D_AI_ta Lint

What type of AI/ML would you like to implement?

Select from 20+ AI/ML use cases across 4 categories

All

Traditional ML

Deep Learning

Generative AI

Specialized

🎯

Classification

Spam detection, sentiment analysis, fraud

Traditional ML

🧠

RAG

Knowledge Q&A, chatbots, document search

Generative AI

📈

Regression

Price prediction, forecasting, risk scoring

Traditional ML

💬

NLP

NER, sentiment, translation, topic modeling

Deep Learning

⭐

Recommendation

Personalized recommendations, content

Specialized

🚨

Anomaly Detection

Fraud, system failures, outliers

Specialized

⏰

Time Series

Stock prices, demand, IoT sensors

Specialized

👁️

Computer Vision

Object detection, face recognition

Deep Learning

🎓

LLM Fine-tuning

Customize LLMs for domain tasks

Generative AI

+ 11 more use cases available

Select your data source

Connect to 15+ data sources across databases, warehouses, files, and APIs

🗄️

SQL Database

PostgreSQL, MySQL, MS SQL, Oracle, SQLite

🍃

NoSQL Database

MongoDB, Cassandra, DynamoDB, CouchDB

🏭

Data Warehouse

Snowflake, BigQuery, Redshift, Databricks

☁️

Cloud Storage

AWS S3, Azure Blob, Google Cloud Storage

📄

File Upload

CSV, JSON, Excel, Parquet, Avro, XML

🌊

Streaming

Kafka, Kinesis, Pub/Sub, Event Hubs

🔌

REST API

HTTP endpoints, webhook data

🧮

Vector DB

Pinecone, Weaviate, Qdrant, Milvus

⏱️

Time Series DB

InfluxDB, TimescaleDB, Prometheus

+ 6 more data sources available

Label your dataset

Specify which column contains your target labels

Label Column

Selected: Classification

Source: PostgreSQL Database

Dataset: customer_data (5,000 rows)

AI Readiness Score

0%

Analyzing...

Dataset: customer_data | 5,000 rows | 8 columns | Classification Use Case

Data Quality Metrics

Completeness 0%

Accuracy 0%

Consistency 0%

Validity 0%

Uniqueness 0%

AI Readiness Metrics

Data Sufficiency 0%

Feature Quality 0%

Label Quality 0%

Data Diversity 0%

Data Balance 0%

Comprehensive Action Plan

All

Critical

High

Critical: Insufficient training data

Your dataset has 5,000 rows but needs at least 10,000 for reliable model training.

Mitigation Steps:

Collect more data from production systems
Use data augmentation (SMOTE, synthetic data generation)
Consider transfer learning with pre-trained models

High: Class imbalance (1:87 ratio)

Minority class has only 57 samples vs 4,943 majority samples.

Mitigation Steps:

Use SMOTE oversampling to balance classes
Apply class weights in model training
Consider undersampling majority class

Medium: Missing feature engineering

Create additional features from existing columns to improve model performance.

Mitigation Steps:

Create interaction features (e.g., age × income)
Extract date features (month, day of week)
Normalize numerical features

Column Analysis

Column	Type	Null %	Unique %	Issues
customer_id	integer	0%	100%	None
age	integer	2%	85%	Outliers
customer_churned	boolean	0%	1%	Imbalanced

Outcomes that matter

Production‑ready data in hours

De‑risk AI launches

Connect anything in minutes

Actionable guidance, not red flags

Fits your workflow

Enterprise confidence

15+ Data Sources

See It In Action

What type of AI/ML would you like to implement?

Classification

RAG

Regression

NLP

Recommendation

Anomaly Detection

Time Series

Computer Vision

LLM Fine-tuning

Select your data source

SQL Database

NoSQL Database

Data Warehouse

Cloud Storage

File Upload

Streaming

REST API

Vector DB

Time Series DB

Label your dataset

AI Readiness Score

Data Quality Metrics

AI Readiness Metrics

Comprehensive Action Plan

Column Analysis

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Free

Starter

Professional

Enterprise

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