Overview
AI Hub reduces dependency on data science teams by letting business users upload their own datasets and run analytics, modeling, and content-generation workflows through a collection of modular agents. Every agent is self-contained, transparent in its logic, and exports business-ready outputs (Excel, CSV, JSON, dashboards). The platform combines classical ML (scikit-learn) at its core with optional LLM augmentation where natural-language interpretation adds value.
A/B Testing Agent
Experimentation
- Three-step experiment workflow (define → upload → decide) with a single decision card output: ship / hold / continue / no-effect / redesign.
- Deterministic statistical engine: frequentist tests, confidence intervals, guardrails, segment analysis, and Bayesian probability.
Hypothesis Testing
Confidence Intervals
Bayesian Inference
Guardrail Metrics
Segment Analysis
Streamlit
Open on Streamlit Cloud →
Anomaly Detection Agent
Detection
- Runs three unsupervised models in parallel and auto-selects the best by silhouette score; produces per-record anomaly scores and feature-level deviation explanations.
- Highlights flagged rows in an exportable Excel report ready for risk, audit, and operational review.
Isolation Forest
One-Class SVM
Local Outlier Factor
Silhouette Score
Streamlit
Open on Streamlit Cloud →
Data Cleaning Agent
Preparation
- Detects missing values, duplicates, type mismatches, placeholder values, format issues, and outliers; user selects the corrective action per issue.
- Applies user-approved transformations sequentially and emits a transparent change log so the cleaned dataset feeds downstream agents safely.
pandas
numpy
Missing Value Imputation
IQR / Winsorize
Type Coercion
Audit Log
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Data Comparison Agent
Reconciliation
- Compares 2–4 files (CSV / Excel / XML) on a key column; surfaces row-level diffs with yellow / green / red color coding and presence-matrix across files.
- Metric mode computes percent change, absolute delta, and ratio against a chosen baseline; read-only by design, full Excel export with preserved color encoding.
Key-based Matching
Diff Engine
Percent Change
Read-only Pipeline
Excel Export
Optional LLM Narrative
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Data Quality Agent
Quality Control
- Detects 8 issue types (missing, format, type drift, semantic inconsistency, duplication, range violation, sparse columns, low-signal features) and assigns Low → Critical risk levels.
- Cell-level color highlighting and prioritized issue list ensures downstream agents (anomaly, segmentation, forecasting) operate on validated data.
Rule-based Validation
Risk Scoring
Statistical Profiling
Cell-level Highlighting
Excel Reports
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Data Visualization Agent
Visualization
- Auto-profiles columns, recommends chart candidates, and produces interactive Plotly visuals (distribution, trend, correlation, comparison).
- Manual chart-builder flow for users who want full custom control; bulk download of all generated visuals.
Plotly
Auto Data Profiling
Chart Recommendation
Streamlit
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Feature Engineering Agent
Preparation
- Auto-detects column types and offers type-appropriate transformations alongside “What is it? / When to use it?” explanations for each option.
- Every applied transformation is recorded in a timestamped log; output dataset and log are downloadable as CSV / Excel.
Date Decomposition
One-Hot / Frequency Encoding
Z-score
Log Transform
Binning
Missing-Value Flags
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Forecasting Agent
Time Series
- Profiles the series, auto-detects frequency, runs four forecasting models in parallel, and selects the lowest-error model with full transparency on relative performance.
- What-if scenario overlays for campaigns, special days, and trend assumptions; designed as a decision-support tool, not an autonomous decision engine.
SARIMA
Prophet
XGBoost
Naive Baseline
What-if Simulation
Confidence Intervals
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MailCraft Agent
LLM
- LLM-powered email generation for new emails or replies; tone, length, category, language, salutation, and corporate signature parameters are enforced via system-prompt engineering.
- One-click variant regeneration (shorter / more formal / more polite / clearer) and bilingual TR/EN support.
LLM
Prompt Engineering
Tone Control
Bilingual TR/EN
vLLM
Streamlit
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Mock Data Generator Agent
Synthetic Data
- Two modes: Upload (auto-extracts schema from a sample file and replicates structure) and Scratch (column-by-column definition from zero).
- Supports schema consistency, categorical weighting, correlation rules, business rules, and noise modes — output up to 1M rows in CSV / Excel / JSON.
Faker
Schema Inference
Statistical Sampling
Correlation Rules
Business Rule Engine
CSV / Excel / JSON Export
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Prompt Optimization Agent
LLM Tooling
- Refines raw user prompts into clearer, more structured, result-focused instructions tailored to a target agent (Code Assist / MailCraft / General Purpose).
- Acts as a quality multiplier across the rest of AI Hub, indirectly raising the precision of every LLM-backed agent.
LLM
Prompt Engineering
Template Library
Agent-Aware Routing
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Quiz Generator Agent
Content Generation
- Generates context-grounded MCQ, True/False, and Fill-in-the-Blank questions from training documents (PDF, PPTX, DOCX, TXT) using an on-prem / corporate LLM.
- Document parsing, paragraph-aware chunking, multi-stage LLM generation, quality filters, Turkish-first NFC normalization, and LMS-compatible (Enocta) Excel export.
LLM
Document Parsing
Smart Chunking
Multi-Stage Pipeline
NFC Normalization
vLLM
LMS Excel Export
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Segment Intelligence Agent
Clustering
- Performs unsupervised segmentation and surfaces, for each cluster, summary statistics, distinguishing features, and deltas vs. the overall population.
- Cluster summaries can be translated into business-language profiles and action ideas with LLM.
K-Means
Cluster Profiling
Feature Differentiation
LLM
Streamlit
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Smart Modeling Agent
AutoML
- Five-tab guided flow: upload → profile → problem definition → train → results. Auto preprocessing (imputation, encoding, optional scaling), stratified split where needed, multi-algorithm comparison.
- Explicit positioning as a prototyping / exploration tool, not production deployment — every choice is logged into a downloadable Modeling Artifact ZIP for auditability.
scikit-learn
Classification
Regression
Auto Preprocessing
Stratified Split
Multi-Algorithm Comparison
Modeling Artifact
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Social Pulse AI
Social Intelligence
- Multi-bank social-media analytics platform: sentiment distribution, engagement, reach, anomaly detection, and topic breakdowns from multi-sheet Excel datasets.
- Modules for brand perception, crisis early-warning, competitor benchmarking, audience profiling, and action recommendations — turns raw comments into “Insight → Decision → Action”.
LLM
Sentiment Analysis
Anomaly Detection
Competitor Benchmarking
Topic Analytics
Streamlit
Open on Streamlit Cloud →