Training Data for
Frontier Intelligence.

Acadify provides curated, human-verified datasets needed to train models that think, code, and reason. We focus on quality over quantity, delivering expert-level SFT and RLHF data.

// DATASET_AUDIT_LOG
> Checking data quality score...
> Domain: Advanced_Calculus
> Source: SME_Verified_Chains
> Logic Gap Check: PASSED
> STATUS: PRODUCTION_READY
VERIFICATION DENSITY 98.5%

Specialized Training Data

We deliver highly-curated datasets designed to solve specific reasoning bottlenecks in LLMs.

50M+

Programming & SWE

High-density repository-level data including complex pull request discussions, multi-file context tracking, and execution traces. Designed to train agents for autonomous bug fixing.

LANGUAGES Python, Rust, C++, TypeScript, Go
STRUCTURE Verified Context & Traces
100K+

STEM Reasoning

Subject-matter expert (SME) verified chains of thought for advanced physics, chemistry, and graduate-level mathematics.

50K+

RLHF Preference

Curated response pairs focusing on complex alignment constraints, safety guardrails, and instructional compliance.

Multimodal Interaction

Screenshot-to-action sequences and OCR-grounded layout analysis to train visually-aware GUI agents.

1M+
INTERACTION SAMPLES

Real Data,
Real Results.

We prioritize precision. Every Acadify dataset undergoes a multi-stage verification process by domain experts to ensure zero hallucinations in the training corpus.

Expert Verification

Data points in our reasoning suite are verified by human professionals to ensure technical accuracy and logical flow.

Execution Tracing

Coding instructions are verified by executing them against sandboxed test cases, ensuring the provided code actually works.

Dataset FAQ

Common questions about our boutique data collection and quality assurance methods.

We use advanced n-gram analysis and semantic hashing to ensure our curated training data does not accidentally overlap with public test benchmarks like MMLU or HumanEval, preserving the integrity of downstream evaluations.

Our specialized STEM data is created in-house by our network of subject matter experts (SMEs) who write original, multi-step reasoning chains designed to correct specific LLM failure modes.