Training datasets
Expert-authored prompts, technical problems, solutions, and reference material for training and post-training.
Specialized AI training data
Expert data for frontier AI.
We create technically rigorous training and evaluation datasets across biological sciences, electrical engineering, and computer science.
Built for the complexity of scientific and engineering reasoning.
RK / DATA SYSTEMS
BIO / EE / CS
EXPERT ↗ VERIFIED
Specialized knowledge → structured data
FIG. 01Complex domains. Clear training signals.
Domain-specific expertise
Biology, engineering & computation
Data built to specification
Structured around model objectives
Quality-first workflow
Expert review & task-level checks
01 / What we do
Data for the problems that matter.
From expert-authored examples to targeted capability evaluations, we focus on the hard-to-source data that scientific and technical models need.
Evals & benchmarks
Challenging, domain-specific tasks and reference answers designed to reveal model strengths and gaps.
Custom data programs
Data creation scoped around a client's target domain, model behavior, task format, and validation needs.
02 / Our expertise
Built for technical depth.
Specialist knowledge is essential where a plausible answer is not necessarily a correct one.
Biological sciences
Data for reasoning across biological mechanisms, systems, and computational analysis.
Electrical engineering
Technically demanding problems spanning physical systems, hardware, and computation.
Computer science
Expert-created data focused on computational reasoning and software correctness.
Our scope is intentionally focused. Specific topics and expert requirements are defined with each project.
Have a specialized request? ↗03 / Our approach
Rigor from the first example.
Scientific AI data needs more than surface-level plausibility. Our approach centers on domain fit, clear specifications, and validation designed for the task.
Step 01
Define
Agree on domain scope, task types, acceptance criteria, and expected deliverables.
Step 02
Author
Match task creation to contributors with relevant academic or technical expertise.
Step 03
Review
Use domain-appropriate expert review to check accuracy, clarity, and difficulty.
Step 04
Validate
Apply objective or executable checks where possible before dataset delivery.
Each workflow is tailored to the project. Verification methods and review depth are agreed upon during scoping.
04 / About RKash
A focused partner for scientific AI data.
RKash is building a specialized AI data company around a simple conviction: models tackling technical subjects need data created with genuine subject-matter understanding.
Founded by Rojan Kashani, a UC Berkeley Electrical Engineering and Computer Sciences graduate, RKash brings an engineering mindset to expert-driven dataset creation.
Get in touchLet's work together
Better models start with better data.
Planning a specialized training dataset or evaluation? Tell us what capability you're working on and let's explore the right approach.
Contact RKash