Trust & Transparency
Mizaan is built on the belief that AI scoring systems must be transparent, explainable, and ultimately accountable to the humans they serve. Here's how we make that real.
Privacy-First Architecture
Mizaan runs locally by default using Ollama. Your work items, scores, and feedback never leave your infrastructure unless you explicitly choose a cloud LLM provider. We believe the most secure data is data that never travels.
- •Local-first with Ollama — zero data transmission by default
- •No telemetry or analytics on scoring data
- •All data stored in your own PostgreSQL instance
- •Cloud providers are opt-in, never default
Explainable Reasoning
Every score comes with detailed reasoning explaining exactly how the AI arrived at its assessment. You can inspect dimension-by-dimension breakdowns, confidence levels, and the specific rubric criteria applied.
- •Full reasoning chain for every score
- •Dimension-by-dimension breakdown (impact, effort, quality)
- •Confidence level indicating AI certainty
- •Similar historical cases shown for context
Human Authority
Humans always have the final say. Any AI score can be overridden with reasoning, and those overrides become part of the learning system. The AI adapts to your judgment — not the other way around.
- •Override any score at any time
- •Human reasoning is stored and used for future learning
- •The system learns from your expertise, not just algorithms
- •Escalation paths for disagreements between agents
Complete Audit Trail
Every scoring event, override, and learning update is logged with timestamps, actor IDs, and full context. Built for compliance requirements and internal review processes.
- •Immutable log of all scoring decisions
- •Human override history with reasoning
- •Version-controlled rubric changes
- •Exportable audit reports
No Black Boxes
Mizaan uses Retrieval-Augmented Generation (RAG), not fine-tuning. This means the learning mechanism is transparent — you can see exactly which past cases influenced a score, and the model weights are never modified.
- •RAG-based learning — no opaque model changes
- •Viewable few-shot examples injected into each prompt
- •No hidden training on your data
- •Open architecture — inspect any step of the pipeline
Feedback-Driven Improvement
The system improves through human feedback, not autonomous learning. Every improvement is traceable back to a specific human decision, making the system accountable to the people it serves.
- •Pattern detection surfaces recurring override themes
- •Rubric suggestions generated from feedback analysis
- •All improvements require human approval
- •Transparent feedback loop visible to all stakeholders