Velos (formerly GradientJ) vs Stellaris AI

Velos is a managed automation platform for back-office teams that want to replace outsourced manual work with software. It targets insurance carriers, MGAs, and finance operations that still rely on BPOs or internal staff for document-heavy workflows like bordereaux, policy servicing, and month-end close.

The company learns your process rules, tests against your real data, and turns recurring work into auditable workflows that combine code with large language models. Velos handles design, deployment, and ongoing management so teams get faster turnaround without adding headcount every time volume spikes.

It is built for organizations handling sensitive, high-volume operations where accuracy matters. Customers include commercial insurance teams, private equity firms, and fractional CFO shops looking to automate multi-hour processes that used to require offshore teams or manual spreadsheets.

Stellaris AI builds large language models marketed around native safety and human-like reasoning for real-world tasks. Its flagship SGPT line targets text and code generation, knowledge Q&A, logical reasoning, and analytics at a scale the company describes as hundreds of billions of parameters. The public site centers on a waitlist for SGPT-4.5 rather than a self-serve chat product you can open today.

Where many LLM labs bolt safety filters on after training, Stellaris AI frames safety as part of the model stack through strict source referencing and harm minimization in the architecture. It also highlights Real-time Context Learning (RCL) for adapting answers with live knowledge, a combination aimed at teams that want cited outputs instead of unchecked generation.

Researchers, enterprise AI teams, and early adopters join the SGPT-4.5 waitlist for first access. The company cites 10+ years of research and three core product pillars: Stellaris GPT, Native Safety, and RCL.