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Tailored AI analysis
for growing organizations.

Many teams already pay for an AI assistant and use it as they go. Few know which of their work it handles well, where it quietly gets things wrong, or whether a different model, tool or setup would do better.

You don’t need to know what to ask, and you don’t need an AI team. Start by telling us how your team works today. We work out with you what is worth testing, then measure it on your real work.

Who we work with

Wherever AI meets
real work.

Teams changing their workflows

Moving real work onto AI models and agents, and needing to know what holds up as models change.

Changing workflows ↓

Teams running their own models

Serving open-weight models on your own GPUs. We measure which model, serving engine and settings deliver the answers, speed and cost you need on your hardware.

Running your own models ↓

Teams changing their workflows

Deploying AI is not
deploying software.

Box CEO Aaron Levie put it plainly: changing workflows for AI takes far more work than anyone expects. Workflows need re-engineering for agents, evaluations need to be built and maintained, and the whole system needs updating every time a new model arrives.

If your organization is in the middle of that change, talk to us. Measuring what works, and checking it still works after every model update, is what RadianVector tools are built for.

LinkedIn post by Aaron Levie, CEO of Box: There's a huge opportunity right now in being the deployment layer for AI into the economy. The amount of work it takes to change out workflows in enterprises tends to be far greater than anyone realizes or would prefer.
Aaron Levie, CEO of Box, on LinkedIn ↗
Post on X by Brian Armstrong, CEO of Coinbase: We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at Coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case). I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves. The attached chart shows the fine-tuned model improving fraud detection over each comparison model, including an Opus 4.5 decision agent.
Brian Armstrong, CEO of Coinbase, on X ↗
Teams running their own models

Open models can win.
When they are tuned well.

Coinbase fine-tuned Qwen3.5-9B, an open-weight model, on its own data. For fraud prevention it outperformed frontier models at much lower cost.

Running your own model is where RadianVector tools go furthest. A hosted API exposes a few settings; your own stack exposes every one of them. We measure which combination delivers the answers, speed and cost you need, on your hardware and your workload.

  • vLLM · SGLang · TensorRT-LLM
  • Quantization
  • KV-cache precision
  • Speculative decoding
  • Batching & concurrency
  • KV-cache offload & preemption
  • Temperature · top-k · top-p
  • Reasoning budget

See every setting we tune →   Our Qwen benchmark →

Start a conversation

Want to understand your AI better?
Let’s measure it.

Tell us about your organization and how you use AI today. We’ll suggest what is worth looking at first.