Data Privacy
Keep proprietary and regulated data sovereign and secure.
Cloud, on-prem, or at the edge.
Same model, same governance, same control plane — sized and operated for the environment that fits your security, latency, and cost profile.
- On-prem for full data sovereignty
- Private cloud (AWS · Azure · GCP) for elastic scale
- Edge for offline + low-latency environments
At LLM.co, we believe your data should empower your organization—not put it at risk. Whether you're handling contracts, case files, financial records, or internal documentation, our platform ensures that bringing your own data (BYOD) never means giving up control. Deploy AI workflows securely, without exposing sensitive information to public clouds or third-party models.
Private, Encrypted AI That Respects Your Rules
Our BYOD pipeline includes privacy-enhanced RAG systems that search your internal documents in real-time without exposing them to inference APIs or cloud-based transformers. Outputs are traceable, auditable, and grounded in your verified data.
Data Ingestion with Guardrails
Upload PDFs, Word docs, emails, knowledge bases, and structured data with optional client-side encryption. All ingestion points are hardened for compliance.
Vectorization Without Exposure
We convert your documents into vector format using self-hosted or isolated vector databases like FAISS or Chroma. Your data never leaves your environment—ideal for legal privilege, HIPAA, or SOC 2 contexts.
Custom AI Without Public Leakage
Fine-tune or instruct your LLM using isolated datasets. Your knowledge base powers your model—no cross-pollination with anyone else's data.
Why Privacy Matters in Enterprise AI Solutions
In industries like law, finance, healthcare, and government, privacy isn't optional—it's mandated. When using generative AI, you need assurance that your internal documents, client records, and sensitive IP stay confidential and compliant.
No Data Leaks: We never train public models on your data. Period.
Air-Gapped & On-Prem: Run everything inside your firewall or VPC.
Zero Retention: Inputs, prompts, and documents are never stored or reused.
Features Built with Privacy at the Forefront
All of our enterprise, private LLM features are built with a privacy-first stance.
Email/Call/Meeting Summarization: LLM.co enables secure, AI-powered summarization and semantic search across emails, calls, and meeting transcripts—delivering actionable insights without exposing sensitive communications to public AI tools. Deployed on-prem or in your VPC, our platform helps teams extract key takeaways, action items, and context across conversations, all with full traceability and compliance.
Security-first AI Agents: LLM.co delivers private, secure AI agents designed to operate entirely within your infrastructure—on-premise or in a VPC—without exposing sensitive data to public APIs. Each agent is domain-tuned, role-restricted, and fully auditable, enabling safe automation of high-trust tasks in finance, healthcare, law, government, and enterprise IT.
Internal Search: LLM.co delivers private, AI-powered internal search across your documents, emails, knowledge bases, and databases—fully deployed on-premise or in your virtual private cloud. With natural language queries, semantic search, and retrieval-augmented answers grounded in your own data, your team can instantly access critical knowledge without compromising security, compliance, or access control.
Multi-document Q&A: LLM.co enables private, AI-powered question answering across thousands of internal documents—delivering grounded, cited responses from your own data sources. Whether you're working with contracts, research, policies, or technical docs, our system gives you accurate, secure answers in seconds, with zero exposure to third-party AI services.
Custom Chatbots: LLM.co enables fully private, domain-specific AI chatbots trained on your internal documents, support data, and brand voice—deployed securely on-premise or in your VPC. Whether for internal teams or customer-facing portals, our chatbots deliver accurate, on-brand responses using retrieval-augmented generation, role-based access, and full control over tone, behavior, and data exposure.
Offline AI Agents: LLM.co's Offline AI Agents bring the power of secure, domain-tuned language models to fully air-gapped environments—no internet, no cloud, and no data leakage. Designed for defense, healthcare, finance, and other highly regulated sectors, these agents run autonomously on local hardware, enabling intelligent document analysis and task automation entirely within your infrastructure.
Knowledge Base Assistants: LLM.co's Knowledge Base Assistants turn your internal documentation—wikis, SOPs, PDFs, and more—into secure, AI-powered tools your team can query in real time. Deployed privately and trained on your own data, these assistants provide accurate, contextual answers with full source traceability, helping teams work faster without sacrificing compliance or control.
Contract Review: LLM.co delivers private, AI-powered contract review tools that help legal, procurement, and deal teams analyze, summarize, and compare contracts at scale—entirely within your infrastructure. With clause-level extraction, risk flagging, and retrieval-augmented summaries, our platform accelerates legal workflows without compromising data security, compliance, or precision.
Practical Use Cases for Data Privacy
We primarily focus and work with compliance-heavy industries that demand data privacy above all else.
