Your Cloud. Your Data. Your AI.
Your AI Pilot Worked.
So Why Isn't It Live?
XePlatform builds and operates the production infrastructure your AI system needs, inside your own cloud account.
Platform running in hours·
No platform team required·
Sovereign by architecture·
Predictable infrastructure costs
Live in hours, not next year
Your data never leaves your account
No platform team to hire
Audit trail by default
Fixed cost, no token billing
Yours to keep. No lock-in.
Operated by XePlatform
Control Plane
Platform Orchestration only
Lives in your cloud account
Your Cloud Account
Secure and Private
Managed Kubernetes clusters · GPU instances · VPC · All data,
billed to your account
🧠Private LLM Models
🚫Data inside boundary
⚡GPU Nodes
🤖Agent Orchestration
📈Auto-scaling
🗄Observability
📋Automated Backups
🚀Release Pipelines
🔒Security Enforcement
📋Audit Trails
External endpoints, called out from your AI Apps
Managed
AI
optional
🌐Bedrock · Vertex · Azure AI
🔌Claude · GPT-4 · Grok APIs
From Pilot to Production
Four Things Work.
A Fifth One Doesn't.
The pilot is rarely where an AI project dies. It dies in the gap between something that works in a demo and something the business can rely on every day.
01The Model Works
›
The Infrastructure to Run It Doesn't Exist
02The Demo Works
›
Nobody Owns It at 3am
03The Prototype Works
›
Security Won't Sign It Off
04Usage Grows
›
Costs Stop Being Predictable
XePlatform Operates the Missing Layer.
Not the model, and not the application. The layer underneath both: the clusters, GPUs, releases, monitoring, access controls and cost telemetry that turn a working prototype into a system the business can depend on. We build it and we run it, inside your own cloud account.
Two Ways to Use
XePlatform
One Platform.
Two Ways to Run AI.
Use the AI providers you already pay for, or run models on hardware you control. Same platform either way, and the decision is reversible. Most teams start with one and move workloads to the other as volume or sensitivity grows.
Most teams end up running
both. managed endpoints for general workloads, private GPU for sensitive or
high-volume tasks.
XePlatform operates both as one platform, with unified observability and cost
attribution across the entire stack.
For example: in
pharma, proprietary molecule models and internal IP run on private GPU, while the Patent Scan Agent
orchestration, patent retrieval pipelines, and audit workflows run on managed infrastructure. One
platform. One cost view.
What You Can Launch
People Buy Outcomes.
Not Infrastructure.
XePlatform is the operational layer beneath these products, shipped without hiring a DevOps team, regardless of whether your models are private, managed, or both. Which AI use case are you building?
Internal AI Copilots
Legal, finance, HR, and ops assistants: private, sovereign, inside your cloud account.
Secure Document AI
RAG over contracts, policies, and manuals, with fine-tuning, full audit trail, and data
residency.
AI Search
Semantic search across internal knowledge and products, tuned to your domain, running
in your cloud account.
Multi-Agent Workflows
Orchestrated AI agents across internal systems, with governance controls,
observability, and rollback.
Fine-Tuned Domain Models
PEFT / LoRA fine-tuning for your brand voice, terminology, and domain knowledge.
Compliance Automation
AI-driven policy checks, audit preparation, and regulatory reporting: governed,
traceable, sovereign.
Customer Support Agents
AI agents that resolve queries and escalate intelligently, operating entirely in your
cloud account.
Voice AI Systems
Real-time voice inference on dedicated GPU: no shared inference layer, no token
billing.
Image & Video Generation
High-VRAM media workloads on instance-based GPU: predictable cost, no token overage.
Multi-Model Inference
Any open-source, fine-tuned, or proprietary model, on GPU you control, swapped without
replatforming.
Batch AI Processing
High-volume inference for contracts, medical records, and reports: fast, auditable, and
cost predictable.
AI-Powered Knowledge Assistants
RAG over internal wikis, docs, and codebases. Private, current, access-controlled.
Built for Your Industry
The Same Operational Complexity.
Sector-Specific Stakes
XePlatform removes the AI infrastructure burden regardless of your sector. Your data stays in your account: encrypted, auditable, and traceable by architecture. Your team owns the compliance accreditation. We give them the foundation to achieve it.
