The AI-operated infrastructure OS

Run production AI infrastructure without scaling your Ops team.

Your team asks in plain language. Pandore reads your estate, writes a plan, checks it against your policies, and waits for approval before anything runs. Multi-cloud, GPU-native, EU-hosted.

  • EU data residency
  • No standing write access for the AI
  • Human approval before execution
  • GPU-native
  • Air-gap ready (2027)

A real governed run

ml-platform
working

Comparing A100 80GB across OVHcloud, Scaleway and CoreWeave on residency, availability and price
OVHcloud · 2× A100 80GB · $4.10/GPU·hplan
EU residency · GPU budget · within policy
Awaiting approval
Executor applied · 2/2 replicas ready · endpoint live
Audit logged· Read-only by default

Built by the Avarinth team. EU-native by design. In early access with design partners.

  • Multi-cloud
  • GPU-native
  • Policy and memory engine
  • Air-gap ready (2027)

Why now

AI spend is exploding. The bottleneck is now operations.

$2.5T

worldwide AI spend in 2026 (Gartner)

77%

of provisioned GPU capacity sits idle (industry estimate, 2026)

85%

of AI projects never reach production (industry estimate, 2026)

~$300B

projected sovereign AI infrastructure market by 2040, up from about $25B in 2026 (industry forecast)

For mid-market and regulated companies, the question is no longer whether to adopt AI. It is whether the infrastructure can keep up.

The problem

For a regulated enterprise running its own GPUs across several clouds, a 6 to 11 M€ per year infrastructure stack that no single vendor can orchestrate

This is no longer an infrastructure budget. It is a multi-million operating surface spread across compute, tooling, contracts, policies, and scarce human expertise. The figures below are an illustrative range for an AI-heavy regulated enterprise, not audited numbers.

GPU and AI compute
1 to 2 M€
Baseline cloud (CPU, storage, network)
0.8 to 1.6 M€
Ops and SRE headcount, plus external consultants
4 to 6 M€
Kubernetes orchestration
100 to 600 k€
Monitoring and observability
150 to 500 k€
Compliance, security, audit
250 to 600 k€
GPU scheduling and optimization
50 to 300 k€
Total infra stack6 to 11 M€ / year

Fragmented infrastructure is one of the largest hidden costs, and one of the largest execution bottlenecks, of enterprise AI adoption.

How it works

One operating loop. The request changes, the loop stays the same.

Map our PostgreSQL clusters, flag versions past end of support and any single-replica primaries, then draft a remediation plan.

01

Context

Reads your estate live through read-only connections. It never invents infrastructure facts.

Pandore is not a point solution for GPU optimization. It is a governed execution layer for infrastructure operations.

The product

Not a dashboard. The execution workspace where teams ask, approve, run and audit.

The workspace where teams plan, approve, run and audit infrastructure. Here is what each step looks like.

Plan

A reasoned plan, not a chatbot reply

Pandore reads your live estate, then writes a typed plan with a goal, a diagnosis and steps you can inspect before anything runs.

Approve

Nothing runs until policy passes and a human approves

Every change is a decision card with the exact command, its blast radius and its risk class. Approve it, or deny with a reason.

Audit

Every action becomes hash-chained evidence

An append-only ledger records each step, attributed to the assistant, the policy engine, a human or the executor. Verify the chain, export the proof.

Estate

Every cluster, host and GPU in one live view

Whole-GPU capacity, node health and compute cost, for the estate you put under Pandore's management. That managed footprint, agreed up front, is what your pricing tracks, not your whole infrastructure bill.

The operating layer

Pandore operates every generation of infrastructure

Legacy

Existing estate

Physical servers, VMs, storage and networks. Databases, middleware and legacy apps. Scripts and undocumented dependencies.

Cloud-native

Current operations

Multi-cloud, Kubernetes and managed services. Observability, FinOps, IaC and CI/CD. Hybrid environments with fragmented tooling.

AI

High-value wedge

GPU and accelerator clusters. Model deployment, orchestration and optimization. Sovereignty, compliance and cost constraints.

Different generations. The same operational challenge: fragmented context, manual execution, scarce experts.

More than an assistant

An assistant talks. Pandore remembers, checks, executes and proves.

Persistent infra memory

Remembers state, actions, incidents and past decisions, so teams do not restart from zero.

Policy engine

Authorizes, constrains or blocks each step before it runs. Built for CIO, CISO and compliance.

Live infra graph

Maps resources, services, dependencies, costs and constraints. Execution is context-aware, not generic advice.