Legal Teams Reviewing Private Case Law & Filings
Law firms and in-house legal departments are under constant pressure to analyze vast quantities of sensitive documents—everything from contracts and NDAs to regulatory filings and litigation records. With LLM.co, legal teams can securely ingest and query their internal case law databases, compare contract language across clients or jurisdictions, and generate summaries or memos without risking confidentiality. The platform supports nuanced searches across discovery files, internal compliance documentation, and privileged communications, enabling attorneys to respond faster and more accurately. Since all data remains securely within the organization's control—whether deployed on-prem or in a VPC—the platform maintains client-attorney privilege and ensures that no sensitive data ever leaves the organization's infrastructure.
Banks Processing Internal Policy Documents
Financial institutions operate under an intense regulatory environment where precision and confidentiality are critical. Banks can use LLM.co to analyze internal policy documents, compliance protocols, operational handbooks, and training guides without exposing proprietary or customer-sensitive information to external services. Risk and compliance teams can ask natural language questions about internal AML procedures, know-your-customer rules, or audit guidelines and receive fast, grounded answers based entirely on the bank's own documentation. Because everything runs within a private, access-controlled environment, LLM.co helps financial institutions avoid costly compliance breaches while reducing the manual burden of navigating thousands of pages of internal procedures.
Healthcare Organizations Querying EHR Systems Securely
Hospitals, healthcare networks, and insurers handle some of the most sensitive personal data available—protected health information governed by HIPAA and other privacy laws. With LLM.co, these organizations can bring their own electronic health records, clinical guidelines, billing codes, and medical research into a secure AI environment. Clinicians and administrative staff can ask complex questions about a patient's history, generate referral summaries, or analyze treatment outcomes without risking data leakage. The system runs entirely within their IT infrastructure or VPC, ensuring that no PHI is exposed to external vendors or APIs. This enables real-time, AI-powered support for diagnosis, triage, and documentation, all while preserving regulatory compliance and patient trust.
Government Teams Navigating Classified Knowledge Bases
Government agencies and defense contractors often work with restricted, confidential, or classified materials where traditional SaaS AI tools simply aren't an option. LLM.co offers a secure, compartmentalized solution that allows these teams to deploy AI locally and interact with internal SOPs, historical memos, mission-critical briefings, and policy documents. Whether the goal is to assist in FOIA request triage, threat intelligence review, or internal investigations, LLM.co allows natural language interaction with sensitive information while enforcing strict access controls and comprehensive audit trails. Because the system can run entirely in air-gapped or SCIF-compliant environments, it supports zero-trust government deployments with no compromise to data security or operational integrity.
Common questions
01How does LLM.co ensure my private data isn't leaked or reused?
LLM.co was built from the ground up with strict data isolation in mind. When you bring your own data—whether it's legal documents, financial reports, or patient records—that data is never used to train public models, stored outside your environment, or shared with any third parties. Our deployments operate entirely within your infrastructure or VPC, and all processing is encrypted end-to-end. Nothing is cached, retained, or exposed without your explicit control. We don't just promise data privacy—we engineer it into every part of the pipeline.
02Can I use LLM.co if I need to comply with HIPAA, GDPR, or SOC 2?
Yes. LLM.co is designed specifically for regulated environments and is fully customizable to meet your compliance requirements. Whether you're operating under HIPAA for healthcare, GDPR for data protection in the EU, or internal audit controls aligned with SOC 2, we support the technical and administrative controls necessary to maintain compliance. From secure access controls and encryption to full audit logging and deployment in private infrastructure, we provide a privacy-preserving AI environment you can trust.
03Is the data stored or processed by LLM.co accessible to your team or to OpenAI/Anthropic/other providers?
No. When deployed in private or on-prem environments, your data is never sent to any external third-party provider—not to OpenAI, not to Anthropic, not even to LLM.co. You remain in full control of where your data lives and how it's accessed. If you use retrieval-augmented generation (RAG), your vector database and embeddings stay within your environment. Our team cannot see or access your documents, prompts, or outputs unless you explicitly invite us for troubleshooting or managed service engagements.
04What kinds of documents or systems can I bring into the platform?
LLM.co supports a wide range of document types and structured data, including PDFs, Word files, PowerPoint decks, spreadsheets, emails, HTML, and CSVs. We also integrate with internal systems like document management platforms, EHRs, CRMs, and ticketing systems. Whether you need to ingest contracts, court filings, operating procedures, or compliance logs, our platform can parse, tokenize, and vectorize it—all without compromising privacy or data sovereignty.
05What's the difference between using LLM.co and a public API like OpenAI or Google Gemini?
The key difference is ownership and control. With LLM.co, you don't send your data out to someone else's cloud—you bring the model to your data. Public APIs are fast and accessible but require uploading your private information to infrastructure you don't control. That means potential retention, surveillance, or model training using your data. LLM.co reverses that model: everything runs privately, with no external dependencies, full encryption, and zero data leakage. You get the benefits of large language models without compromising privacy, compliance, or intellectual property.
