% of conversation workflows — reduces churn.
* No PMs were harmed in the making of this dashboard. Several backlogs were.
GoHighLevel receives thousands of feature requests. None of them are connected to each other.
Ten customers requesting the same feature across ten posts fragments the signal. The true aggregate demand never surfaces. Decisions are made on louder posts, not more-requested ones.
PMs read unstructured text under time pressure and make inconsistent judgment calls. There is no shared scoring model, no structured output, no audit trail.
Features already in the changelog sit as open requests because no one has cross-referenced the two. Time gets spent triaging resolved problems.
The Automations board alone has 3,000+ posts. Reading them is a multi-hour task repeated every sprint. That time never stops compounding.
AI execution tools make this problem worse, not better.
AI coding tools give developers a 2-4x productivity boost. But if those tools are pointed at duplicates, already-shipped work, or low-priority noise, that boost multiplies wasted effort, not value. Speed without direction is just faster mistakes. What is missing is not more execution power. It is a demand intelligence layer that tells the team what is actually worth building.
Three pillars that multiply what AI already gives you.
Roadmap Radar is not another AI coding tool. It sits upstream of execution and tells every tool what to build, and in what order.
Today: Canny. Next: Reddit discussions, YouTube comment threads, support tickets, Pendo analytics, GitHub issues. The pipeline works with any text-based source.
One place to see everything customers are saying, across every channel.
Grouped demand instead of scattered noise. Impact scores from the full data set. PRDs grounded in real customer pain points. Tasks with story point estimates, sprint-ready on arrival.
AI execution speed applied to higher-quality inputs produces better results, not just faster ones.
Fully autonomous AI commits are fast but unreliable. Developers who review a plan before running it catch edge cases, adapt to the real codebase, and own the output.
AI handles the planning. Developers handle the execution. Higher trust. Better code. Faster over time.
A nine-stage intelligence pipeline.
Every Canny post passes through structured AI processing. The result is a ranked, grouped demand model with full context, ready to act on.
Pull posts, comments, and changelogs from Canny across any board.
GPT-4o reads each post and generates a structured summary that captures the core request, including comments.
Two search methods find similar requests: AI similarity matching and keyword overlap. GPT-4o then confirms which pairs are truly the same.
Customer pain points, needs, an impact score from 0 to 10, and business impact are extracted for every request.
Published changelogs are compared against all open requests. Features that are already shipped get flagged automatically.
Quick UI/UX wins are identified and separated. All groups are ranked by combined impact score.
Simple keyword search misses requests phrased differently. Pure AI similarity search misses exact word matches. Combining both, then verifying with GPT-4o, is significantly more accurate than either approach on its own.
From insight to sprint-ready task in two minutes.
One click on any group triggers a four-agent chain. The result lands in ClickUp as an Epic with story-pointed sub-tasks and a Cursor build plan per dev task in GitHub.
Full product requirements document built from real customer pain points and needs
Dev, QA, and Design tasks with story point estimates, sprint-ready
Feature specification file for each development task
Step-by-step Cursor plan with file paths, test instructions, and coding guidance
Full PRD as description. Story-pointed sub-tasks with [dev]/[qa]/[design] labels. QA card auto-created and moved to ready-for-qa.
A step-by-step Cursor plan per dev task, pushed to a dedicated plans repo. The AI never has access to the product codebase.
Dev sees ClickUp comment with GitHub link. Opens plan in Cursor. Reviews and executes. Owns the commit. AI never touches the product codebase.
One place for every signal, from every channel.
The pipeline works with any text-based source. Adding a new one takes hours, not weeks. Every additional source makes the prioritization picture more complete.
Infrastructure provisioned. 4–6 dev days to production.
Common questions.
Developers with better inputs beat developers with more execution power.
Copilot, Codex, Claude Code. They are all making developers faster. The missing piece is knowing which problems are worth solving in the first place, with enough evidence to act confidently.
Roadmap Radar fills that gap. It is built to work alongside any AI execution tool, not replace one. The more sources it ingests, the sharper the signal. The more developers trust the plan, the better the output. Teams with better demand intelligence will pull ahead, and that advantage will grow as execution tools get faster.