Qeplar is a sovereign product operating system. Specialist AI agents cover every domain of your product org — strategy, delivery, revenue, operations, go-to-market, support. They observe your data, deliberate in real meetings, execute the work humans don’t need to, and prove their own accuracy every quarter. It refuses to pile work on overloaded teams. It retires stale priorities. It learns what works for your org — for years. On your server. Not the cloud.
Six continuous stages. No one presses start. The loop is always running. Observe, check capacity, act, trade off, sunset what’s stale, learn from the outcome. Then do it again, with more memory than last time.
Specialist agents watch your launches, priorities, sprints, VoC, competitors, field support, and market news. Every signal is scored by relevance to each user and stored with provenance.
Before creating work, the capacity engine checks historical execution hours, current load, team velocity. If a team is over capacity, the system proposes trade-offs instead of piling on.
Competitive research, VoC summarization, changelog drafting, meeting minutes, alignment scoring, release notes, Hubble signal correlation — Qeplar does it before you arrive.
Recommendations appear inline where you work — never as a separate panel. Accept, defer, or dismiss each one. Every decision is logged with reasoning, so the reason survives personnel changes.
Priorities with zero progress for 60+ days surface as sunset proposals. Management reviews the facts and decides: sunset, defer, or reactivate. Nothing rots silently in the backlog anymore.
30, 60, 90 days, 1 year, 3 years later, Qeplar asks “did that work?” Agents that are accurate get more weight. Agents that miss get less. The system proves it’s getting smarter.
Your customer interviews are proprietary intelligence. Your competitive positioning took years to build. Your roadmap is the work product of every strategic decision your organization has made. Every cloud product tool processes all of it on shared infrastructure you don’t control — and none of them can tell you with certainty whether your data trains their next AI model.
Qeplar runs on your hardware. Your database. Your local LLM — Ollama with qwen2.5, gemma, or whatever model passes your security review. Zero ports open. Runs fully disconnected by default. Approved for defense, aerospace, medical, and regulated industries on day one.
Specialist agents across three tiers. The Board deliberates in real meetings. Background watchers never sleep. External scanners read the market for you. They argue. They revise. They converge — or they tell you where they didn’t.
Each agent has a defined persona, domain scope, and color. Your admin can edit any agent, create new ones for your industry (Regulatory, Security, Compliance), test against live data before activating, and watch their accuracy scores accumulate over time.
Every recommendation gets validated at seven cadences. Every forecast gets compared to reality. Every agent accumulates a track record. You don’t have to trust that the AI is getting smarter — you see the numbers, per agent, per domain, per quarter.
Most buyers recover the annual license in the first six to eight weeks — in PM hours saved, executive time reclaimed, and revenue retained from early competitive signals.
Hubble — the companion live support platform — sends real-time field signals into Qeplar via HMAC-signed bridge. Ticket patterns, escalations, CSAT, adoption friction, field test results, support cost trends. Three customers report the same issue? Qeplar knows before your sprint planning.
Signals flow both ways. When a blocker resolves in Qeplar, Hubble techs see the resolution on related tickets. When a release ships, the changelog pushes to Hubble automatically. Launch phase transitions forecast support load. The loop between product decisions and field reality is closed — automatically.
Firmware stability issue on Sonar Array S12 — 3 enterprise accounts this week. Auto-flagged as launch blocker.
“How to configure batch export” surfaced 42 times in 30 days across 6 agents. Feature request created.
Blocker resolved for QA1200 v3.2.1 — Qeplar pushed resolution note to 7 related Hubble tickets automatically.
Launch confirmed Oct 15. Qeplar pushed predicted 35–50% ticket increase to Hubble staffing six weeks ahead.
Token-authenticated view for end customers, channel partners, and dealers into the launches that affect them. Phase pipeline, release timeline, known issues, enablement assets. Scoped per account. No internal data exposed — no priorities, grades, revenue, or competitive intel.
Every one of these is built and running in production today. None of them are roadmap items.
