Announcing | TLM (Trustworthy Language Model) for reliable LLM outputs.Learn more.

Business Intelligence & Analytics

Real-world data is messy and full of errors and other problems, which can lead to faulty data analysis. Draw more accurate conclusions by first quickly correcting your dataset.

Cleanlab’s AI automatically detects incorrect values and other issues lurking in your dataset (outliers, near duplicates, low-quality examples, non-IID sampling, etc). This includes errors in associated metadata (e.g. annotations or tags for images/documents).
Hero Picture

HOW CLEANLAB CAN IMPROVE YOUR DATA ANALYSIS

Help Section Icon
Videos on using Cleanlab Studio to find and fix incorrect values in:
Help Section Icon
Summarize overall patterns in data errors to better understand where they stem from and how they might affect conclusions. Read about enhancing data preparation with AI for Business Intelligence.
Help Section Icon
Audit data stored in many file formats: Excel, CSV, JSON, etc. including data with many raw text fields or images.
Help Section Icon
Automatically discover outliers (anomalies) which may have an outsized impact on data-driven conclusions and should be handled with care. Read more.
Help Section Icon
Automatically detect violations of key statistical assumptions like IID-sampling, e.g. if the data are drifting over time. Such violations may invalidate many data-driven conclusions. Read more.
Help Section Icon
Effectively analyze crowdsourced datasets in a robust manner, and estimate which examples require additional review and which annotators are best/worst overall. Read more.
Help Section Icon
Use Cleanlab AutoML to train and deploy state-of-the-art ML models in 1-click. Robustly train models on cleaned data to predict any information recorded in your dataset, no Machine Learning expertise required! This can help with missing value imputation and other tasks involving incomplete information.
Help Section Icon
Read about ensuring high quality evaluation data for LLM prompt selection.
Help Section Icon
Read about automatic error detection for multi-label data (e.g. image/document tagging).
Help Section Icon
Read about errors in famous datasets detected with Cleanlab Studio.


Cleanlab Studio auto-corrects raw data to ensure reliable analytics so you can make good decisions.

Image
Case Study
Estimating Wake-Word False Accept Rates of Smart Speaker


Google used Cleanlab to estimate how often its assistant devices mis-respond to the wake-word “Hey Google”.

Image

Amazon used Cleanlab to estimate how often its assistant devices mis-respond to the wake-word “Alexa”.

Image

These estimation problems are challenging due to incomplete data and erroneous labels. Learn more.

Case Study
E-commerce analytics


Cleanlab Studio was used to improve an E-commerce website, product listings, and analytics. Finding and fixing errors in product descriptions/metadata can be entirely automated, and improves: customer experience, product discoverability, SEO, advertising, as well as analytics/decision-making.

Read more: Enhancing Product Analytics and E-commerce with Cleanlab Studio

Image