06Does LLM.co support CCPA compliance for AI workloads?
Yes. The California Consumer Privacy Act requires that consumers can request deletion of their personal data and that organizations disclose how that data is used. Because LLM.co deployments do not retain prompts, documents, or outputs beyond the session — and never use customer data to train shared models — the technical obligations of CCPA are substantially easier to fulfill. Your team retains full control over what is stored and for how long, enabling you to honor deletion requests without the complexity that arises when data has been shared with a third-party API provider.
07What does the EU AI Act mean for enterprises deploying LLMs in 2026?
The EU AI Act, fully applicable from August 2026, classifies many enterprise AI applications — particularly those used in healthcare, legal, HR, and financial services — as high-risk systems subject to conformity assessments, technical documentation, and transparency obligations. Private deployments that keep data within EU-sovereign infrastructure simplify compliance by eliminating cross-border transfer concerns. LLM.co's on-prem and VPC deployment models, combined with audit logging and access controls, provide the documented control environment that high-risk system conformity assessments require.
08Does LLM.co support data residency requirements for multinational organizations?
Yes. Data residency — the requirement that data be stored and processed within a specific geographic or jurisdictional boundary — is a core architectural consideration for multinational enterprises. LLM.co deployments can be pinned to any region, data center, or on-prem environment your organization controls. Model inference, vector databases, audit logs, and any intermediate representations never leave the designated boundary. This makes it straightforward to demonstrate compliance with GDPR's data-transfer restrictions, sector-specific sovereignty mandates, and internal data-governance policies.
09What is a DPIA, and how does an on-prem LLM deployment affect it?
A Data Protection Impact Assessment (DPIA) is a formal evaluation required under GDPR when processing is likely to result in high risk to individuals' rights — including most enterprise AI use cases involving personal data. On-prem and private-VPC deployments substantially reduce the risk profile documented in a DPIA by eliminating third-party data transfers, restricting access to known personnel, and enabling comprehensive audit trails. LLM.co's governance tooling produces the access logs, retention records, and processing documentation that a completed DPIA typically expects as supporting evidence.
Regulatory Coverage: GDPR, CCPA, HIPAA, and the EU AI Act
Enterprise AI deployments now operate under an overlapping web of data-privacy obligations. GDPR governs any processing of EU residents' personal data and requires lawful basis, purpose limitation, and documented data-subject rights — including the right to erasure. CCPA extends comparable protections to California consumers. HIPAA mandates safeguards for PHI at rest and in transit, with Business Associate Agreements required for any vendor touching clinical data. The EU AI Act, fully applicable from August 2026, adds conformity assessments and transparency obligations for high-risk AI systems — including those used in healthcare, finance, and legal decision support. An on-prem or private-cloud deployment collapses most third-party data-transfer risk by keeping processing entirely within your jurisdiction, simplifying both Data Processing Agreements with regulators and internal DPIAs.
Multinational organizations often face simultaneous obligations across GDPR, CCPA, and sector-specific rules. LLM.co's data-residency controls let you pin model inference, vector storage, and audit logs to a specific geographic boundary — whether an EU-sovereign data center, a US-region VPC, or an air-gapped on-prem rack. This architectural choice is frequently the deciding factor in a DPIA, because it removes cross-border data transfer risk entirely. Our governance and audit tooling produces the evidence trail DPAs and internal compliance teams require.
PII and PHI Redaction Before Inference
A robust LLM data-privacy posture goes beyond deployment topology. Even in a private environment, minimizing the personal data that reaches the model layer reduces residual risk and satisfies data-minimization principles under GDPR and HIPAA. LLM.co supports a pre-inference redaction pipeline that detects and tokenizes PII and PHI — names, dates of birth, SSNs, account numbers, medical record identifiers — before the prompt is submitted to the model. Reversible pseudonymization allows the original values to be restored in the final output while ensuring raw identifiers never cross trust boundaries inside the inference engine. This pattern is especially valuable for RAG workflows where retrieved document chunks may contain incidental personal data alongside the substantive content being queried.
Post-inference, the same pipeline validates that no re-identified personal data surfaces in model responses before they are returned to the application layer. Redaction events and per-request audit records are captured to support DPIA documentation and regulatory reporting. For organizations operating under strict data-minimization mandates — common in enterprise healthcare and financial-services contexts — this layered approach provides a defense-in-depth posture that a deployment boundary alone cannot fully achieve.
Private AI On Your Terms
Tell us your use case and constraints — on-prem, cloud, or edge — and we'll map a compliant deployment within one business day.
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