We’re not a “Productboard alternative.” We’re what comes after tools that require your strategy to live in someone else’s cloud.
| Capability | Productboard | Aha! | Jira PD | ChatGPT for PM | Qeplar |
|---|---|---|---|---|---|
| Runs on your hardware | × | × | × | × | ✓ |
| Your data never leaves your building | × | × | × | × | ✓ |
| Autonomous execution (overnight work) | × | × | × | × | ✓ |
| Capacity-aware (refuses overload) | × | × | × | × | ✓ |
| Auto-sunset stale work | × | × | × | × | ✓ |
| Validates own accuracy (quarterly) | × | × | × | × | ✓ |
| Multi-agent deliberation (not single LLM) | × | × | × | × | ✓ |
| Launch readiness scoring | × | partial | × | × | ✓ |
| Predictive slip with revenue at risk | × | × | × | × | ✓ |
| Strategic direction versioning | × | × | × | × | ✓ |
| Field intelligence bridge | × | × | × | × | ✓ |
| Customer-facing launch portal | × | partial | × | × | ✓ |
| Integrates with your existing tools | ✓ | ✓ | ✓ | × | ✓ |
| Roadmap & goals hierarchy | ✓ | ✓ | ✓ | × | ✓ |
Qeplar doesn’t promise it gets smarter. It proves it. Validated recommendations accumulate. Agent track records grow. Organizational patterns emerge. When someone leaves, their decisions, knowledge artifacts, and strategic DNA stay in the system.
Morning briefings, launch readiness grades, competitive alerts, sprint health, autonomous overnight work. The platform does your job before you open your calendar.
Five years of validated forecasts reveal which strategies produce results. The system knows your Q4 launches always slip two weeks, that competitive responses within 48 hours retain accounts 3× better. Per-agent, per-domain accuracy baselines you can bet on.
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 inherits what took ten years to learn. No competitor can replicate that by switching tools.
“I opened Qeplar on a Monday and it had already detected a competitor launch over the weekend, convened six agents, assessed impact on three product lines, and blocked an overload allocation my PM tried to push through. On our own server. No cloud API touched our data. No analyst stayed up Sunday. 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 in the first meeting. Qeplar does more than both combined — and the AI proves its own accuracy. Our board has never been able to ask those kinds of questions before.”Director of Product Strategy — Global Medical Devices Company
Every deployment is different. Team size, integration needs, AI hardware, support requirements. We’ll walk through it together after you see the platform in action.
We’ll walk through the full platform on real hardware with your questions answered in real time. Launch readiness, agent deliberation, capacity blocking, validation pipeline — all live.
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 — and the AI does the overnight work humans shouldn’t have to.
Productboard helps you decide what to build. Aha! manages roadmaps. Neither is autonomous. Neither checks capacity. Neither validates its own accuracy. Neither runs on your hardware. Qeplar is the next generation — a product operating system where specialist AI agents across every product domain observe, deliberate, act on your data, and prove their accuracy every quarter. And unlike both of them, your roadmap and competitive intelligence never leave your building.
Every recommendation Qeplar makes gets validated at seven cadences: 30 days, 60 days, 90 days, 6 months, 1 year, 2 years, 3 years. You rate whether it worked (or Qeplar infers it implicitly from outcomes). Scores roll up per agent, per domain, per LLM model. Inaccurate agents get weighted down in future synthesis. The impact dashboard shows hard numbers: accuracy % rolling 12 months, hours saved by autonomous execution, revenue retained from early competitive response. Every number is traceable back to specific recommendations. No vanity metrics.
Every team and person in Qeplar has a measured capacity — derived from 40h available minus meetings minus context switch time, adjusted by historical execution velocity. Before a new priority is assigned, Qeplar checks if the team is at or over capacity. If over, it proposes trade-offs: which priorities to defer, which to reassign, which to accept as a slip. Your PMs can’t blind-dump work anymore. Your people don’t burn out silently.
Everything runs on your hardware using local inference via Ollama. Your data never leaves your server. The Board 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 enable external intelligence scanning (Serper/Gemini) — and even that can be toggled off for fully disconnected operation. Defense and aerospace deployments run air-gapped by default.
Day one: import products, priorities, launch data. The morning briefing reflects your actual state by day two. Week one: Board runs its first deliberation and generates its first autonomous work. Week two: predictive slip tracks your launches. Quarter one: first validation cadences fire and agent accuracy starts accumulating. Year one: the platform has enough data on your organization specifically to start surfacing patterns no human would track.
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. Remote access works through encrypted mesh networking — no VPN, no inbound ports, no firewall changes.
They’re looking at one piece of it. Qeplar is a complete product operating system — specialist agents across every domain, a validation pipeline that grades seven cadences out to three years, a capacity engine that blocks overload, a sunset engine that keeps backlogs clean, a forecast engine that corrects itself when it’s wrong, an organizational memory that survives personnel changes. Those all took years to build and years more to prove. A team of engineers could build a priority tracker in a month. They cannot build a sovereign product operating system while also shipping their actual product. And if they could, they’d still have no validated organizational intelligence to show for it on day one.
