Answer Hub
What Can On-Box AI Actually Do That Cloud AI Can't?
Sub-second response on routine queries. Local data sovereignty. No per-seat cloud pricing for high-volume users. Offline operation when the network is down. Models tuned to your specific data without leaking it to a cloud vendor. Predictable cost for predictable workloads.
Five Real Differences
First, latency: a local model running on a configured workstation returns a routine answer in under a second. A cloud round-trip is two to ten seconds depending on the network, the queue, and the model. For an inside sales rep parsing fifty RFQs a day, the cumulative time savings is real — and the workflow becomes interactive instead of batch. Second, sovereignty: the prompt, the document attached, and the agent's reasoning never leave the building. For commercial work where the data has compliance implications (DOT pay applications, certified payroll, customer financial records), this matters. Third, cost shape: cloud AI charges per token. A power user querying constantly hits caps fast or pays per-token rates that get expensive at scale. On-box has fixed amortized cost. Fourth, offline: a job site with intermittent connectivity, a service truck in a dead zone, a power outage — the local model keeps working. Fifth, data tuning: models on the box can be fine-tuned against your firm's specific catalog, customer history, and operational patterns without sending the data anywhere.
Where Cloud Still Wins
Frontier reasoning. The on-box models are open-weight (Llama, Qwen, Mistral families); they're good for routine work but they don't yet match GPT-5 or Claude Sonnet 4.6 on hard reasoning, deep code, long context. So we don't pretend they do. The configured engagement uses local models for the high-volume routine work and routes the harder queries to the cloud — Claude, OpenAI, whichever fits the task — with a metered budget. The cloud isn't gone, it's just used deliberately instead of by default.
Frequently Asked Questions
Which open-weight models do you run on the Box?
We evaluate the current generation every few months and roll forward as better ones land. As of mid-2026 the configured stack typically uses a Qwen3 or Llama 4 family model for general inference, a smaller distilled coding model, and an embedding model for retrieval. The choice depends on the role configuration; we don't lock you to a single model family.
How much of the work actually stays on-box?
Depends on the workflow. For inside sales / submittal coordination / aging-quote follow-up, often 80-90% of the inference is local. For deeper reasoning tasks (estimating, complex multi-document synthesis) more of it hits the cloud. The configuration tunes the routing — you don't have to think about it.
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