Plausible ≠ compliant
An LLM optimizes for a plan that looks right, not one that satisfies your security baseline. A public S3 bucket and a private one look almost identical in a diff review at 5 p.m.
Describe what you need in plain language. Apkallu Cloud generates the Terraform and Kubernetes — then formally proves every plan against your security and compliance policies before a single resource changes.
Generative tools made writing infrastructure fast. They didn't make it safe: the same model that scaffolds your cluster in seconds will cheerfully open a security group to the world.
An LLM optimizes for a plan that looks right, not one that satisfies your security baseline. A public S3 bucket and a private one look almost identical in a diff review at 5 p.m.
When AI writes 400 lines of Terraform in seconds, human review becomes the bottleneck — or worse, a rubber stamp. Speed without a gate just ships mistakes faster.
Even a perfect deploy decays: a console hotfix here, a manual scale-up there. What was compliant on day one is unknown by day ninety unless something keeps checking.
The same Prover-in-the-Loop that verifies Apkallu Studio's flight code gates every infrastructure change: AI proposes the plan, formal policy checks decide whether it applies.
Natural language, in the CLI or in Slack. Architecture context — your VPCs, naming, tagging, budgets — is already part of the model's constraints.
Apkallu Cloud emits real, reviewable Terraform and Kubernetes manifests — your repos, your state, your pipelines. No opaque control plane doing things you can't diff.
Your baselines — network exposure, encryption, least privilege, cost ceilings — are encoded as machine-checkable obligations. Violations come back as counterexamples that drive automatic plan repair; a rule can't be silenced with a comment.
Proven plans apply through your existing pipeline. Afterward, live state is continuously re-checked against the same obligations — drift isn't a quarterly audit finding, it's an alert with a proposed, pre-proven fix.
Clusters, networking, IAM, observability, CI/CD — generated end-to-end as reviewable Terraform, Helm, and K8s manifests that follow your conventions.
"Scale the trainer pool to 6." "Why did ingress latency spike?" Every command runs through the same prove-before-apply gate — conversational, never cowboy.
H100/A100 node groups, distributed training topologies, model-serving autoscale, and spot strategies — with cost ceilings expressed as provable obligations.
Start from proven baselines (CIS-aligned network, encryption, IAM) and add your own obligations. Policies version like code and prove like theorems.
Reconciliation targets the last proven configuration — the system heals toward a state that's known-good by construction, not just "what was there before."
Runs against AWS today (EKS-first), in your accounts, with your state backends. On-prem and air-gapped delivery available through Apkallu Info engagements.
A sample of the baseline policy set. Each is a machine-checked theorem about the plan — hold, or the plan doesn't apply.
We're onboarding a small number of early-access teams — GPU/ML platform groups and regulated-industry infra teams first. Tell us about your environment and we'll set up a working session on your actual policies.