About Qmmit
Building the intelligence layer
for AI-native development.
We believe the next decade of software will be built by developers working alongside AI. Qmmit provides the infrastructure to make that collaboration visible, measurable, and verifiable.
Our Mission
Make AI-assisted development transparent.
Every day, millions of developers use AI tools to write code. The code ships. The commits land. But the intelligence behind those commits — the prompts, the model choices, the iterative thinking — disappears the moment the terminal closes.
Qmmit captures that intelligence. We link every AI interaction to the code it produced, creating a verified record of how software is actually built in the AI era. Not self-reported. Not estimated. Verified against real git commits.
Our goal is simple: give developers credit for how they think, not just what they ship.
What We Do
Three products. One platform.
Prompt Tracking
Automatic capture of AI interactions from 7 coding tools. Zero workflow changes. Invisible git hooks handle everything.
AI Spend Intelligence
Unified view of token consumption, model costs, and tool usage across your entire development workflow. Know where your AI budget goes.
Verified Portfolios
Every prompt linked to a real commit SHA. Cryptographically verifiable. A developer profile that proves how you build with AI.
How It Works
Invisible by design.
Qmmit integrates at the git layer. A lightweight CLI installs hooks that fire on every commit and push. These hooks read session data that your AI tools already store locally, match interactions to commits using our proprietary scoring engine, and sync verified data to your profile.
Compatibility
Works with the tools you already use.
Qmmit supports the 7 most widely used AI coding tools. No plugins required. No API keys to configure. If the tool saves session data locally, Qmmit can read it.
Privacy
Your prompts are more sensitive than your code.
We treat them that way. Qmmit is local-first by architecture. Nothing leaves your machine without your explicit action. Secrets are detected and redacted automatically. You control what becomes public, what stays private, and what gets deleted.
Vision
Where we are going.
Qmmit is evolving from a visibility platform into an AI-powered optimization engine. We are building the world's first reinforcement learning system for developer-AI interaction — using paired prompt-outcome data to make every developer measurably better at working with AI.
Visibility & Intelligence
Unified analytics across all AI tools. Token spend tracking, model performance comparison, and workflow drift detection. The observability layer for AI-assisted development.
Prompt Quality Scoring
Machine learning models trained on millions of prompt-outcome pairs score every prompt on effectiveness. Developers see real-time feedback on prompt quality — specificity, context richness, and predicted success rate.
AI-Powered Prompt Optimization
Reinforcement learning from code outcomes (RLCO). Our offline RL pipeline uses Decision Transformers and Conservative Q-Learning to learn optimal prompting strategies from historical data. The system suggests improved prompts that maximize code quality while minimizing token consumption.
Intelligent Model Routing
Contextual bandits that learn which AI model performs best for each task type in your specific codebase. Thompson Sampling with contextual features routes tasks to the optimal model — reducing cost and improving output quality simultaneously.
Personalized Learning & Adaptation
RLHF-inspired personalization that adapts to each developer's prompting style. The system clusters developers by interaction patterns and trains per-cluster reward models, delivering suggestions calibrated to individual workflows.
On-Chain Verification & The Developer Graph
Cryptographic attestations anchored on-chain via EAS. Every AI contribution becomes independently verifiable without trusting any platform. Soulbound credentials for AI skill milestones. The global reputation layer for AI-native builders.
AI Research
The optimization engine.
Qmmit captures what no other platform has: paired prompt-outcome data at scale. Every prompt is linked to the code it produced, whether that code shipped, and how efficiently it was generated. This creates a natural reinforcement learning environment where the action is the prompt and the reward is production-quality code.
Reward Modeling
Supervised models trained on prompt-outcome pairs predict prompt effectiveness before execution. Developers get quality scores and improvement suggestions in real-time.
Offline Reinforcement Learning
Decision Transformers and Conservative Q-Learning trained on historical trajectories learn optimal prompting policies without requiring online experimentation.
Contextual Model Selection
Multi-armed bandits with contextual features learn which model excels at which task type — routing requests to minimize cost and maximize output quality.
Prompt Chain Optimization
Sequential decision modeling over multi-prompt sessions. The system predicts optimal chain length, suggests next prompts, and identifies when code is ready to ship.
Codebase-Aware Generation
Retrieval-augmented prompt construction using code embeddings. The system injects relevant file context and architectural patterns into prompts automatically.
Token Efficiency Engine
Prompt compression, context window management, and cost-per-outcome optimization. Reduce AI spend by 30-50% while maintaining or improving output quality.
The data flywheel: more developers → more prompt-outcome pairs → better optimization models → more effective developers → more developers.
Principles
What we believe.
Start building your verified AI portfolio.
One command. Zero config. Works with the tools you already use.