Generate TypeScript interfaces and types from CSV data
Generate strongly-typed TypeScript interfaces, types, and JSDoc comments directly from CSV column headers and sample data rows.
🛡️ Your rows never leave your browser.
Why Onfyt: Processes csv to typescript interface entirely in-browser with dedicated Web Workers so your rows never leave your machine.
Interactive Sample Data
| user_id | full_name | email_verified | login_count | last_active |
|---|---|---|---|---|
| usr_1001 | Grace Hopper | true | 42 | 2026-03-15T10:00:00Z |
| usr_1002 | Alan Turing | false | 15 | 2026-03-14T08:30:00Z |
| usr_1003 | Ada Lovelace | true | 120 | 2026-03-16T14:20:00Z |
| usr_1004 | Claude Shannon | true | 8 |
How this tool works
When integrating CSV exports or third-party spreadsheet uploads into modern TypeScript, Next.js, or Node.js applications, developers must write interface definitions to type-check incoming records. Creating these interfaces manually for datasets with dozens of columns is error-prone and time-consuming.
This tool analyzes all rows in your CSV dataset to infer accurate TypeScript types:
- Integer and floating-point columns are typed as number.
- "true" and "false" values are typed as boolean.
- ISO 8601 timestamps are identified as string (or Date).
- Columns with blank or missing values are flagged as optional (?) or unioned with null (string | null).
- Invalid identifier characters (spaces, hyphens) are sanitized into camelCase or wrapped in quotes.
The type inference engine inspects every row rather than relying solely on the first record. If a column contains numbers in early rows but text tokens or missing entries later on, the engine widens the type safely to a union (e.g., number | string | null), preventing runtime crashes when parsing heterogeneous production spreadsheets.
The entire analysis runs client-side in your browser. Your customer data, internal database columns, and schemas remain confidential and never leave your computer. You get clean, production-ready TypeScript interfaces ready to paste into your frontend or backend codebase.
From a compiler and type system perspective, generating robust TypeScript interfaces from CSV datasets requires deep type inference across every record in the table. Naive type generators inspect only the first row, leading to compilation errors if subsequent rows introduce string values into numeric columns or contain empty cells. DataFix inspects the entire dataset to infer accurate primitive types, identifying floating-point numbers, integers, booleans, and ISO timestamps, while safely widening inconsistent columns to union types (such as string | number | null).
For software engineering teams building production TypeScript and Next.js applications, client-side type generation ensures that proprietary database schemas and internal dataset structures remain completely private. Transmitting schema definitions and customer data to external online type generators exposes internal application architecture to external surveillance. DataFix runs entirely in-memory within your browser sandbox via Web Workers, providing zero-latency code generation and complete security.
From an architectural perspective, integrating strongly typed interfaces into frontend data pipelines prevents subtle runtime bugs before code reaches production. When frontend components consume CSV uploads without strict type definitions, undefined property accesses and silent type coercions (such as adding "10" and 5 to get "105") create frustrating visual bugs. The interfaces generated by DataFix can be dropped directly into TypeScript projects, providing immediate autocomplete support in Visual Studio Code and strict compilation safety across React, Vue, and Angular applications.
For software engineering teams building production TypeScript and Next.js applications, client-side type generation ensures that proprietary database schemas and internal dataset structures remain completely private. Transmitting schema definitions and customer data to external online type generators exposes internal application architecture to external surveillance. DataFix runs entirely in-memory within your browser sandbox via Web Workers, providing zero-latency code generation and complete security.
Things that usually go wrong
- Column Names with Spaces: Headers like "Customer Name" are invalid TypeScript identifiers. DataFix converts them to camelCase (customerName) or quotes ("Customer Name").
- Premature Type Narrowing: If row 1 has a number but row 5 has text, a naive parser fails. DataFix inspects all rows to ensure safe union types (string | number).
- Empty First Row Cells: If the top row has blank cells, DataFix scans subsequent rows to determine the true underlying data type.
Edge cases we tested
🧪 Empty cells or missing values
Safely handled without schema corruption.
🧪 Leading or trailing cell whitespace
Sanitized prior to processing.
🧪 Large files with 25,000+ rows
Streamed via client-side Web Worker without tab freezing.
Frequently Asked Questions
Can it generate type aliases instead of interfaces?
Yes. You can toggle between "export interface" and "export type" syntax.
How does it detect optional properties?
If any row has an empty cell for a column, the property is generated with a question mark (e.g. bio?: string).
Can I customize the casing of property names?
Yes. You can preserve original casing or convert headers to camelCase, snake_case, or PascalCase.
Is my data transmitted to any server?
No. The type generator executes entirely in your browser using local JavaScript.
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