Sliq

Product Introduction

1. Definition: Sliq is an AI-powered data cleaning platform designed for technical users (data engineers, analysts, and scientists) working with structured and unstructured datasets. It automates error correction, missing value imputation, and schema standardization using machine learning algorithms.
2. Core Value Proposition: Sliq eliminates manual data preprocessing bottlenecks by transforming raw, messy data into analysis-ready datasets within minutes—accelerating analytics pipelines and AI/ML workflows.

Main Features

1. Context-Aware Cleaning Engine:
   Sliq uses domain-specific NLP models (trained on finance, healthcare, retail, etc.) to interpret data semantics. For example, it auto-corrects "$1.5K" to "1500" in financial data or standardizes medical codes. Built on PySpark and TensorFlow, it infers context from column headers, values, and metadata.
2. Distributed Processing Architecture:
   Leverages parallel computing (Dask/Ray) to clean gigabyte-scale datasets in minutes. Benchmarks show 10x faster processing than Pandas-based tools. Handles CSV, JSON, Parquet, and SQL sources natively.
3. Schema Intelligence:
   Automatically detects and repairs schema drift (e.g., date formats changing from "DD/MM/YY" to "YYYY-MM-DD") using probabilistic pattern matching. Generates data quality reports with error classifications (nulls, duplicates, outliers).

Problems Solved

1. Pain Point: Engineers waste 60–70% of project time manually fixing data errors, delaying analytics and causing model inaccuracies.
2. Target Audience:
   • Data engineers managing ETL pipelines
   • Analysts preparing business intelligence reports
   • ML engineers preprocessing training data
3. Use Cases:
   • Fixing sales data with mixed currency formats before revenue analysis
   • Imputing missing patient records for clinical research
   • Standardizing e-commerce logs for recommendation engines

Unique Advantages

1. Differentiation: Unlike OpenRefine or Python scripts, Sliq requires no manual rule-writing. It outperforms generic tools (e.g., Trifacta) with domain-aware corrections and Python/SDK integrations.
2. Key Innovation: Patented "Semantic Repair" technology combines transfer learning and probabilistic graph networks to resolve ambiguities (e.g., inferring "NY" = "New York" in addresses).

Frequently Asked Questions (FAQ)

1. How does Sliq handle sensitive data?
   Sliq processes data locally or in your VPC with SOC 2 compliance. No raw data leaves your infrastructure during cleaning.
2. What file sizes can Sliq process?
   Optimized for datasets up to 100GB via distributed computing. Handles 1M+ rows in under 3 minutes on standard cloud instances.
3. Does Sliq support custom data cleaning rules?
   Yes, extend base models with user-defined Python functions for organization-specific validations (e.g., custom ID formats).
4. Can Sliq clean unstructured text data?
   Current version focuses on tabular/semi-structured data. NLP-based text cleaning is roadmap for Q4 2025.
5. How is pricing structured?
   Tiered by compute hours and dataset volume. Free tier includes 10GB/month cleaning; enterprise plans offer SLA-backed throughput.