Framework Capabilities Projects Philosophy GitHub
AI-Assisted Analytics Framework

Analytics that
knows its limits.

Agent-driven statistical reasoning with human validation built in. Structured, auditable, and governance-aware throughout. From descriptive baselines to strategic forecasting.

View on GitHub Explore Framework
7×
Case Projects
4
Business Domains
13+
Analytical Modules
∞
Human Oversight
Explore
Descriptive Analytics
Customer Segmentation
Relationship Analysis
Statistical Inference
Churn Prediction
A/B Experimentation
Strategic Forecasting
Validation Workflows
Executive Reporting
Governance Layer
Descriptive Analytics
Customer Segmentation
Relationship Analysis
Statistical Inference
Churn Prediction
A/B Experimentation
Strategic Forecasting
Validation Workflows
Executive Reporting
Governance Layer
Analytics Lifecycle

From data to decision.

These portfolio projects map a complete analytical progression, evolving from descriptive intelligence to strategic foresight.

P01
Describe
Marketplace
"What is happening?"
P02
Segment
Fintech
"Who behaves differently?"
P03
Relate
SaaS
"What moves together?"
P04
Infer
Fintech
"How confident are we?"
P05
Predict
SaaS
"Who is at risk?"
P06
Test
Product
"Did the change work?"
P07
Forecast
SaaS Strategy
"What might happen next?"
Core Capabilities

Built for
every stage.

Reusable analytical modules from cleaning and descriptive statistics all the way through prediction, experimentation, and governance-aware forecasting.

Descriptive Analysis
KPI foundations, distributions, trend identification and operational baselines across domains.
Segmentation
Behavioural clustering and risk profiling across customer and operational cohorts.
Relationship Analysis
Correlation mapping and variable interaction discovery with assumption testing throughout.
Statistical Inference
Uncertainty-aware hypothesis testing with confidence intervals and assumption review built in.
Predictive Modeling
Churn prediction and retention-risk scoring with model evaluation and validation-first reporting.
Experimentation
A/B testing with statistical power analysis, effect size estimation, and randomisation checks.
Forecasting
Probabilistic scenario modelling and strategic decision support with explicit uncertainty bands.
Governance Layer
Assumption disclosure, uncertainty-aware reporting, and human-review enforcement throughout.
StatsAgent Portfolio

7 case studies.
One complete lifecycle.

P01Marketplace
Descriptive Analytics & KPI Foundations
Operational baselines, key metric frameworks, and distribution analysis across marketplace activity patterns.
P02Fintech
Customer Segmentation & Risk Profiling
Behavioural clustering and repayment-risk identification across a synthetic lending portfolio.
P03SaaS
Relationship Analysis & Operational Patterns
Correlation discovery and variable interaction mapping across account operational behaviour.
P04Fintech
Statistical Inference & Uncertainty Analysis
Hypothesis testing with confidence intervals, effect estimation, and assumption-aware interpretation.
P05SaaS
Churn Prediction & Retention Modeling
Classification modelling for at-risk account identification with validation-first evaluation protocols.
P06Product Analytics
Experimentation & A/B Testing
Statistical power, randomisation checks, and effect estimation for product change validation.
P07SaaS Strategy
Strategic Forecasting & Scenario Modeling
Probabilistic forecasting with scenario comparison, assumption-dependent projections, and executive decision-support outputs. The capstone stage of the analytical lifecycle.
Forecasting Scenario Modeling Executive Reporting Strategic Decisions
Analytical Philosophy

Four principles.
One standard.

The convictions that shape every analytical decision inside StatsAgent.

01
Context Before Calculation
Every metric must answer a business question. Calculations without operational interpretation are incomplete analysis — numbers alone never create understanding.
02
Validation Before Interpretation
Analytical outputs are not automatically trusted because code executed successfully. Each stage includes validation checks, assumption review, and explicit uncertainty discussion.
03
Interpretation Before Automation
StatsAgent is AI-assisted analysis with human validation, not a fully autonomous decision-making agent. The objective is to support and accelerate analysts, not replace them.
04
Governance-Aware Analytics
Explicit reinforcement of uncertainty awareness, limitation disclosure, observational vs. causal distinction, assumption transparency, and operational review requirements at every stage.
Technology Stack

The tools
underneath.

Purpose-chosen for interpretability and reproducibility — not black-box optimisation.

agent_framework
Agno
llm_provider
Claude — Anthropic
statistical_engine
Python
core_libraries
Pandas · NumPy · SciPy · Scikit-learn
visualization
Matplotlib · HTML Dashboards
reporting
Executive HTML Reporting
validation
Custom Governance & Review Layer
Governance Layer

Built-in honesty.
By design.

StatsAgent discloses assumptions, enforces review requirements, and keeps humans genuinely in the loop at every stage.

Synthetic Data Disclosure
All datasets are clearly flagged as synthetic throughout documentation.
Human Review Required
AI outputs are designed for analyst oversight, not autonomous deployment.
Uncertainty-Aware
Every estimate is accompanied by confidence intervals and assumption disclosure.
Causal Separation
Observational findings are strictly separated from causal interpretations.
Assumption Transparency
Modelling assumptions are surfaced and documented for every analytical stage.
Auditability
Every output is traceable: validation logs, metadata, and changelogs included.
About

Analytical systems.
Human judgment.

Raianne Kuzer is a product-minded analyst interested in interpretable data, decision systems, and strategy.

Focus
Analytics systems, statistical reasoning, product thinking, and governance-aware AI workflows.
Built With
Python, SQL, statistical analysis, visualization, and structured analytical frameworks.