Services
Implementation scope and rollout planning
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Deploy fully isolated AI assistants that keep all data, models, and inference securely within your corporate network.
Deploy a secure, air-gapped AI assistant in under 4 weeks, ensuring zero data egress and full compliance with strict data sovereignty mandates.
We engineer end-to-end solutions where all data, models, and inference remain on-premises. This eliminates cloud data transfer risks and provides ironclad IP protection for your proprietary workflows and datasets.
This service is part of our broader Enterprise AI Copilot Customization pillar, which also includes solutions for Legacy ERP AI Copilot Integration and Proprietary Software AI Overlay Engineering. For the highest security requirements, explore our Confidential Computing for AI Workloads services.
Deploying a secure, internal AI assistant delivers measurable business value by protecting intellectual property, accelerating workflows, and ensuring compliance. Our air-gapped solutions guarantee data never leaves your network.
All model inference, training data, and user interactions remain within your corporate firewall. Eliminate data leakage risks and meet strict data residency requirements for finance, healthcare, and government sectors.
Reduce time spent searching internal wikis, databases, and legacy systems. Employees get instant, conversational answers from proprietary data, cutting research time by over 60%. Learn more about our approach to Enterprise Search and Retrieval AI.
Sensitive R&D data, proprietary code, and strategic documents are used to train and power the assistant without exposure to third-party APIs. This is a core component of our Sovereign AI Infrastructure Development practice.
Built-in audit trails, access controls, and policy enforcement ensure compliance with frameworks like HIPAA, FINRA, GDPR, and the EU AI Act from day one. Explore our technical frameworks for Enterprise AI Governance.
Eliminate dependency on external AI service outages, API rate limits, and pricing changes. Maintain business continuity with a fully controlled, high-availability system that integrates with your existing AIOps monitoring.
Train the assistant on your unique corporate corpus—legal documents, engineering specs, support tickets—to provide expert-level guidance, reducing bottlenecks and preserving institutional knowledge. This is powered by our Domain-Specific Language Model (DSLM) Training capabilities.
Our proven methodology for deploying secure internal AI assistants ensures a controlled, low-risk implementation with clear deliverables at each phase. This timeline is typical for a mid-sized enterprise with a single data source.
| Phase & Key Activities | Timeline | Core Deliverables | Client Involvement |
|---|---|---|---|
Phase 1: Discovery & Architecture Design
| 1-2 Weeks |
| Stakeholder workshops Provide security policies Grant infrastructure access |
Phase 2: Secure Environment Provisioning
| 2-3 Weeks |
| Approve network design Provide security certificates Validate internal access |
Phase 3: Model Selection & Data Pipeline Integration
| 3-4 Weeks |
| Approve model selection Validate data source connections Review initial query responses |
Phase 4: Assistant Development & Security Hardening
| 3-5 Weeks |
| Participate in UI/UX review Define user roles & permissions Approve security test results |
Phase 5: Pilot Deployment & User Training
| 2 Weeks |
| Identify pilot group Participate in training sessions Provide structured feedback |
Phase 6: Full Rollout & Handover
| 1-2 Weeks |
| Approve rollout schedule Final acceptance testing Sign-off on documentation |
Our deployment architecture ensures all data, models, and inference remain within your corporate network, meeting the highest standards for data sovereignty and intellectual property protection.
Full-stack deployment of your AI assistant within your data center or approved private cloud (AWS GovCloud, Azure Government). We manage the entire lifecycle—from initial provisioning to ongoing updates—without any data ever leaving your controlled environment.
Implement encryption for data at rest, in transit, and in use via hardware-based Trusted Execution Environments (TEEs). Integrates with your existing HSM and key management systems for a defense-in-depth security posture.
Proactive monitoring and governance to detect and manage any unsanctioned AI usage or configuration drift. Our systems provide continuous vulnerability assessment against frameworks like MITRE ATLAS to defend against novel AI-specific threats.
Engineered data pipelines ensure proprietary training data and model outputs are strictly confined within sovereign borders. Enables safe contribution to global federated learning models without raw data exchange, crucial for multinationals.
Deploy on infrastructure that meets FedRAMP, SOC 2 Type II, and ISO 27001 standards. We facilitate third-party security audits (e.g., Trail of Bits) and provide penetration testing reports to validate the security of your AI deployment.
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Common questions from CTOs and security leaders about deploying secure, air-gapped AI assistants within corporate networks.
A standard deployment for a secure, air-gapped AI assistant takes 2-4 weeks from kickoff to production handoff. This timeline includes environment provisioning, model containerization, RAG pipeline integration, and security hardening. Complex integrations with multiple legacy ERPs or proprietary databases can extend this to 6-8 weeks. We provide a fixed-scope project plan during the discovery phase.