Fueling Frontier AI With High-Precision Data.
Because AI Models Are Only As Intelligent As The Data They Consume.
DATARITH
Data Engine for AI Precision
Find LLM Failures. Generate The Fix. Prove Improvement.
How Datarith Works
From systematic reasoning failure discovery to 5-layer validated training pairs in days, not months.
Probe Your Model
We Test Across 12 Reasoning Dimensions
Run your LLM through 65,000+ benchmark problems spanning arithmetic, formal logic, multi-hop reasoning, causal inference, and more. We map every failure with precision.
- GSM8K & MATH Arithmetic Probes
- LogiQA & FOLIO Formal Logic
- StrategyQA & COPA Causal Probes
Generate The Fix
We Cluster Failures and Build Hard Variants
We cluster your failures using pgvector semantic embeddings, identify systematic weaknesses, and generate 10,000–100,000 adversarial training pairs specifically designed to close those gaps.
- Adversarial Edge-Case Variants
- Semantic Similarity Clustering
- Targeted Failure Pair Synthesis
Validate & Deliver
We Verify Every Data Point Before Delivery
Each training pair passes through 5-layer validation — mathematical verification, code execution, consensus checks, format validation, and deduplication. You get data you can trust.
- 95%+ Strict Pass Rate Guarantee
- Multi-Model Consensus Checks
- Pre/Post Accuracy Delta Forecast
12 Dimensions of Reasoning Failure
Click any dimension card below to inspect real-time LLM failure traces vs. Datarith's generated precision fixes.
Multi-Step Arithmetic Probe
"Janet sells 16 apples. She keeps 4 and divides remainder into 3 bags... Model calculated 16 - 4 = 12 / 2 = 6 apples (Division mismatch error)."
"Adversarial variant generated with formal chain-of-thought verification: Step 1: 16 - 4 = 12. Step 2: 12 / 3 = 4 per bag. Verified via 5-layer quality gate."
Automated Failure Discovery
Send us any OpenAI-compatible model endpoint. Datarith probes it across 12 distinct reasoning dimensions and returns a structured failure map — diagnosing what your model systematically gets wrong and why.
Adversarial Data Generation
For every discovered failure cluster, we generate 5–10 adversarial variants — same reasoning structure, harder edge cases, verified correct answers. Built with stronger LLMs and validated programmatically.
5-Layer Quality Gate
Every training pair is verified: (1) Mathematical check, (2) Multi-model consensus, (3) Format integrity, (4) Reasoning depth, and (5) Semantic deduplication. 95%+ pass rate guarantee.
Failure Pattern Clustering
We cluster failures by semantic similarity using pgvector embeddings. Separate your
model's multi-step arithmetic failures from logic failures and get dataset targeting for each
cluster.
Measurement-First Reports
Every job returns: per-dimension accuracy scores, detailed failure analysis, dataset improvement forecasts, and pre/post benchmark comparison metrics.
Format Flexible Delivery
Datasets delivered formatted specifically for your training pipeline: Alpaca, ShareGPT, Llama 3 fine-tuning format, JSONL, or HuggingFace Dataset format.
Built For Teams Who Need Precision
AI Labs & Frontier LLM Teams
"We have an internal model that keeps failing on specific math benchmarks. We don't want to pay Scale AI to label more data we already have. We need the right data that fixes what breaks."
Enterprise AI Teams
"We're fine-tuning an LLM for legal and financial applications. Generic data vendors just label what we send them. We need someone to tell us where our model breaks and build the fix."
AI Safety & Red-Teaming Teams
"We need to understand systematically where frontier models fail before deployment. Manual red-teaming doesn't scale. We need automated adversarial probes."
Stop Labeling Data Blindly.
"The highest-value training data is not the data you don't have — it's the data that fixes your specific failures."
AI companies spend millions on training data. But almost none of them know where their model actually fails. They annotate data blindly, hoping that more data = better reasoning. It doesn't work that way.
A model that gets 80% on multi-step arithmetic doesn't need more arithmetic data. It needs the specific variant of arithmetic that makes it fail.
We built Datarith to make LLM reasoning measurable, fixable, and improvable — one failure pattern at a time. No guesswork. No annotation waste.
Frequently Asked Questions
Scale AI and Appen are annotation platforms — they 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.
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.
Scout jobs take 24–48 hours. Standard jobs take 3–5 days. Pro jobs take 7–14 days. Enterprise custom timelines are tailored to model complexity and dataset volume.
A training pair consists of: verified problem prompt + step-by-step correct reasoning trace + programmatically verified final answer. Delivered in Alpaca, ShareGPT, Llama-3, or custom JSONL format.
We run a 5-layer validation pipeline on every pair: (1) Code/math execution verification, (2) Multi-model consensus check, (3) Format integrity check, (4) Reasoning depth analysis, and (5) Semantic deduplication. Only pairs passing 95%+ strict validation are delivered.
Every job includes pre-probe baseline scores and post-training evaluation. You run the identical probe benchmark suite on your retrained model, and Datarith generates a comparative improvement delta report.