Unpublished research run through a cloud API is data you've handed to a third party before you've published a word. We'll build the whole system on your own hardware instead: the agents, the orchestration, and the models that carry your lab's real work, and your unpublished data never leaves the building.
Every deployment starts on-site, with the people who do the work.
Publication priority, data provenance, and grant compliance all depend on controlling where your research data is processed.
When a researcher submits unpublished data to a cloud AI service for analysis, summarization, or writing assistance, that data leaves the institutional network. It is transmitted to a third-party data center, processed on shared infrastructure, and handled according to the provider's data policies - not your institution's. For research teams working toward publication, patent filing, or grant deliverables, this creates multiple risks.
First, intellectual property exposure. Even if a cloud provider's policy states they won't use your data for training, the data has still been processed on their infrastructure. If a competing lab later publishes similar findings, questions about data leakage become harder to dismiss when your data was processed through a shared service. This IP exposure risk is why research labs join law firms, medical practices, and defense contractors in moving AI inference on-premises.
Second, grant compliance. Federal agencies including NIH, NSF, and DoD are tightening data management requirements. Data management plans increasingly require documenting where and how research data is processed. Cloud AI processing adds a third-party data handler to your compliance documentation.
Third, operational dependency. Cloud API rate limits, outages, and model deprecations can disrupt research workflows at critical moments. When you're running a week-long analysis pipeline or processing a dataset under deadline, your work depends on a service you don't control.
Your data stays in your building. Your inference runs on your schedule. Your models stay available on your terms.
All AI processing happens on a physical server in your lab, department, or institutional data center. Unpublished data, proprietary datasets, and pre-publication analyses never leave your network. No third-party data handling policies apply.
No per-token charges. No rate limits. No usage caps. Run extended analysis sessions, process entire datasets, and iterate on research questions without watching a billing dashboard. The hardware runs as much as you need it to.
All pre-installed models carry permissive open-source licenses with open weights. You can inspect model architectures, examine parameters, and document exactly which model in which configuration produced your results. Full methodological transparency for reproducible research.
A general model knows science the way a textbook does. In the abstract, sourced from everyone, tuned to no one. It's never read your instrument logs, doesn't know your bench protocols, can't tell your PI's shorthand from a rotating grad student's. And it's never once laid eyes on your unpublished results. That's the gap we close.
Island Mountain builds the agents and the orchestration that carry the multi-step work that's eating your lab's hours: literature synthesis across a stack of papers, protocol and methods drafting, grant narratives, data-analysis summaries, experiment write-ups, and the IRB and IACUC paperwork nobody volunteers for. It runs on current open-weight models like Kimi K3, ZAI GLM 5.2, Qwen 3.8+, and Nous Hermes 4, served through Ollama or vLLM behind an OpenWebUI front end, or whatever open weights fit the task. All of it governed by our own agentic orchestration and the Woven Security Fabric, so every action carries an identity, an approval, and a receipt, and the credentials expire when the task is done. Your unpublished data never leaves the building to make any of it happen.
When the work needs a model that speaks your science, we'll build one. Reasoning models and small language models from scratch, trained to orbit your lab's ontology: your instruments, your protocols, your prior results, the way your group really writes. It picks up your terminology and your cadence, the notation you've leaned on since the first grant, not a generic survey of the field.
It air-gaps completely for the work that demands it: the embargoed dataset, the sponsor's proprietary compound, the findings that aren't published yet. And it reaches the cloud only when a task genuinely needs to. You draw that line, workflow by workflow, and it's yours to move.
Then we hand back the keys. On-site onboarding, SOPs written for your people, the whole thing dropped into your team's hands so they run and rework the workflows without paging us. Want the upkeep off the lab manager's desk? We offer tiered support sized to the lab, and we plug in wherever that's easiest to carry. A lab is a body of work built over years, and the tooling should belong to the institution that outlasts any single grant. We build it alongside you, and we won't call it done until your people can run it without us.
These aren't chat prompts. They're agentic workflows the orchestration runs start to finish, each step carrying its own identity and receipt, and your unpublished data never leaves your network.
Process and synthesize large volumes of published literature. Identify themes across papers, summarize findings, compare methodologies, and generate structured literature review drafts. Long context windows hold a whole stack of sources in a single session, so the analysis spans them at once.
Code interview transcripts, identify themes in open-ended survey responses, and analyze field notes. Process sensitive participant data - including IRB-protected information - without transmitting it to cloud services.
Generate first drafts of specific aims, research plans, significance sections, and budget justifications. Iterate on proposal language with unlimited prompting. Process preliminary data descriptions without exposing unpublished findings.
Assist with data labeling, classification tasks, and annotation workflows. Process proprietary datasets for categorization, tagging, and structured extraction without uploading them to third-party annotation platforms.
Generate Python, R, and statistical analysis code. Draft data processing pipelines, create visualization scripts, and write analysis functions. Iterate on code with the full context of your research question without exposing methodology to cloud providers.
Draft manuscript sections, restructure arguments, improve clarity, and format content for specific journal requirements. The system turns out clean academic prose. Process entire manuscripts locally for revision and editing support.
