Welcome to the official Name Not Found organization on Hugging Face
Name Not Found is building practical long-context AI systems for teams that need to reason across massive amounts of information without breaking unit economics.
Our work focuses on making large-scale context processing useful, deployable, and commercially viable for real businesses. We are especially interested in workloads where companies repeatedly process large volumes of documents, records, code, research, logs, or operational data and need answers that are grounded in the full context.
We are a small, scrappy team working in public where it makes sense and partnering privately with organizations that have large, high-value knowledge problems.
Featured Models and Tools
NNF-CR
NNF-CR is our context reasoning model designed for large-context workloads where the goal is not just retrieval, but reasoning across a large body of information.
It is built for use cases such as:
- Reviewing large document collections
- Synthesizing evidence across many sources
- Finding patterns across historical records
- Answering complex questions over company-specific data
- Supporting workflows where human teams currently perform repetitive review
The public model is intended to demonstrate the direction of our research and give developers a way to explore the system. Enterprise deployments are customized for client-specific data, workflows, and performance requirements.
What We Are Exploring
We are interested in a new category of AI systems that can process large context at scale with better economics than traditional quadratic attention approaches.
Our focus areas include:
- Long-context reasoning
- Enterprise knowledge systems
- Repetitive document review
- Large-scale research synthesis
- Private deployment workflows
- Client-specific model adaptation
- Efficient inference for large-context workloads
Example Use Cases
We are especially excited about workflows where large amounts of new information arrive continuously and need to be reviewed, understood, or entered into downstream systems.
Examples include:
- Healthcare records and insurance documentation
- Legal discovery and contract review
- Financial filings and diligence materials
- Scientific literature and research corpora
- Enterprise support tickets and internal knowledge bases
- Codebases, issues, pull requests, and engineering history
- Compliance, audit, and operational review workflows
Enterprise Access
For companies with high-volume knowledge workflows, we offer private deployments and custom integrations.
Enterprise deployments can be designed around:
- A client’s proprietary data
- Private infrastructure requirements
- Custom workflows and approval layers
- Domain-specific evaluation criteria
- Human-in-the-loop review
- Ongoing adaptation to company-specific processes
If your team has a large-context problem that is expensive, repetitive, or impossible to solve with current AI systems, we would like to hear from you.
Open Research and Community Resources
We will use this organization to share:
- Model releases
- Technical notes
- Demos
- Evaluation results
- Example datasets
- Spaces
- Developer resources
As a small team, we may not publish everything at once. Our goal is to make the important pieces easy to find and to give builders, researchers, and enterprise teams a clear path to engage with the work.
Get Involved
Follow the organization for upcoming releases, demos, and technical updates.
For enterprise access, partnerships, or early evaluations, reach out through the contact information listed on our Hugging Face profile. email: ai@namenotfound.ai