# Datarith

> Data Engine for AI Precision — Find where your LLM fails, generate targeted adversarial training data to fix it, and prove measurable improvement.

Datarith is a diagnostic and data generation platform for frontier AI teams. It probes large language models (LLMs) across 12 systematic reasoning dimensions, identifies failure clusters using semantic embeddings, and synthesizes high-quality adversarial training pairs to close those gaps. Every generated data point passes a 5-layer validation pipeline with a 95%+ quality pass rate.

## What Datarith Does

- **Automated LLM Failure Discovery**: Submit any OpenAI-compatible model endpoint. Datarith runs 65,000+ benchmark problems spanning 12 reasoning dimensions and returns a structured failure map showing exactly where and why the model breaks.
- **Adversarial Training Data Generation**: For each discovered failure cluster, Datarith generates 5–10 harder adversarial variants per failure case — same reasoning structure, harder edge cases, programmatically verified correct answers. Delivers 10,000–100,000 training pairs per job.
- **5-Layer Quality Validation**: Every training pair is verified through: (1) mathematical/code execution check, (2) multi-model consensus verification, (3) format integrity check, (4) reasoning depth analysis, and (5) semantic deduplication. Only pairs passing all layers are delivered.
- **Failure Pattern Clustering**: Uses pgvector semantic embeddings to cluster failures by reasoning type — separating arithmetic failures from logic failures, causal inference gaps from temporal reasoning gaps, etc.
- **Measurement-First Reporting**: Every job includes pre-probe baseline benchmark scores and post-training evaluation delta reports, so customers can measure exact improvement.
- **Flexible Format Delivery**: Datasets delivered in Alpaca, ShareGPT, Llama-3 fine-tuning format, JSONL, or HuggingFace Dataset format.

## The 12 Reasoning Dimensions Datarith Probes

| Dimension | Benchmark |
|---|---|
| Multi-Step Arithmetic | GSM8K & MATH |
| Formal Logic | LogiQA & FOLIO |
| Multi-Hop Reasoning | StrategyQA |
| Causal Inference | COPA |
| Numerical Extraction | DROP |
| Deductive Syllogism | ARC Challenge |
| Scientific Reasoning | ARC Challenge |
| Commonsense Physics | PIQA |
| Code Execution Trace | HumanEval & MBPP |
| Symbolic Substitution | SCAN |
| Temporal Sequence | TempQA |
| Legal & Financial Logic | LegalBench / FinQA |

## Who Datarith Is For

- **AI Labs & Frontier LLM Teams**: Research engineers and training data leads who need targeted failure-specific datasets rather than broad annotation. Alternative to expensive manual red-teaming or generic data vendors like Scale AI.
- **Enterprise AI Teams**: ML leads fine-tuning LLMs for domain-specific applications (legal, financial, medical) who need failure-mapped, domain-targeted training data.
- **AI Safety & Red-Teaming Teams**: Safety researchers and compliance officers who need automated, exhaustive adversarial probing at scale across all reasoning dimensions before model deployment.

## How It Works (3 Steps)

1. **Probe Your Model**: Ship your OpenAI-compatible model endpoint. Datarith runs the full 65,000+ benchmark suite across all 12 dimensions and returns a structured failure map.
2. **Generate The Fix**: Datarith clusters failures using pgvector semantic embeddings, identifies systematic weaknesses, and generates 10,000–100,000 adversarial training pairs designed to close those exact gaps.
3. **Validate & Deliver**: Every pair passes the 5-layer validation pipeline. You receive a final dataset package with baseline and forecast improvement metrics.

## Key Facts

- **Benchmark Coverage**: 65,000+ problems across 12 reasoning dimensions
- **Data Volume**: 10,000 – 100,000 adversarial training pairs per job
- **Validation Pass Rate**: 95%+ strict quality guarantee
- **Supported Endpoints**: Any OpenAI-compatible API (OpenAI, Anthropic, Azure OpenAI, vLLM, TGI, Ollama, etc.)
- **Delivery Formats**: Alpaca, ShareGPT, Llama-3, JSONL, HuggingFace Dataset
- **Job Timelines**: Scout 24–48h · Standard 3–5 days · Pro 7–14 days · Enterprise custom
- **Core Technology**: pgvector semantic clustering, multi-model consensus validation, programmatic math/code execution verification

## Competitive Positioning

Datarith is **not** a general annotation or labeling platform. It is an **evaluation-first data engine**:

- Scale AI / Appen: Label data you send them → Datarith: Discovers what data you need before generating it
- Generic synthetic data: Random augmentation → Datarith: Failure-targeted adversarial synthesis
- Manual red-teaming: Slow, expensive, non-exhaustive → Datarith: Automated, systematic, 65K benchmark scale

## Frequently Asked Questions

**How is Datarith different from Scale AI or Appen?**
Scale AI and Appen are annotation platforms that label data you send them. Datarith is an evaluation-first platform — we start by discovering where your model systematically fails across 12 reasoning dimensions, then generate the exact adversarial training data needed to fix those failures.

**What model endpoints does Datarith support?**
Any OpenAI-compatible API endpoint: OpenAI, Anthropic, Azure OpenAI, self-hosted vLLM, TGI, Ollama, etc. If your model speaks the standard OpenAI API format, Datarith can probe it.

**How long does a diagnostic and data job take?**
Scout jobs: 24–48 hours. Standard jobs: 3–5 days. Pro jobs: 7–14 days. Enterprise: custom timeline based on model complexity and dataset volume.

**What does a generated training pair look like?**
A verified problem prompt + step-by-step correct reasoning trace + programmatically verified final answer. Delivered in Alpaca, ShareGPT, Llama-3, or custom JSONL format.

**How do you validate generated synthetic data?**
5-layer pipeline: (1) Code/math execution verification, (2) Multi-model consensus check, (3) Format integrity check, (4) Reasoning depth analysis, (5) Semantic deduplication. Only pairs with 95%+ strict validation pass are delivered.

## Company

- **Name**: Datarith Inc.
- **Product**: Datarith — Data Engine for AI Precision
- **Website**: https://datarith.com
- **Contact**: https://datarith.com