They rebuilt our retrieval layer in six weeks and the answers finally cite the right documents. Our analysts trust it now.
We build the neural engines behind the most demanding operations.
Northforge builds and runs private AI for regulated operators. Twenty-year-old platforms get agents, retrieval and forecasting layered on top, and the data never leaves the client's perimeter.
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Northforge started from one premise: the enterprises that win the next decade will be the ones that keep control of their own data. We began as four researchers in Portland with a narrow obsession: making old, load-bearing systems talk to modern models without a rewrite.
Over the years that turned into an agency that treats AI as the operating layer of a company rather than a feature. We have shipped production architectures for healthcare and retail groups, and each one taught us something we folded back into the next. What has not changed: every model is explainable to the people it affects.
We give global leaders private, scalable neural layers.
Old platforms, new capability. We add agents, retrieval and forecasting to the systems a regulated firm already trusts, and we do it inside its own security perimeter.
AI Roadmapping
We write the roadmap for your AI programme, from data sovereignty to the order in which systems get automated.
On-Site Inference
Inference at the source, so latency stays low and sensitive data never has to travel.
Private Models
Language models trained only on your data for accuracy inside your industry's vocabulary.
Predictive Operations
Supply and demand engines that forecast the next problem before it reaches the floor.
Enterprises that run on the neural architectures and agent workflows we build for them.
Rare combination: deep model work and a team that actually ships. The agent pipeline went live ahead of the board date.
The forecasting agents cut our manual reconciliation hours by roughly half in the first quarter. Nobody misses the spreadsheets.
We got a roadmap we could defend to finance, not a demo. Every phase had a number attached to it.
They rebuilt our retrieval layer in six weeks and the answers finally cite the right documents. Our analysts trust it now.
Rare combination: deep model work and a team that actually ships. The agent pipeline went live ahead of the board date.
The forecasting agents cut our manual reconciliation hours by roughly half in the first quarter. Nobody misses the spreadsheets.
We got a roadmap we could defend to finance, not a demo. Every phase had a number attached to it.
Straight answers on specifications, deployment timelines and how we handle your data.
Everything you need to know before we build.
Talk to UsEverything runs inside your own cloud account or on-premise cluster. We work with self-hosted vector stores and models, and nothing leaves your network boundary without a written exception.
A discovery sprint takes two weeks. Most first deployments land in six to ten weeks depending on how many source systems need connectors and review gates.
Yes. We connect through your existing APIs, event streams and warehouses rather than replacing them, and we hand over the connectors as documented code.
When it moves a metric. We start with retrieval and orchestration, then fine-tune only the components where a custom model beats a general one on your evaluation set.
Every phase is tied to an operating metric you already track, such as handling time, error rate or throughput, and we report against it monthly.
You do. Everything we build is delivered under your repository and your licences, with no runtime dependency on us.
Open-weight models for on-premise work, plus the major hosted providers where the data policy allows. We pick per workload, not per preference.