Private AI on infrastructure you control.
OpenWAS deploys open weight language models inside your cloud or data center, puts one governed gateway in front of every model you use, and builds the AI tools your teams rely on. Then we keep it running.
- Any modelOpen weight or closed, chosen by testing on your own data.
- Your environmentAWS, Azure, Google Cloud, your data center or an air gapped site.
- Fully operatedMonitoring, upgrades, model refreshes and support after launch.
Your data is already flowing into AI. The question is who controls it.
of enterprise employees paste data into generative AI tools, and 71.6% of that access runs through personal accounts the company cannot see.
LayerX, Enterprise AI and SaaS Data Security Report, 2025of organizations that suffered an AI related breach had no proper AI access controls. Shadow AI added about $670,000 to the average breach.
IBM, Cost of a Data Breach Report, 2025of organizations already use open source AI models, and 76% expect to use more of them in the coming years.
McKinsey, Mozilla Foundation and Patrick J. McGovern Foundation, 2025lower cost when a production feature moved from GPT 4 to open small language models, with competitive quality.
Irugalbandara et al., Scaling Down to Scale Up, arXiv 2023What we do
Everything a company needs to use AI privately, from the first decision to daily operation.
AI strategy and assessment
Find the workloads worth automating, classify the data they touch and map the rules they must follow.
Model selection and fine tuning
Test open weight and closed models on your own tasks, review licenses, and adapt the winner to your data.
Private AI infrastructure
GPU capacity, model serving, networking, identity and monitoring, built as code in your own account.
AI gateway and data control
One governed entry point to every model, with masking, routing, budgets, guardrails and audit logs.
AI tools, agents and automation
Assistants over your documents, agents for operations and integrations with the systems you already run.
Managed AI operations
Monitoring, upgrades, model refreshes, evaluation and support, with agreed service levels.
Open and closed models behind one control layer
Open weight models give you ownership and predictable cost. Closed frontier models still lead on some of the hardest tasks. You should be able to use both without losing control of your data.
- Sensitive data stays on models running inside your network
- Personal and secret values are masked before any request leaves
- Every request is authenticated, budgeted and recorded
- Switching models becomes a configuration change, not a rebuild
What teams build with private AI
The strongest returns usually come from back office work: documents, checks and requests that take people hours every day.
Document intake
Read purchase orders, delivery notes and forms, extract the fields and post them to your ERP.
Knowledge assistant
Answers from manuals, procedures and policies, with links to the exact source page.
Report drafting
Turn shift logs, quality data and incident notes into consistent daily and weekly reports.
Invoice matching
Compare invoices with purchase orders and receipts, and flag differences for review.
Close support
Draft variance explanations and reconciliation notes from your ledger data.
Contract and spend review
Find renewal dates, price clauses and commitments across supplier agreements.
Ticket triage
Classify, prioritise and route incoming requests, with customer data kept in house.
Reply drafting
Suggest answers grounded in your help center and past resolutions, for an agent to approve.
Conversation insights
Summarise calls and chats to show recurring problems and product feedback.
Clause review
Compare contracts against your playbook and highlight deviations and missing terms.
Policy search
Answer questions across regulations and internal policies, with citations to each source.
KYC and due diligence
Gather and summarise documents for checks, leaving the decision with your team.
Employee help desk
Answer questions on leave, benefits and travel from your own handbook.
Onboarding
Personalised checklists and answers for new joiners from day one.
Screening support
Summarise applications against role criteria, with people making every hiring decision.
Private code assistant
Code completion and review that never sends your source code outside.
IT service desk
Resolve common requests and draft runbook steps from your own documentation.
Log and incident analysis
Summarise alerts and logs into a clear timeline during incidents.
Deployed where your data has to stay
Your cloud account
AWS, Azure or Google Cloud, in the region you choose, inside your own network.
Your data center
On your GPU servers, sized and configured for the models you need.
Air gapped
Fully offline sites for defense, public sector and critical infrastructure.
Hybrid
Sensitive work stays private while approved tasks use hosted models through the gateway.
Designed for the frameworks your auditors use
We build controls and evidence that support these standards and regulations. Certification and legal responsibility stay with your organization.
- OWASP Top 10 for LLM Applications
- NIST AI Risk Management Framework
- ISO/IEC 42001
- ISO/IEC 27001
- SOC 2
- EU AI Act
- GDPR
- HIPAA
- DORA
- India DPDP Act
Why companies choose OpenWAS
Model neutral
We are not tied to any model maker. We recommend what wins on your data, and design so you can switch later.
You own everything
Infrastructure, code, prompts, evaluation sets and fine tuned weights live in your accounts.
Open foundations
Built on proven open source such as vLLM, SGLang and Kubernetes, with no proprietary runtime to license.
Engineers end to end
The people who scope your project are the people who build and run it.
Start with one workload.
Tell us what you want to automate and where your data has to stay.