Measuringwhatactuallymatters
Mizaan is a multi-agent AI scoring engine that evaluates work through reasoning, validates through auditing, and learns through human feedback.
90-minute discovery call with Fortune 500 CFO. Discussed pain points, demoed solution, scheduled follow-up.
See it in action
How Mizaan measures what matters
Scoringsystemsweren'tbuiltfortheworkpeopleactuallydo
Opaque Algorithms
Black-box scoring gives you a number with no explanation. Teams lose trust when they can't understand why they got the score they did.
Human Bias
Manual reviews are inconsistent. Different reviewers give different scores for the same work, creating unfairness across teams.
Volume over Impact
Traditional metrics reward activity, not outcomes. Ten quick calls count more than one strategic conversation that closes a deal.
Threeagents.Onefairscore.
Primary Scorer
Analyzes work against your rubric. Produces a score with detailed reasoning and confidence level.
Auditor
Reviews the primary score for consistency. Compares against historical patterns. Flags anomalies.
Human Interface
Presents conflicts to human reviewers. Captures override decisions as feedback for learning.
The system learns from every human decision.
Frominputtoinsightinfivesteps
Input
Submit any work item — a sales call transcript, support ticket, code review, or performance report.
Reasoning
The Primary Scorer analyzes the work against your rubric, retrieving similar past cases to calibrate its judgment.
Validation
The Auditor independently reviews the score for consistency, checking against historical patterns and flagging anomalies.
Resolution
When agents disagree, conflicts are escalated to human reviewers with full reasoning from both sides.
Learning
Every human decision is embedded and stored. The system retrieves relevant feedback to improve future scores — no retraining needed.
Oneengine.Everyteam.
Score sales activities by impact, not volume
- Evaluate calls by strategic depth, not just duration
- Recognize relationship-building over cold outreach volume
- Learn from manager overrides on deal-closing activities
- Compare rep performance with consistent, explainable criteria
Work Item
90-min discovery call with Fortune 500 CFO
Score
8.5
Reasoning
Extended engagement with C-suite decision-maker. Deep discovery of pain points with demo. Strategic relationship-building activity.
AIthatearnsitsauthority
Privacy-First
Run locally with Ollama. Your data never leaves your infrastructure. Zero third-party inference by default.
Explainable Reasoning
Every score comes with detailed reasoning. See exactly why the AI scored the way it did, dimension by dimension.
Human Authority
Humans always have the final say. Override any score with reasoning, and the system learns from your judgment.
Full Audit Trail
Every score, override, and learning event is logged. Complete transparency for compliance and review.
Builtforengineerswhocareaboutthestack
LLM-Agnostic
Swap providers with one line. Ollama, Claude, GPT-4, Azure — same scoring logic, your choice of model.
Agent Orchestration
LangChain + LangGraph for mature multi-agent coordination with built-in tool use and memory management.
Feedback Loops
RAG-based learning via pgvector. Human overrides become few-shot examples — no fine-tuning infrastructure needed.
Secure APIs
FastAPI with async support, rate limiting, and structured validation. Production-ready from day one.
from mizaan import ScoringEngine
from mizaan.providers import OllamaProvider
# Initialize with local LLM — zero cloud dependency
engine = ScoringEngine(
provider=OllamaProvider(model="mistral"),
rubric="sales-performance-v2"
)
# Score a work item
result = engine.score(
item="90-min discovery call with Fortune 500 CFO",
context={"type": "sales_call", "duration": 90}
)
print(result.score) # 8.5
print(result.reasoning) # "Extended engagement with..."
print(result.confidence) # 0.87
print(result.dimensions) # {"impact": 9, "effort": 8, ...}Fromenginetoecosystem
Engine
Core scoring pipeline with Ollama, single rubric, feedback learning, and REST API.
- Three-agent scoring pipeline
- RAG-based feedback learning
- Local LLM with Ollama
- Scoring & feedback API
Platform
Multi-provider support, custom rubrics, team dashboards, and analytics.
- Claude & OpenAI adapters
- Custom rubric builder
- Team analytics dashboard
- Webhook integrations
Ecosystem
Enterprise deployment, marketplace for rubrics, and cross-org benchmarking.
- Azure OpenAI & VPC deployment
- Rubric marketplace
- Cross-organization benchmarking
- Advanced cost optimization
“Every person deserves to have their work measured with the same care they put into doing it.
Mizaan was born from a simple frustration: watching talented people get reduced to numbers that didn't reflect their actual impact. The word "Mizaan" means balance and scale in Arabic — fairness in measurement. Under Mannat AI, we're building tools that bring transparency and fairness to how work is evaluated. Not by removing humans from the loop, but by giving them better tools to make better decisions. This isn't about replacing judgment. It's about augmenting it with reasoning, validation, and continuous learning.
Founder, Mannat AI
Beamongthefirsttomeasurewhatmatters
Join the early access program and help shape the future of fair, explainable work scoring.