Use case · Private, on-prem AI

Private AI that runs on 100% local models. Your data stays in-house.

Most AI tools are a pipe to someone else's cloud model. NodePlus is the opposite: the engine runs on hardware you control, so your documents, records, and code never leave the building.

No per-token bills, no third-party AI provider in the loop, and no trade-off in quality. The same local engine posts frontier-class results on public coding and memory benchmarks.

What in-house AI gets you
100%
local models, no third-party AI provider
$0
per-token bills, flat recurring subscription
66.6%
SWE-bench Pro, frontier-class on local models
§ IThe trade nobody should make

Cloud AI asks you to hand over your data to use it

The default way to adopt AI is to send your most sensitive data to a model you do not control, and pay by the token for the privilege. For many operators, that is not an option.

  • Documents, records, and source code are sent to a third-party model for every request.
  • Residency, retention, and confidentiality obligations rule out public AI APIs outright.
  • Per-token pricing makes real usage an unpredictable, metered cost.
  • Vendor lock-in follows: your workflows come to depend on one provider you cannot audit.
  • The common workaround, a smaller local model bolted on, gives up too much quality to trust.
  • So the highest-value use cases, the ones touching real data, are the ones teams cannot run.
§ IIHow NodePlus is built

The whole engine runs where your data already is

NodePlus keeps inference, reasoning, and code generation on hardware you control, then connects to your systems locally. Nothing is sent out.

On your hardware

The models run in your building or your isolated tenant. Data is read in place, never shipped out for inference.

Frontier-class quality

66.6% on SWE-bench Pro and 93.8% on LongMemEval, on local models. Local does not mean weaker here.

No per-token bills

A flat subscription instead of metered inference. Cost does not scale with how much you use it.

Yours to audit

No opaque third-party provider in the loop. You control the stack, the data boundary, and the audit trail.

Connects your systems

Reads across finance, sales, operations, and HR, and builds the connectors between them locally.

No vendor lock-in

The engine is yours to run. Your workflows do not depend on a cloud provider you cannot leave.

§ IIIThe proof

Local models, measured against the public leaderboards

The claim that a private, local system can match cloud AI only means something with receipts. NodePlus took the public benchmarks to show the engine is real, and ran them end to end with no tokens purchased.

Every system that scores higher on the public coding leaderboard is a closed, paid, cloud model. NodePlus lands among the leaders, entirely on local hardware.
§ QCommon questions

What does private, on-prem AI actually mean here?

The reasoning and coding engine runs on local models on hardware you control, in your building or your isolated tenant. Your documents, records, and code are read in place and never sent to a third-party AI provider for inference.

Do local models mean weaker results?

No. The same local engine scores 66.6% on SWE-bench Pro, a benchmark of real software-engineering tasks, and 93.8% on LongMemEval for long-term recall. Every system that scores higher on the public coding leaderboard is a closed, paid, cloud model.

How is this priced if there are no tokens?

A flat recurring subscription. There are no per-token bills and no metered cloud inference, so cost does not scale with how much you use it. Heavy months cost the same as light ones.

Can it still connect to our existing systems?

Yes. NodePlus reads across the finance, sales, operations, and HR systems you already run, and the coding engine builds the connectors between them. The integration happens locally, not through an outside service.

Who is this for?

Any operator who cannot or will not send their data to a cloud model: regulated businesses, firms with residency obligations, and teams that simply want their AI, and their data, to stay in-house.

Keep your AI, and your data, in-house

Book a briefing and we will scope a private deployment against your systems, all on local models you control.