The decision layer between customer voice and your roadmap. Signals in. Ranked, explainable epics out.
Obzervify closes the loop from signal capture to execution. It shows the strongest issue, explains why it matters, and recommends what to ship next — then learns from the outcome.
Signals live in support tools, social feeds, roadmaps, dashboards, and engineering logs.
Leaders still make roadmap calls without a transparent evidence chain.
Customer sentiment is fragmented across channels with no unified lens.
Misaligned teams create drift in product, spend, GTM, and executive reporting.
Investors and PMs ask the same question: "how is this different from Pendo, Productboard, Mixpanel, Linear?" Here's the short answer.
Every number on this screen streams from the same brain running the prototype. Switch the view on the left — each one is a different operating mode for the same underlying intelligence.
Each row is a connected source. Each column is one of 48 thirty-minute slices. Color encodes sentiment intensity. The coral spike that starts in Zendesk at −14m propagates through X, Reddit, Intercom, App Store, then Slack — that overlap is where confidence comes from.
External sources are dominant this week, with fintech product dynamics heavily weighted toward payment success and onboarding.
@indiehacker — 2.4k repostsr-8823Each company opens a dedicated page that animates Obzervify block-by-block: ingest → cluster → score → recommend → sync to Jira & Confluence. Every run is wired to real pipeline logic — just backed by demo data.
Real-time connectors across support, social, reviews, dev workflow, and revenue tools. One normalized stream. Every score is traceable back to its source.
The product is defensible because the value is in the closed loop: ingest, understand, prioritize, and operationalize. Each pillar gets stronger as more organizations run their workflow through the system.
Real-time connectors unify social, support, app store, internal collaboration, and dev workflow events into one evidence stream.
Semantic grouping compresses thousands of raw mentions into a handful of actionable themes without losing source traceability.
Reach, impact, confidence, and effort are assembled automatically from customer volume, sentiment intensity, revenue proxies, and engineering velocity.
The system closes the loop by pushing approved clusters into Jira epics and Confluence-ready decision docs.
The rollout starts with signal ingestion and analytics, then layers clustering, prioritization, Atlassian sync, and workflow automation.
Ship the ingestion backbone, normalized signal store, baseline analytics, and a simplified timeline view.
Add the intelligence layer that makes Obzervify sticky: clustering, scoring, epic generation, and explainable recommendations.
Close the loop into Atlassian, Linear, and executive review tooling. Make the workflow production-grade.
Ship enterprise features, multi-tenant scale, and the recommendation feedback loop that makes every org's model better.
Pick a company, watch the brain spin up — from one X post to a signed-off Jira epic with a linked Confluence PRD.
This is what one Obzervify-driven incident looks like, end-to-end. Each line is a real moment in the pipeline. There are no manual hand-offs between them.