We're building transparent AI infrastructure for organizations that need to understand and control their AI systems. Open weights, documented architectures, and enterprise-grade deployment.
NAPH was founded on a straightforward premise: organizations adopting AI should be able to audit, modify, and fully control the systems they depend on. When a model processes your most sensitive data, you shouldn't have to trust a black box.
We develop large language models that match the capabilities of leading closed-source systems while providing complete transparency into their architecture, training methodology, and behavior. Our models are open-weight, meaning you can download the full parameters, deploy on your own infrastructure, and fine-tune on your proprietary data.
This isn't about ideology. It's about practical requirements. Regulated industries need to demonstrate how their AI systems work to auditors. Security-conscious organizations need to process sensitive data without sending it to third-party servers. Engineering teams need to understand model behavior when debugging production issues. Open-weight models address all of these needs.
We focus on building the most capable open-weight models possible, then providing the infrastructure and support that enterprises need to deploy them successfully. This means:
Model development that prioritizes capability, efficiency, and transparency. We publish detailed technical reports on our architecture decisions, training data, and evaluation methodology. When you use a NAPH model, you know exactly what you're working with.
Deployment support that meets enterprise requirements. Our team helps organizations design and implement AI infrastructure that satisfies their security, compliance, and performance needs. Whether that's on-premises, private cloud, or air-gapped environments.
Ongoing research that advances the state of the art in open-weight models. We invest heavily in training efficiency, inference optimization, and safety techniques. Improvements flow into our model releases so customers benefit from continued advancement.
We publish what we know about our models. Architecture details, training methodology, benchmark results, and known limitations. You can audit our work.
Theory matters less than working systems. We focus on models that deploy successfully in production environments with real performance and reliability requirements.
Capable AI systems require careful development. We invest in safety research, red-teaming, and responsible disclosure. Open doesn't mean careless.
Closed-source AI creates dependency. When you build on a proprietary model, you're locked into a vendor's roadmap, pricing decisions, and policy changes. You can't inspect the model's behavior, adapt it to your specific needs, or deploy it without ongoing API calls.
Open-weight models invert this dynamic. You own the weights. You can deploy anywhere. You can fine-tune for your domain. You can switch providers without rebuilding your application. You can run inference without sending data to external servers.
This doesn't mean open-weight is right for everyone. Hosted APIs are simpler for some use cases. But for organizations where control, security, and long-term independence matter, open-weight is the better architecture.
We're committed to developing models that give enterprises a real alternative to closed-source AI. That means matching frontier capabilities, not settling for "good enough for open source." It means providing enterprise-grade support, not just releasing weights and walking away. It means investing in safety and security, not treating openness as an excuse for carelessness.
The AI industry is young. The companies and models that succeed will be the ones that earn trust through consistent delivery and transparent operation. We intend to be one of them.
Location
NAPH is headquartered in Canoga Park, California with a distributed team across the United States.
Interested in partnering with NAPH or discussing enterprise deployment? Get in touch.