Model-agnostic

Switch or combine models with no LLM lock-in. Bring your own key. Protects cost, performance and sovereignty.

Execution runners

Applies approved changes across cloud, on-prem, CPU and GPU. Air-gapped environments are on the 2027 roadmap.

Audit journal

Generates logs, reports and evidence automatically. Accountability is built in, not reconstructed.

Curated expert knowledge

Grounds operations in proven infrastructure practices, not guesswork.

Modeled outcomes

Pandore pays for itself

AI B2B SaaS

Payback under 2 months

Saved expert time and one deferred infrastructure hire.

Sovereign cloud provider

AI workloads retained

Keep AI compute on your sovereign platform, and win AI accounts you could not staff for.

Large financial institution

+192% ROI

Coordination effort saved and use cases moved to production.

These are modeled outcomes, pressure-tested with our design partners, not audited production results. The value comes from saved expert time, faster onboarding, retained AI workloads, and AI projects that finally reach production.

The difference

Each layer has its specialists. Pandore coordinates the whole loop.

Sovereign and GPU clouds

CoreWeave, OVHcloud, Scaleway

Covered

  • Compute

Orchestration point tools

Pulumi, Spacelift, Kubiya

Covered

  • Orchestration

GPU schedulers

Run:ai, Cast AI

Covered

  • GPU scheduling

Observability

Datadog, Grafana, Komodor

Covered

  • Observability

Mono-cloud copilots

AWS, Azure, GCP

Covered

  • Compute
  • Orchestration

Pandore

AI-operated infrastructure OS that coordinates the whole loop: EU-native, multi-cloud, GPU-native, with a memory and policy engine.

Covered

  • Compute
  • Orchestration
  • GPU scheduling
  • Observability
  • Ops / SRE
  • Compliance

Every layer is necessary. Pandore does not replace them, it coordinates them into a single loop.

Security and governance

Autonomy you can put in front of an auditor

  • The assistant is read-only

    It reads your infrastructure and proposes changes. It cannot apply them.

  • The AI never holds the keys

    A separate executor holds every write credential and applies approved changes once, with an ephemeral single-use grant.

  • Policy runs before execution

    Out-of-policy changes are blocked, not flagged after. Irreversible production changes can require two approvals.

  • Every action is evidence

    Requests, checks, approvals and changes are written to an immutable audit log you can export.

  • EU by default

    Pandore runs in the EU and keeps your estate data there. Run models EU-hosted or self-hosted, so nothing has to leave your boundary.

infra/eu-west/gpu.tfproposed
resource "gpu_pool" {
- region = "us-east-1"
+ region = "eu-west-1"
+ residency = "eu"
}
Policy engine
  • EU residency: pass
  • blocked: region outside EU residency scope
assistant proposed, Priya approved, executor appliedExport evidence

SOC 2 Type 1 and air-gapped deployment are on our 2027 roadmap. We will show you current status on a call rather than claim a certification we do not hold yet.

The team

Built by operators, for operators

Building the AI infrastructure OS for sovereign Europe and every organization that cannot scale AI through scarce human operators alone.

Lilian Debaque
CEO
Olga Lopusanschi
Deputy CEO
Clément Thiriet
CTO
Alexis Veillet
Chief Commercial Officer
Mathieu Fares
Head of Architecture

FAQ

Questions teams ask us

Does the AI change my infrastructure on its own?

No. The assistant is read-only. It proposes a change, a human approves it, and a separate executor applies it. The AI never holds a write credential.

Which clouds do you support?

Multi-cloud by design, including OVHcloud, Scaleway, AWS, Azure and GCP, plus on-prem and air-gapped environments.

Am I locked into one model vendor?

No. Pandore is model-agnostic. Bring your own key, run EU-hosted or self-hosted models, switch or combine models without lock-in.

How does it stay compliant with EU rules?

Pandore is built to support the obligations that bind regulated European enterprises, including GDPR, DORA and NIS2: EU data residency, an immutable audit trail, and a policy engine that enforces residency and approval rules before any change runs. Formal certifications such as SOC 2 Type 1 are on our 2027 roadmap, and we will not claim one we do not hold yet.

What does it cost?

A fixed platform fee, plus a set rate on the compute you place under Pandore's management, not on your whole infrastructure bill. We agree both with you up front and size them to your estate on a call, so you can forecast the cost.

See Pandore plan a real change on your infrastructure

Then watch it wait for your approval. 30 minutes, on your clouds, your GPUs, your compliance constraints.

Request a demo