You are not the product. Your data isn’t either.

Advanced AI. Zero data exposure.

With our PRAVITE solution, deploy high-performance systems that are fully private, fully controlled, and completely offline.

Private + AI = PRAVITE

  • Offline LLM Deployments
  • Hardware + RAG + Secure Environments
  • NDA / Data Sensitivity Constraint Solutions

Taken together, offline LLM deployments, hardware-integrated RAG systems, and secure operating environments form a cohesive architecture that redefines how organizations adopt AI. Instead of trading privacy for capability, companies gain both: high-performance, domain-aware intelligence operating entirely within their control. This model aligns directly with the needs of data-sensitive industries, enabling them to unlock AI-driven efficiency and insight while maintaining strict adherence to confidentiality, compliance, and operational integrity. It is not an incremental improvement over cloud AI—it is a fundamentally different paradigm, built around ownership, security, and trust.

The Process

We start with a straightforward conversation to understand your organization, your data, and your constraints. What you can and cannot do from a security, contractual, and operational standpoint. From there, we assess your current workflows and identify where a private AI solution can deliver meaningful value, then outline practical options tailored to your environment, whether that’s a simple workstation setup or a more robust internal system. If you choose to move forward, we handle the setup, guide the integration into your existing processes, and work with your data in a controlled and secure manner to ensure the system is relevant and useful from day one. The result is a fully deployed solution that fits your needs, operates within your boundaries, and is ready for your team to use with confidence.

Offline LLM Deployments

Offline large language model (LLM) deployments shift AI from a cloud-dependent service to a self-contained capability operating entirely within a client’s controlled environment. This architecture eliminates external API calls, internet exposure, and third-party data handling, which are the primary vectors for data leakage and compliance risk. Practically, this means models are hosted on local or on-premise infrastructure (typically either a dedicated workstation or an internal server node) allowing teams to interact with advanced AI while maintaining strict network isolation (air-gapped by default). For organizations, especially in medical, legal, or those regulated through privacy agreements and NDAs, this translates to deterministic control over model behavior, data residency, and system availability, independent of vendor uptime or policy changes.

Hardware + RAG + Secure Environments

A robust offline AI solution is not just the model, it is the integration of purpose built hardware, retrieval augmented generation (RAG), and a secure data environment. Hardware is optimized for inference workloads (GPU, VRAM, high-speed NVMe, and memory bandwidth), ensuring responsiveness and scalability. RAG layers in domain-specific intelligence by indexing internal documents, specifications, contracts, datasets, etc., into a vector database, allowing the LLM to retrieve and reason over authoritative, organization specific content. When combined with controlled access environments (role-based permissions, encrypted storage, segmented networks, offline workstation), this forms a closed-loop system where sensitive data is ingested, processed, and queried without ever leaving the organization’s infrastructure. The result is not just an AI tool, but an internal knowledge system that is both performant and secure.

NDA / Data Sensitivity Constraint Solutions

Organizations operating under strict non-disclosure agreements (NDAs) or handling sensitive data, such as proprietary designs, financial records, or client-owned intellectual property, face a fundamental barrier to adopting cloud-based AI. Standard SaaS models often conflict with contractual obligations, regulatory requirements, or internal risk thresholds. Offline AI systems resolve this tension by ensuring that all data interactions remain within the client’s legal and technical boundary. This is particularly critical for legal practices, healthcare providers, and defense-related industries, where even minimal data exposure can carry significant liability. By deploying AI locally, these clients can leverage advanced capabilities (summarization, search, analysis, automation) without renegotiating contracts, compromising compliance, or introducing unacceptable risk.

Need to Know

The metrics, services, and values of working with inspiraSCAN.

Consultation

We begin with a focused discussion to understand your organization, goals, and constraints.

Assessment

We evaluate your workflows to identify where private AI can provide real value.

Data Review

We examine the types and sensitivity of your data to ensure proper handling and alignment.

Case Definition

We define practical, high-impact applications tailored to your operations.

Solution Design

We outline a customized approach that fits your technical environment and requirements.

Options

We provide multiple implementation paths, from lightweight setups to full-scale systems.

Hardware

We configure the necessary systems to ensure performance and reliability.

Secure Processing

Your data is handled in a controlled, private environment at all times.

Deployment

We implement and integrate the solution into your existing workflows.

User Enablement

We guide your team to ensure they can effectively use the system from day one.

Support

We remain available to refine, adjust, and improve the system as your needs evolve.

Value

The result is a sustainable, private AI capability built to grow with your organization.

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