I’m building LinksRF to make shared-link traffic easier to understand
I'm building LinksRF, a bootstrapped platform for creating, sharing, and understanding campaign links.
The idea came from a simple problem: a "view" does not always mean a person actually visited.
A link may be requested by a real visitor, an AI agent, social preview generator, search crawler, security scanner, monitoring service, or automated script. When everything is combined into a single click count, it becomes difficult to understand what genuinely happened.
LinksRF tries to make that traffic more explainable by separating:
- Likely human visitors
- Confirmed behavioral engagement
- Social and messaging previews
- Verified crawlers and bots
- AI and automated agents
- Security scanners
- Suspicious or repeated scripted traffic
- Raw requests that cannot yet be confidently classified
Alongside traffic-quality analytics, LinksRF includes:
- Short and readable campaign links
- Branded custom domains
- UTM campaign tracking
- Shareable boards and resource collections
- Bio pages and focused funnels
- Sources, devices, locations, and returning-visitor insights
Teams, workspaces, and campaign management
The goal is not to claim that every visitor can be identified perfectly. It is to provide better evidence than a single raw number and clearly communicate when the system is uncertain.
LinksRF is still early, bootstrapped, and launching on Product Hunt this Monday.
I'd really appreciate honest feedback:
When reviewing link analytics, what would help you trust that the traffic represented genuine interest rather than previews or automation?
This solves a real problem that exists in the gap between "we got clicks" and "we actually understand if those clicks matter." Most founders and marketing teams optimize for vanity metrics because they don't have access to the signal underneath.
The bet I'd validate: is this painful enough for enough teams to become their own analytics layer? Or does this end up as a feature request inside platforms that have distribution lock-in already?
The early signal will be which segment adopts first - growth teams tired of ambiguity, or content creators wanting to understand their audience. That'll tell you where the real pain is.
The interesting shift is moving from "how many clicks did I get?" to "what kind of traffic actually happened?"
What would convince you that traffic quality classification is painful enough for teams to adopt a dedicated product, rather than expecting it as a feature inside existing analytics platforms?
I like that you are showing uncertainty instead of pretending every request can be classified perfectly. That would make me trust the numbers more. The useful view for me would be the evidence behind each bucket, not just the label. For example, why was a visit marked as likely human and what behavior moved it into confirmed engagement? Are you planning to let users inspect that reasoning when a campaign result looks unusual?