Data locked in silos
Enterprise data sits across dozens of systems, formats, and environments. AI can’t use what it can’t reach - and connecting everything without governance creates its own risks.
Digital Convergence Technologies
DCT Vault™ · Secure Data-to-AI
Most enterprises have the data AI needs. What they lack is a secure, governed way to connect the two.
DCT Vault™ builds the secure data foundation that makes enterprise AI possible - AI-ready pipelines, private model deployment, retrieval architectures, and controlled model integration that ensure your data stays where it belongs while your AI capabilities scale.
AI that can’t access your data isn’t useful. AI that accesses it without controls isn’t safe. Vault solves both problems.
Explore Secure Data-to-AIEnterprise AI runs on data. The problem is that enterprise data is some of the most sensitive, regulated, and operationally critical information that exists - and most AI deployment models weren’t designed with that reality in mind.
Organizations face a difficult choice: limit AI to generic, untrained models that can’t reflect their business context, or expose sensitive data to external systems that create privacy, compliance, and competitive risk.
That tradeoff is false. The right architecture eliminates it.
Enterprise data sits across dozens of systems, formats, and environments. AI can’t use what it can’t reach - and connecting everything without governance creates its own risks.
Regulated industries face strict requirements around how data is processed, stored, and accessed. External AI models that ingest sensitive data create compliance liability that most legal and security teams won’t accept.
Models trained on public data can’t answer questions about your products, your customers, or your operations. Enterprise AI needs enterprise context.
When AI runs on external infrastructure, organizations lose visibility into how their data is being used, stored, and retained.
Vault builds the secure data-to-AI architecture that makes enterprise AI both powerful and trustworthy. It connects your data to your AI capabilities through pipelines, retrieval systems, and deployment models that keep sensitive information under organizational control - without limiting what AI can do with it.
The result is AI that knows your business, answers questions about your operations, and operates within the boundaries your security and compliance teams require.
Design and integrate structured and unstructured data pipelines that prepare enterprise data for AI workloads - cleaning, transforming, and routing information from across the organization into formats AI can actually use.
Deploy AI models on private or controlled infrastructure so enterprise data never leaves organizational boundaries. For use cases where data sensitivity, regulatory requirements, or competitive concerns make external model hosting unacceptable, Vault delivers full AI capability within your own environment.
Implement Retrieval-Augmented Generation (RAG) architectures, vector search, and semantic retrieval systems that give AI models access to your enterprise knowledge without requiring sensitive data to be embedded into model weights or sent to external services.
Prepare, govern, and integrate enterprise data across systems to support reliable, high-quality AI applications. Data quality issues upstream create AI accuracy problems downstream - Vault addresses the foundation so AI performs correctly in production.
Where external AI models are required, Vault implements the safeguards, proxy architectures, and data sanitization layers that ensure proprietary or sensitive information never reaches external systems - even when those systems are part of the workflow.
Unlike approaches that rely solely on large external models, Vault supports deployment of both Large Language Models and optimized Small Language Models (SLMs) - selecting the right model for each use case based on performance, cost, privacy requirements, and infrastructure constraints.