MindBacklog

Across every startup I built, the most critical bottleneck wasn\'t shipping code—it was deciding what to build. Legacy tools require active users to function. If you are shaping a concept, they offer nothing but an empty whiteboard. Without compounding evidence mapped across concepts and live features, the loudest voice inevitably wins the roadmap.

The Vision: Build a compounding intelligence layer. I wanted a platform where every signal from competitors and every piece of feedback from users compounds to make the next decision sharper. No starting from zero every session.

I designed a full-stack, distributed architecture to handle the scale of global signal ingestion without compromising UI response times.

• Core Platform (Laravel 11): Manages RBAC, multitenancy, bidirectional Jira sync, REST APIs for external ingress, and a state-of-the-art UI using the Filament framework.
• AI Worker (Python / FastAPI): A standalone async worker that listens to Redis job queues. It executes the heavy lifting: web scraping with Playwright, 4-phase signal classification, and generating memory-persistent vector embeddings using Gemini 1.5 Pro.

Generic AI is amnesiac. If you paste feedback into ChatGPT, you get a decent answer and then lose it all tomorrow.

MindBacklog\'s MIND Engine creates a persistent `product_capability_graph`. Through structured Retrieval-Augmented Generation (RAG) mapped directly to your features and objectives, the AI inherently understands the "soul" of your product. The more signals it ingests from your 8+ channels, the sharper its PRDs, User Stories, and WSJF/RICE scores become.

To solve the “empty whiteboard” problem, I engineered Concept Mode. Before you have a single active user, you can connect competitor App Store URLs and landing pages. Ask Mind automatically mines them for feature gaps, complaints, and competitive advantages—validating concepts against real market data before you write a single line of code. Once you launch, the workspace fluidly transitions into handling live user feedback.

MindBacklog replaces subjective debate with mathematical evidence. Instead of guessing, PMs look at an AI Priority Score driven by literally thousands of cross-referenced market and user signals. By eliminating manual triage and generic chat prompts, the platform allows product leaders to defend their roadmaps with data and focus exclusively on high-leverage strategic decisions.