Strategy, launches, competitive intelligence, customer insight, operations, releases, sprint execution, field support signals — Qeplar connects every domain your product team touches into a single operating surface. You see everything. You miss nothing.
The information exists. It’s just scattered across tools, inboxes, and people who haven’t updated their spreadsheet this week. By the time it reaches you, the window to act has already closed.
You ask this in a meeting. Three people give three different answers. Nobody mentions the critical blocker that’s been sitting unresolved for six weeks. Support knows. Engineering knows. But it never reached your desk. Qeplar gives you a real-time readiness grade, predicted slip, and revenue exposure — no meetings required.
A competitor launched. A key account restructured. Three customers reported the same pain point. Your field support team saw it all — but the intel landed in a Slack thread, a ticket comment, and a survey nobody reads. Qeplar connects every signal source into a single intelligence layer that surfaces what you need to see before it’s too late.
Sprints complete. Tasks move to done. But does any of it connect to strategic goals, revenue targets, or competitive position? Qeplar ties every task, priority, and sprint item back to the strategy that justified it — and grades the execution chain A through F so you see which work is moving the needle and which is just motion.
Every layer of your product organization — from the goals your board sets to the field test your support team just ran. Everything talks to everything. One login. One source of truth.
See the complete chain from boardroom goal to daily execution. Goals cascade into priorities, priorities link to products, execution gets graded A–F in real time.
Every priority graded automatically from six weighted factors. Stale work gets flagged. Deadlines approaching get escalated. No status meetings needed.
A 7-phase launch pipeline with weighted readiness scoring. Predictive slip engine. Revenue exposure mapped to every blocker. One-click Exec Summary for the board.
Record every customer interaction. Highlight text, classify it as a pain point, feature request, or competitive mention. Auto-creates backlog items, priorities, and competitive signals. Three customers report the same pain? Qeplar tells you.
Your morning briefing. AI-generated summary of what changed overnight, what needs attention today, and what will go wrong tomorrow if nothing changes. Pin insights. Share to Teams. Type / to act.
Customer needs drive backlog items. Backlog items fill sprints. Sprints connect to priorities. Your PMs manage execution without leaving the platform. Kanban, timeline, and blocked-item tracking built in.
Track every product version, bug fix, improvement, and hotfix. See cross-product ecosystem releases where applications depend on each other. Know what shipped, what broke, and what’s next.
Track competitor activity. Surface competitive signals from customer conversations automatically. When a competitor launches, Qeplar generates a counter-strategy priority linked to the affected product.
Account health alongside product decisions. VoC analysis, purchase drivers, objection tracking. Understand which accounts drive revenue and what they need from your next release.
Real roadmaps for real conversations with the board. Gantt timeline per product. Now/Next/Later swim lanes from your goals hierarchy. Show progress, not promises.
Demo floor management with uptime monitoring. Event coordination with resource allocation. Plant surveys documenting how customers actually work. Sales readiness tracking. Enablement assets in one place — not scattered across hard drives and email.
Jira issues become blockers. GitHub releases trigger readiness updates. Salesforce accounts surface in your intelligence layer. SAP ERP shows real-time product performance. Teams and Slack receive proactive alerts. Data flows in. Intelligence flows out.
Every feature below is built and shipping today.
Not a chatbot. Not a copilot. A room full of strategists who talk to each other about your data — then present you with what they agreed on, where they disagreed, and what they think you should do next. They convene real meetings. They challenge each other’s assumptions. They produce minutes you can read, recommendations you can approve with one click, and forecasts that update as reality changes. Behind the meetings, a separate layer of agents watches everything continuously — and a third layer reads the market so you don’t have to.
30 days later, Qeplar asks: “Did that recommendation work?” Your answer feeds back into the system. Over months and years, the agents learn which calls produce results for your organization specifically — not generic best practices, but patterns validated against your outcomes. Year 1 is useful. Year 5 is indispensable. Year 10 is irreplaceable.
Hubble — the companion live support platform — sends real-time field signals directly into Qeplar via a secure, HMAC-signed bridge. Ticket patterns, escalation trends, support costs, field test results, CSAT scores, adoption friction, customer sentiment. Every signal gets matched to a product and weighted by account tier.
When three enterprise customers report the same issue in a week, Qeplar surfaces it before your next sprint planning. When a field test fails, a blocker is auto-created. When support costs rise, your readiness score adjusts. The loop between field reality and product decisions is closed — automatically.
Support ticket escalated for Sonar Array S12 — firmware stability issue reported by 3 enterprise accounts this week.
Knowledge gap detected: “How to configure batch export” surfaced 42 times in 30 days across 6 support agents.
Automated field test passed for QA1200 v3.2.1 — all 47 test cases green. Launch readiness score updated.
CSAT trending down for Meridian Dynamics (enterprise) — 3 sessions rated below 3/5 in the last 14 days. Account health warning emitted.
Your product roadmap is your competitive advantage. Your customer interviews are proprietary intelligence. Your competitive positioning is the result of years of market knowledge. Every cloud product tool on the market processes all of it on shared infrastructure you don’t control.
