Neobolt is an AI alignment research laboratory that creates methods to make artificial intelligence systems honest, harmless, and useful by integrating safety into the training process. It also offers production‑grade AI consulting, delivering deployed agents, retrieval‑augmented generation pipelines, and LLMOps that directly improve business metrics, as well as AI‑native software built from the ground up.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises adopting large language models often face risks of unsafe or unaligned behavior, leading to potential misinformation, harmful outputs, or misaligned business actions. Existing AI solutions are typically retrofitted onto legacy systems, lacking built‑in safety guarantees and making it difficult to ensure consistent honesty, harmlessness, and utility.
Solution
Neobolt provides AI alignment research and engineering services that embed safety directly into model training, producing systems that are honest, harmless, and useful by design. The lab delivers production‑grade AI solutions—including autonomous agents, retrieval‑augmented generation pipelines, and LLM operations tooling—that are ready for deployment in enterprise environments. In parallel, Neobolt creates AI‑native software architectures that integrate intelligence from the ground up, avoiding the pitfalls of bolting AI onto existing workflows. By aligning core model objectives during training, Neobolt ensures that deployed AI behaves predictably and supports real business metrics while adhering to safety principles.
Target Audience
Primary customers are medium to large enterprises and product teams that require reliable, safety‑aligned AI capabilities for operational use, as well as developers building new AI‑first software platforms.
Features
- Integrated safety alignment in the training loop targeting honesty, harmlessness, and utility
- Deployable autonomous agents tailored to enterprise tasks
- Retrieval‑augmented generation pipelines that combine external data with LLM reasoning
- End‑to‑end LLMOps tooling for monitoring, versioning, and scaling AI services
- AI‑native software architecture frameworks that embed intelligence at the core of applications
- Production‑ready implementations that directly impact business KPIs