MindBacklog

Every AI tool I tried as a product manager had the same flaw. It would write a PRD, a strategy document, user stories, but it knew nothing about my product. I was the context. If I didn't hand-feed the right details into the prompt, I got confident garbage back.

MindBacklog is the inverse: product context is the foundation, and everything the AI produces is grounded in it by default. While building it, I realized the context problem was bigger than documents. A PM's real context is scattered across app reviews, support tickets, Reddit threads, Slack, competitor changelogs, Confluence, and Jira. So ingestion became automatic. The platform reads feedback from wherever it lands, works out what actually matters, and drafts what to build next, with every claim traceable back to real signals.

Every incoming signal runs through a four-phase pipeline: enrichment, quality filtering, classification, and clustering, followed by scoring. Evaluation gates sit between the phases: a relevance gatekeeper, dual-method deduplication, and confidence-thresholded verification, so weak output is caught before a user ever sees it.

Retrieval runs on PostgreSQL with pgvector. Models are chosen per task, with explicit cost, latency, and accuracy trade-offs: Gemini 2.5 Flash Lite for classification, LLaMA 3.3 70B on Groq for synthesis, and GPT-OSS 120B on Groq for chat and merge resolution. The web layer is Laravel; a standalone Python worker runs the AI pipeline off Redis queues.

Ask Mind is a 27-tool agentic co-pilot that lives in a side panel on every page. It reads the product's real data, can act on it, and asks for confirmation before committing any change. It is the same human-on-the-commit rule I apply to the agent I run at S&P Global.

Version one was grounded but shallow. My first fix was large per-task prompt templates. Quality jumped, and so did the cost per run, because every query now carried far more context than it needed. That was the wrong fix, and I had already shipped it. The right fix was architectural: a prompt library the system pulls from at tool-calling time, so the model gets exactly the instruction a task needs and nothing more. Quality went up and cost came down.

The platform also speaks MCP in both directions. It exposes an 18-tool MCP server with bearer-token auth that Claude, ChatGPT, and Cursor can work against, and it consumes context from Confluence, Jira, and Slack the same way.

MindBacklog launched with a three-bucket workflow: Radar, Ideas, Roadmap. The Ideas bucket was the signature design. It was in all the original positioning.

Real usage showed people stalling in the middle. Signals went in, decisions didn't come out, and the pattern held week after week. So eight weeks after launch I deleted the bucket. V2, released May 2026, is a simpler two-stage product: an Intelligence Hub with a daily AI brief and week-over-week signal trending, feeding straight into the Roadmap. It performs better.

There was nobody to escalate that call to, and no way to pretend the data said something else. If I claim the product is evidence-driven and then protect my favourite feature from the evidence, I'm lying.

MindBacklog is live in market under Aureliq Inc., with seven releases in its first two months, including the V2 repositioning. Pricing is deliberately simple: $14.99 per seat, one plan, no feature gates.

The demo is one click, no signup: app.mindbacklog.com/demo