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Enterprise AI

Secure, compliant, and governed AI infrastructure at any scale.

Enterprise AI deployments face a different class of problem: data residency regulations, model IP protection, compliance audits, multi-team governance, and the need for contractual SLAs. CogniCloud is purpose-built to meet these requirements without sacrificing the performance or developer experience that makes AI products competitive.

99.99%

Uptime SLA (dedicated tier)

0

Training on your data

SOC 2

Type II — in progress

HIPAA

Business Associate Agreement

The Challenge

Why this is hard.

Off-the-shelf AI APIs send your data to third-party models with opaque training policies. On-premise GPU clusters require multi-year capex and dedicated ML infra teams. Enterprises need the elasticity of the cloud with the security and control of on-premise — without building it themselves.

How CogniCloud helps

Everything you need, built in.

Data never leaves your region

Region pinning ensures that your prompts, completions, and model weights stay within a specific geographic boundary. Supports EU, US, APAC, and custom data residency requirements.

Private model deployment

Your fine-tuned models are stored encrypted at rest and never shared between customers. Dedicated GPU nodes ensure no hardware-level co-tenancy for your inference traffic.

RBAC & team governance

Role-based access control for every resource: GPU quotas, model deployments, vector namespaces, and billing. Full audit logs exported to your SIEM in real time.

Contractual SLAs

99.99% uptime guarantee on dedicated tiers, with automatic SLA credits for any breach. Dedicated support channel with a committed response time SLA.

VPC peering & private link

Connect your existing VPC to CogniCloud via AWS PrivateLink or GCP Private Service Connect. No API traffic traverses the public internet.

Cost governance

Budget alerts, per-team spending limits, and detailed cost attribution by project, user, and model. Full invoice breakdown for internal chargeback.

How it works

From zero to production in three steps.

01

Design partner onboarding

We work with you to map your requirements, configure data residency, and set up VPC peering. No self-serve signup — every enterprise deployment is tailored.

# Sample architecture review checklist
✓ Data residency: eu-west-1 only
✓ VPC peering:   vpc-0a1b2c3d (AWS)
✓ Compliance:    HIPAA BAA signed
✓ RBAC:          6 teams configured
✓ SLA tier:      Dedicated 99.99%
✓ Support:       Slack + 2h SLA
02

Deploy to your private environment

Your model deployments run on dedicated GPU nodes within your chosen region. Hardware isolation is enforced at the hypervisor level.

# Private deployment config
deployment:
  model: acmecorp/llama3-fine-tuned-v4
  tier: dedicated
  region: eu-west-1
  isolation: hardware
  replicas: 8
  sla:
    p99_ttft_ms: 15
    uptime: 99.99%
03

Audit, govern, iterate

Every API call is logged with full metadata. Export logs to your SIEM, set spending alerts, and manage team quotas — all in the governance dashboard.

// Audit log entry (streamed to your SIEM)
{
  "ts":       "2026-02-14T09:12:33Z",
  "user":     "alice@acmecorp.ai",
  "team":     "product-ai",
  "model":    "acmecorp/llama3-v4",
  "tokens_in":  512,
  "tokens_out": 384,
  "latency_ms": 11.2,
  "cost_usd":   0.00038
}
Platform in development

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GPU Compute
Inference APIs
Vector Search
Observability