Foundation models, tailored.
We train language models around your domain and workflow. Starting with expert feedback and evaluation-first development, each model is aligned to the work it needs to do.
Own your frontier model, build your moat.
Train and deploy LLMs that are sharper, cheaper, and faster for your workflows on a platform designed for continual learning with minimal engineering lift.
RL that actually works.
The models you deploy are only the starting point. They learn from feedback and production signals, improving without a heavy traditional development cycle.
Transparent & reliable AI.
Open-weight systems make interpretability practical, while enterprise-grade deployment keeps your API available for high-stakes workflows.
Research.
BYO SWE-grep: automatically train blazing-fast search sub-agents on your knowledge base
Search sub-agents that learn the structure of your internal knowledge for fast, reliable retrieval.
Purpose-built LLMs for dental note-taking
Frontier-style reasoning performance at a fraction of the latency.
Lumina: building self-improving evaluation through customer-in-the-loop refinement
An adaptive evaluation engine that learns to judge like a subject matter expert.
Attention-based attribution: what your model is actually looking at
Interpretable signals for understanding model behavior.
Upweight the strategy, not the tokens
Rationale-guided training teaches the why, not just the what.
Robust, sample-efficient SFT with prompt mutations
Low-divergence prompt variation for better performance at lower cost.
Training loss predicts evaluation performance
A practical evaluation signal for non-verifiable tasks.
Technical, academic roots meet real-world builders.
Engineering PhD candidate at Oxford. Rhodes Scholar. Former medical doctor and elite pole vaulter for Australia.
LLM, RL, and interpretability researcher. Previously worked across NASA, medical AI, and quantitative trading.
Rhodes Scholar and computational neuroscience PhD candidate studying reasoning in natural intelligence.
Software engineer with quantitative trading experience and a history of building and scaling SaaS products.
Mathematics background spanning Cambridge, USyd, quantitative research, and competitive problem solving.
Previously led historical data systems in quantitative trading. Background in systems and applied engineering.
We're building for mission-critical use cases.
sunbow.ai is built for work where better AI can materially improve outcomes. The team combines deep research expertise with practical engineering for customers who need reliable systems.
Explore rolesStart owning your model today.
From training to deployment, launch a specialist LLM that outperforms generic models, adapts over time, and runs reliably at scale.
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