Financial Services & Fintech
- Fraud detection and risk scoring agents
- Client intelligence inside your cloud account
- Full audit trails, encrypted at rest and in transit
- Your team owns accreditation — we give them the foundation
Healthcare & MedTech
- Clinical triage and diagnostic support agents
- Patient data never leaves your cloud perimeter
- Encrypted, logged, and traceable by architecture
- Governance team has full oversight at every layer
Pharma & Life Sciences
- R&D data and compound libraries stay in-house
- Agentic discovery workflows with audit-ready trails
- IP and FTO risks checked before commitment
- Model traceability and observability for compliance
Legal & Professional Services
- Contract analysis and due diligence agents
- Client data never touches a shared inference layer
- Sovereign by architecture, encrypted end-to-end
- Full evidentiary audit trail in your account
Manufacturing
- Predictive maintenance and quality control AI
- Production floor intelligence on your own GPU
- Operational telemetry stays inside your account
- Proprietary process IP fully protected
Space & Satellite Technology
- Computer vision on satellite imagery — no token billing at scale
- Private GPU · Fixed cost · No shared inference layer
- Anomaly detection across telemetry and spectrum data
- Sensor data and orbital IP sovereignty by design.
Media & Entertainment
- Image and video generation — no token billing
- Dedicated high-VRAM GPU, predictable cost
- Brand IP never on shared inference
- High-volume jobs, zero overage risk
Logistics & Supply Chain
- Route optimisation and demand forecasting agents
- Disruption prediction at scale on live telemetry
- Autoscaling GPU with cost observability built in
- Operational data stays inside your account
Your sector not listed?
- Any regulated vertical. Any sensitive workload.
- If your AI workload requires sovereignty, compliance, or operational scale — the platform fits.
Sovereign by Architecture, Not by Contract
Your Data Never Leaves
Your Cloud Boundary.
Every AI vendor will promise you data protection. What they are offering is a clause: a policy, a BAA, a commitment not to look. XePlatform does not ask you to trust a promise, because the architecture removes the question.
The control plane orchestrates. That is all it does.
It holds no credentials to your account, no permanent access role, and no network path to
your data. There is no mechanism by which your information could reach us, which is a different claim from a
promise that we will not look.
Everything of value stays where it already is.
Your models, data, vector stores, secrets and container images live in your own cloud
account, under your own billing, inside your existing security perimeter. Nothing is copied out to be
processed.
Nothing in the request path depends on us.
The infrastructure is yours: standard Kubernetes, standard tooling, in your account. That
is the practical test of ownership, and it is one a hosted service cannot pass.
For the buyer, this is the difference between a security review that takes two weeks and one that takes two quarters. Sovereignty is not the reason to buy. It is the reason nothing blocks you after you do.
See the architecture in detailThe Business Case
What the Delay
Is Actually Costing You.
6–18mo
To build the equivalent platform in-house, before a single AI product ships
$120–180K
Annual cost of one platform engineer, and one is rarely enough
$0/token
On private models at high volume, where per-token billing disappears entirely
The economics are straightforward. Below a certain volume, paying per token is cheaper than running your own hardware. Above it, the position reverses and keeps widening. Either way you avoid the platform team, because operating the layer is what you are buying.
Where your own line falls depends on your workload, so work it out before the business case goes up.
How XePlatform Compares
A Third Category.
Not Build. Not SaaS.
Build in-house or adopt SaaS. Most teams get stuck between control and speed. XePlatform gives you both.
| Build Yourself | SaaS AI Platform | XePlatform | |
|---|---|---|---|
| Infrastructure location | Your cloud | Vendor cloud | Your cloud |
| Who operates it | Your team | Vendor | XePlatform |
| Time to platform ready | 6–18 months | Days | Hours |
| Data boundary | Inside | Leaves your account | Always inside |
| Incident ownership | Your team | Vendor | XePlatform |
| Cloud portability | Yes | No | Yes |
| Platform engineering hire | Required | Not required | Not required |
| Access & control | Full | Vendor-gated | Full, in your account |
| Customisation | Full | Limited to platform APIs | Full cloud-native stack |
| Cost model | Engineer salaries | Token / seat billing | Fixed Subscription + your infra costs |
Build Yourself
SaaS Platform
XePlatform
Infrastructure location
Your cloud
Vendor cloud
Your cloud
Who operates it
Your team
Vendor
XePlatform
Time to platform ready
6–18 months
Days
Hours
Data boundary
Inside
Leaves account
Always inside
Incident ownership
Your team
Vendor
XePlatform
Cloud portability
Yes
No
Yes
Platform eng. hire
Required
Not required
Not required
Access & control
Full
Vendor-gated
Full, yours
Customisation
Full
Limited APIs
Full cloud-native
Cost model
Engineer salaries
Token billing
Fixed sub + infra
The AI control plane that runs inside your cloud account. Not ours. Yours.
