APIron Lab – LLM Utility APIs

Practical tools for LLM workflows: encoding repair, token counting, CSV hygiene, and response normalization.

Utility APIs focused on Japanese and multilingual text pipelines.

View on RapidAPI GitHub Organization

APIron Lab develops practical utility APIs that support LLM and data-processing workflows.

We focus on Pre-AI Input Hygiene – fixing encoding issues, normalizing text, sanitizing structured input, and counting tokens before you send data to an LLM.

All APIs are available both as open-core on GitHub and as hosted endpoints on RapidAPI.

Featured APIs on RapidAPI link back here for a fuller overview of the APIron Lab lineup, documentation, and product positioning.

Available APIs

Utilities for multilingual text repair, token estimation, CSV cleanup, and unstable JSON normalization.

Encoding / Text Hygiene

Encoding Repair API (v2.0)

Base64-only encoding repair API to fix mojibake across UTF-8 / Shift_JIS / EUC-JP / Latin-1 and more.

For legacy exports, migration files, Japanese text recovery, and pre-AI input cleanup.

  • Base64 raw bytes input to avoid information loss
  • Automatic encoding detection and safe repair behavior
  • Unified UTF-8 output for downstream processing
  • Designed for Japanese / multilingual text pipelines
Docs (GitHub Pages) · GitHub · RapidAPI
Token / LLM Utility

Universal Token Counter (UTC)

High-precision multilingual token counter for OpenAI-compatible encodings with a unified JSON response.

For token estimation, cost control, request validation, and logging before LLM execution.

  • OpenAI-compatible tokenization (o200k_base / cl100k_base)
  • Japanese and multilingual text support
  • Stable result + meta response schema
  • Ideal for cost estimation and logging
Docs (GitHub Pages) · GitHub · RapidAPI
CSV / Data Hygiene

CSV Escape & Sanitize API

Practical CSV cleanup API for escaping, sanitizing, and normalizing structured text before Excel, ETL, or AI ingestion.

For messy CSV exports, spreadsheet safety, ETL cleanup, and LLM-ready structured input.

  • RFC4180-compliant escaping
  • Delimiter detection and structural cleanup
  • Excel formula injection protection
  • Safe preprocessing for LLM and data pipelines
Docs (GitHub Pages) · GitHub · RapidAPI
API / Response Utility

API Adjustmenter

Utility API for stabilizing messy JSON responses through normalization, transformation, and diff-oriented cleanup.

For unstable third-party APIs, LLM JSON cleanup, and reusable downstream contracts.

  • Normalize key styles and common value inconsistencies
  • Apply reusable transformation rules
  • Detect structural drift with diff support
  • Useful for automation and integration cleanup
Docs (GitHub Pages) · GitHub · RapidAPI

Roadmap

We will continue to add utility APIs around preprocessing, quality assurance, and text structuring.