Problem: Enterprise adoption of LLMs is skyrocketing but so are concerns around the privacy, control, and vendor lock-in. Most commercial APIs don't offer transparency, and open-source models often lack the secure infrastructure needed for enterprise use.
We assess your existing architecture, security posture, and use case landscape to define a roadmap for AI adoption. You'll get clear model recommendations, hosting options, and a rollout plan tailored to your enterprise.
We design cloud-native or hybrid infrastructure with encrypted storage, hardened API access, IAM roles, audit trails, and zero-trust principles—ensuring your LLM is safe, scalable, and compliant from day one.
From GPU provisioning to container orchestration (Kubernetes, Docker), we guide deployment of optimized inference backends (vLLM, TGI, TensorRT-LLM). We also handle service exposure, API gateway setup, and logging.
Security Auditing & Hardening
We perform in-depth security audits to identify misconfigurations and attack surfaces, then apply patching, container scanning, and vulnerability management to protect your model and data in production.