Qeplar runs on your hardware. Your database. Your AI. Zero ports open on your firewall. Your team accesses it from anywhere in the world through encrypted mesh networking. No VPN. No cloud. No compromise.
Every recommendation that gets validated, every forecast that gets checked, every decision that gets tracked — Qeplar learns what works for your organization specifically. Not generic best practices. Your patterns. Your playbook. Your institutional memory.
Morning briefings, launch readiness grades, competitive alerts, sprint health. The platform does your job before you open your calendar. Immediate value from day one.
Five years of validated forecasts reveal which strategies actually produce results. The system knows that your Q4 launches always slip 2 weeks, that competitive responses within 48 hours retain accounts 3x better. Patterns no human would track.
A decade of organizational intelligence survives every personnel change. When your VP of Product retires, their strategic DNA stays in the system. A new hire opens Qeplar on day one and sees what took 10 years to learn. No competitor can replicate that by switching tools.
Every decision, every validated recommendation, every strategic pattern — preserved for years. When someone leaves, their institutional knowledge stays. A new VP on day one sees 8 years of strategic intelligence that took 8 years to accumulate.
Three operating modes: Operations (full detail for PMs), Management (priorities, launches, team health), and Executive (portfolio grade, revenue exposure, one screen). Each user sees exactly what their role needs.
Agent recommendations come with smart action buttons: schedule a meeting with a pre-built calendar invite, draft a competitive analysis document, research a competitor with live market data. One click from insight to action.
“I opened Qeplar on a Monday morning and it had already detected a competitor launch over the weekend, convened six AI agents to assess the impact on three of our product lines, and had action items waiting for my approval. On our own server. No cloud API touched our data. No analyst stayed up Sunday night. The platform just did it.”VP of Product Strategy — Global Aerospace & Defense Company
“We evaluated Productboard and Aha!. Both required our product roadmap, customer interviews, and competitive positioning to live in their cloud. Legal stopped that conversation in the first meeting. Qeplar does more than both of them combined — and it runs on our own servers.”Director of Product Strategy — Global Medical Devices Company
Every deployment is different. Your team size, integration needs, AI hardware, and support requirements shape the right plan. We’ll walk through it together after you see the platform in action.
We’ll walk through your specific use case — launch readiness, intelligence, team operations, or all of it. Live demo on actual hardware with your questions answered in real time.
No — and that’s intentional. Qeplar operates at the strategy and launch intelligence layer above task management. It connects to Jira, GitHub, and Azure DevOps, pulling signals from your existing tools. Your developers keep their workflow. What changes is that product leaders and executives can finally see what all that work means for launch readiness, revenue, and competitive position — without chasing status in Slack.
Productboard helps you decide what to build. Aha! manages roadmaps. Qeplar ensures the product actually launches successfully — and gives you visibility into the complete chain from strategy to field reality. Launch pipeline, readiness scoring, revenue at risk, predictive slip, field intelligence from support, executive accountability, competitive response, sprint execution, release tracking, customer research, operations. And unlike all of them, Qeplar runs on your hardware. Your roadmap and competitive intelligence never leave your building.
Most teams see meaningful output in the first week. Day one: import your products, priorities, and launch data. Day two: the AI morning briefing starts reflecting your actual state. Week one: the Agent Council runs its first deliberation. Week two: predictive slip is tracking your launches and you have your first exec-ready readiness report — without building a single slide.
One afternoon. Provision a server meeting our sizing guide. Install Docker. Run the compose file we provide. After that, IT has nothing to maintain — Qeplar is self-contained. Updates are a single pull-and-restart command your team can run on their schedule. Remote access works through encrypted mesh networking — no VPN, no inbound ports, no firewall changes.
Everything runs on your hardware using local inference. Your data never leaves your server. The Agent Council reads your products, priorities, launches, VoC data, and integration signals — all stored in your on-premises database — and generates recommendations grounded in that context. Nothing is sent to OpenAI, Anthropic, Google, or any external service unless you explicitly configure it.
Yes. Pricing is tier-based — Starter (25), Business (100), Enterprise (250), and unlimited site license. Upgrading is a license change, not a migration. Your data, configuration, and AI model stay on the same hardware. No cloud tenant to move between. No export/import. No retraining.
Hubble is a companion live support platform that connects field intelligence to Qeplar via a secure bridge. It’s not required — Qeplar delivers full value without it. But for organizations where product teams and support teams need closed-loop visibility — ticket patterns feeding readiness scores, blocker resolutions pushing back to open tickets, launch forecasts informing support staffing — the bridge is what makes that happen. Both platforms run on your infrastructure.
They’re looking at one piece of it. Qeplar has 218 database tables, 86 API route phases, 60+ pages of product surface, 14 AI agents across three tiers — six Council strategists, five background monitors, and three external web scanners — plus a 6-phase intelligence SAGA, 8 integration connectors, and a decade of product management domain knowledge baked into the data model. A team of engineers could build a priority tracker in a month. They cannot build a complete product operating system with predictive launch intelligence, competitive response automation, field support signal processing, and an AI council — and maintain it — while also shipping their actual product.
