Python
Pandas
SQL
Jupyter Notebooks
R
NumPy
Apache Spark
PySpark
Google Sheets
OpenRefine
Excel
Talend
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Supported AI models on Workik
GPT 4.1 Mini, GPT 4.1, GPT o4 Mini, GPT - 4o, GPT 4o Mini, o3-mini
Gemini 2.5 Flash Preview, Gemini 2.0 Flash, Gemini 1.5 Pro
Claude 3.5 Haiku, Claude 3.7 Sonnet
Deepseek Reasoner, Deepseek Chat, Deepseek R1(High)
Llama 3.3 70B, Llama 3.1 405B Instruct
Mistral 8x7B Instruct, Mistral Small, Mistral Large, Codestral
Models availability might vary based on your plan on Workik
Features
Clean Data Instantly
AI generates precise scripts to drop duplicates, fix types, and clean unstructured records across SQL, CSV, and Excel.
Fix Formatting Accurately
Leverage AI to standardize dates, currency, and strings with intelligent transformations using Pandas, PySpark, or raw SQL.
Prep for Analysis or ML
AI builds structured pipelines with normalization, encoding, and outlier fixes for model-ready datasets.
Merge Datasets Seamlessly
AI resolves overlaps, unifies headers, and deduplicates rows across sources like CRM exports or web-scraped files.
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TESTIMONIALS
Real Stories, Real Results with Workik
Workik cleaned inconsistent field formats across millions of CRM records and generated scripts to handle edge cases we missed.
Stefan Tuning
Backend Developer
I used Workik to normalize schemas from multiple PostgreSQL tables and clean legacy null-handling logic before migrating to Snowflake.
Jared Lunther
Data Engineer
Workik helped us deduplicate and standardize product data across vendor feeds, making our analytics reports 10x more reliable.
Ethan Morales
Full Stack Developer
What are some popular use cases of Workik's AI-powered Data Cleaning Code Generator?
Workik’s AI-powered Data Cleaning Code Generator supports a variety of real-world use cases which includes but are not limited to:
* Generate Python, SQL, or PySpark scripts for handling missing values, standardizing formats, and fixing data types.
* Deduplicate records in customer, sales, or event logs across large datasets.
* Normalize date, currency, and categorical fields for reporting and analytics.
* Detect and correct schema drift across evolving data pipelines.
* Validate data integrity by checking type mismatches, constraint violations, and null patterns.
* Prepare clean, structured data for BI dashboards and machine learning models.
* Clean and merge multi-source datasets from APIs, CSVs, or databases with inconsistent structures.
What context-setting options are available in Workik’s AI for Data Cleaning Code Generator?
Workik offers powerful context-setting options for Data Cleaning tasks, enabling users to:
* Connect to databases like PostgreSQL, MySQL, MongoDB, or Snowflake.
* Link GitHub, GitLab, or Bitbucket to access and clean versioned datasets.
* Upload data schemas in JSON, CSV, or SQL formats to guide AI cleaning logic.
* Define column types, key fields, and null-handling rules for structured cleaning.
* Add examples of dirty data to refine AI’s understanding of formatting issues.
* Include data profiling insights to improve validation and transformation steps.
* Import API definitions to clean and validate incoming API payloads or responses.
Can Workik help me standardize user-generated content like names, addresses, or feedback text?
Yes. Workik uses AI to intelligently clean and normalize free-text inputs. It can fix inconsistent casing in names, standardize address abbreviations (like “St.” to “Street”), and even detect junk or placeholder values in feedback fields. This is especially useful in survey analysis or onboarding forms.
What if my data changes daily or has frequent updates?
Workik lets you convert your cleaning logic into reusable, trigger-based pipelines. Set up scheduled runs or connect it to Git events so every time new data is pushed, Workik applies cleaning logic automatically — ideal for teams working with time-sensitive or streaming data.
Is Workik useful for quick exploration before full data cleaning?
Yes. You can upload any dataset and get an instant AI-generated profile report showing null counts, outlier columns, inconsistent types, and other high-level diagnostics, perfect for knowing where to start, especially during audits or early analysis.
Can Workik clean external API responses or real-time JSON data?
Definitely. You can upload or pipe in JSON payloads from APIs and use Workik to validate schema, filter unwanted fields, normalize key names, or convert them into structured formats for storage or analysis.
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DATA CLEANING: Question & Answer
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