More parameters won't get you there. More data won't either. The next generation of AI breakthroughs will come from the right data: expert-curated, domain-specific, and built for the capabilities your model needs most.
It will come from better data. Seldonic was founded on this conviction. Named after Hari Seldon, the mathematician in Asimov's Foundation who predicted the future through the careful study of data, we build the precision datasets and evaluation infrastructure that unlock capabilities no general corpus can provide.
We are not a data labeling company. We are a research lab that treats data as an engineering discipline. Every dataset we produce is designed by domain experts, constructed with scientific rigor, and validated against measurable capability improvements in the models it trains.
"The laws of history are as absolute as the laws of physics, and if the probabilities of error are greater, it is only because history does not deal with as many humans as physics does atoms."
Three core services, each designed to close the gap between where your model is and where it needs to be.
We build high-fidelity datasets for targeted AI capabilities, from molecular reasoning to legal logic chains. Every data point is curated by domain experts, not crowd-sourced annotators. The difference in downstream model performance is significant and measurable.
Custom reinforcement learning environments that test what actually matters. We design reward functions, state spaces, and evaluation harnesses that measure real-world capability gains, not benchmark performance that doesn't transfer to production.
End-to-end advisory from data strategy through deployment. Our team has built production AI systems at OpenAI, Google, Microsoft, Amazon, Scale AI, and Turing. We bring that operational experience to your hardest problems.
We focus on verticals where general data fails and where specialized capability creates outsized value.
Chemistry, physics, and biology reasoning chains validated by PhD researchers
→Contract analysis, regulatory parsing, and multi-jurisdictional logic
→Quantitative reasoning, risk modeling, and market dynamics
→Constraint solving, technical specs, and design optimization
→Clinical reasoning, drug discovery, and diagnostic pathways
→Vulnerability detection, architecture reasoning, and secure coding
→Satellite analysis, climate modeling, and geographic inference
→Pedagogical data, misconception mapping, and scaffolded explanations
→A rigorous pipeline designed for AI companies operating at the frontier.
We analyze your model's failure modes and map the specific capabilities that need data support. Systematic diagnosis, not guesswork.
We recruit domain experts (PhD researchers, practitioners, specialists) who understand nuances that general annotators miss.
Expert-generated data with multi-layer quality assurance, paired with RL environments to measure impact.
Continuous feedback between data quality and model performance. We ship when the numbers prove the improvement.
Our team has built production AI systems at the companies defining the field.
Azure AI, Copilot infrastructure
Alexa AI, AWS ML services
Data operations, RLHF pipelines
AI-native engineering at scale
Research, alignment, safety
Foundation models, research
The web has been crawled. Public datasets are exhausted. The next capability gains require purpose-built, expert-curated data that teaches models to reason in domains where generic corpora are noise.
You can't improve what you can't measure. Our RL environments and custom benchmarks provide ground truth on actual capability improvement, not leaderboard gaming.
A PhD chemist annotating molecular reasoning data produces fundamentally different quality than a general annotator. We've built the pipelines to make expert data economically viable.
Intelligent data scaling means investing in precision over quantity. One expert-verified reasoning chain teaches a model more than a thousand scraped web pages. We've seen this at OpenAI, Google, and Scale AI, and now we build it for you.
Tell us about your capability gaps. We'll map a data strategy in 48 hours.