Current open-weight models carry long context windows, and the strongest of them stretch well past what most cloud APIs will hand you. That matters for full-dataset analysis, long-document synthesis, and research sessions that hold a whole body of source material in view at once. We run the model that fits the job, and the context budget climbs as the open weights do.
Every model on Island Mountain hardware carries a permissive open-source license with open weights. You can inspect the model architecture, examine weight distributions, fine-tune on domain-specific data (with additional tooling), and document your exact model configuration for reproducibility. This is impossible with closed commercial APIs where the model is proprietary and opaque.
Research requires reproducible methods. When you use a cloud API, the provider can change the model at any time - versioning is not guaranteed, and behavior may shift between calls. With local hardware, you control which model version runs. Your results are reproducible because the model stays the same until you choose to update it.
For research involving human subjects, IRB protocols require documenting data handling procedures. Local AI simplifies this documentation: "AI analysis was performed on institution-owned hardware with no external data transmission." This is a cleaner data governance story than explaining cloud provider data handling policies, BAAs, and third-party risk assessments.
Heavy-usage research scenario: 3 researchers running 40-hour weeks with sustained AI-assisted analysis.
| Cloud AI API (3 Researchers) | Island Mountain build | |
|---|---|---|
| Monthly Inference Cost | $750 - $12,000 | $0 after purchase |
| Year 1 Cost | $9,000 - $144,000 | One-time purchase (quoted to the deployment) |
| Year 2 Cumulative | $18,000 - $288,000 | Electricity only (~$1,200 - $2,400/yr) |
| Unpublished Data Exposure | Every session transmits data externally | Zero. Data stays in your lab. |
| Rate Limits | API rate limits apply | None. Your hardware, your schedule. |
| Model Transparency | Closed, proprietary models | Open weights. Permissive licenses. |
| Reproducibility | Model may change without notice | You control the model version. |
| Grant Compliance | Third-party data processing | Institution-owned infrastructure. |
Knowing where the tool ends prevents misapplication.
Island Mountain hardware is built for inference - running pre-trained models to generate text, analyze documents, and assist with writing. It is not a GPU compute cluster for model training, large-scale numerical simulation, or HPC workloads. It does not replace your institution's research computing infrastructure.
The system ships configured for inference. Fine-tuning models on domain-specific data requires additional tooling, expertise, and potentially more GPU memory than inference alone. We can consult on fine-tuning approaches, but it is not a plug-and-play feature at delivery.
The system does not connect to PubMed, Web of Science, institutional repositories, or research data management platforms. The AI works with text you provide to it through the browser interface. Data transfer between research systems and the AI is manual.
After the 30-day support period, your lab's IT support or institutional research computing staff handles system maintenance. This is standard Linux server administration. Most university IT departments are well-equipped for this.
Power & Installation: All Island Mountain systems require a dedicated 208V/30A power circuit (NEMA L6-30R). University server rooms and departmental data closets typically have this infrastructure. The system fits in a standard 4U rack space. Average power draw under typical inference loads is 1.5-2.5 kW. 30 days of remote setup support are included, and we coordinate with institutional IT staff for network integration.
Yes. Cloud AI transmits unpublished research data to third-party infrastructure, creating IP exposure and provenance liability for patentable discoveries. For labs handling data subject to 21 CFR Part 11, IRB protocols, or GxP compliance requirements, third-party processing introduces regulatory complications. On-premises AI from Island Mountain eliminates this risk entirely.
Local AI satisfies data residency requirements with a simple documentation posture. NIH, NSF, and DoD increasingly require data management plans specifying where grant-funded data is processed and stored. With Island Mountain hardware, you document that AI-assisted analysis occurred on institution-owned hardware within your facility, with no external data transmission.
Yes. All models pre-installed on Island Mountain hardware are open-weight with permissive licenses (MIT, Apache 2.0). Researchers can inspect architectures, examine weight distributions, and modify models for specific use cases. This satisfies IRB requirements for methodological transparency and GxP compliance requirements for process documentation.
Cloud AI APIs charge $15 to $60 per million tokens. A three-researcher lab with heavy usage processes 50 to 200 million tokens per month, costing $750 to $12,000 monthly. An Island Mountain build is a one-time purchase, sized and quoted to your workload, with unlimited inference. Cost parity typically reached within 12 to 18 months.
University research lab protecting unpublished grant-funded IP. Pre-publication analysis stays on our hardware, not cloud servers.
Scenario: Research UniversityGenomics lab running sequence analysis with LLM-assisted annotation. IRB requires all patient-derived data stays on institutional infrastructure.
Scenario: Biomedical Research LabMaterials science team processing proprietary compound data. Corporate sponsor IP agreements prohibit cloud AI. Local inference solved it.
Scenario: Materials Science LabOne conversation. No sales pitch. Tell us about your lab's AI workload and data requirements, and we will spec the right system.
Or call directly: 1-341-441-8740
See all eleven industries we serve or explore: Tribal Nations · Defense Contractors