The Build vs Buy Decision
The Operational Layer
for Enterprise AI.
XePlatform operates the infrastructure beneath your AI products: deployment, orchestration, observability, governance, and incident response, so your engineers don't have to.
Without XePlatform
Hire costly platform engineers ($120–180K/year).
Spend 6–18 months building Kubernetes, networking,
and security.
Execution runs on vendor infrastructure, not your
cloud account.
Execution logs and traces outside your compliance
boundary.
No per-agent autoscaling, canary rollouts, or AI cost
attribution.
No GPU workloads alongside managed AI calls.
Platform lock-in. Moving later requires a rewrite.
✓ With XePlatform
Live in hours. Platform running in hours. Your
infrastructure.
Fully managed scaling, upgrades, and incidents.
Your cloud, data, and models remain fully yours.
Sovereign-by-architecture compliance boundaries.
GPU workloads alongside managed AI calls. One
platform.
Built-in canary rollouts, config/environment drift
detection, and rollback.
Unified AI operations across Bedrock, Vertex, Azure
AI, Claude, GPT-4, and Grok.
🏆
Who builds it themselves
Anthropic, H2O.ai, and Fireworks AI all run substantial platform engineering operations on Kubernetes. Each required a world-class platform team and months of buildout. For them, that infrastructure is the product. The platform itself is their competitive advantage.
For Your Technical Reviewers
For the People Who Will
Ask You How This Works.
Your engineering and security colleagues will want the detail before they sign off. This section is for them: what we run, how incidents are prevented, and what stays under your control.
Why Production AI Keeps Failing
Production failures rarely come from models. They come from release engineering.
The 6 Root Causes of Production Incidents
▸Environment Drift
▸Hidden Dependencies
▸Human Error in Deployments
▸Toolchain Compatibility Gaps
▸Inconsistent Config Management
▸Undocumented Hotfixes
XePlatform Prevention Playbook
01
Semantic Versioning: every component, model, and dependency
tracked with structured version identifiers across all environments
02
Lock
Versions: pin all dependencies to ensure consistency across every environment
03
Deploy Gradually: canary rollouts minimise blast radius;
auto-rollback fires on any anomaly
04
Automated Checks: version validation in CI/CD catches drift
before it ever reaches production
What You Unlock
Release Engineering Patent
Pending
Preventive, Not Reactive. 75% Lower MTTR.
XePlatform enforces staging-to-production parity before promotion. Bad deploys are stopped before they reach production, not caught after. Canary rollouts, config/environment drift detection, and auto-rollback built in. This capability is unique to XePlatform.
Sovereignty
Your Data. Your Control. Always.
The execution plane is in your cloud account. There is no technical mechanism by which your data can reach an external system. Not a policy promise, but the architecture itself.
Portability
No Lock-In. Cloud-Agnostic.
IaC-first, Kubernetes-native, open-source at the core. Start on AWS. Move to Azure AKS, Google GKE, or on-premises, no replatforming. Your infrastructure is code, portable by design.
Your Team Should Be Shipping AI, Not Running Infrastructure.
Deploy production AI systems without
building a Platform Engineering organization.
What We Operate on Your Behalf
IaC
golden paths wire all of it together, compressing cluster, GPU, networking and observability setup from
months into hours.
Kubernetes
We set up and run the cluster your AI lives on. Your team never touches it.
GPU Scheduling
GPUs shut off when idle, so you stop paying for capacity nobody is using
Model Serving
Your models get a stable endpoint, version control, and traffic routing
Observability
See what your models are doing, what they cost, and when they start drifting
CI/CD & Releases
New versions go to a slice of traffic first, and roll back on their own if something
breaks
Autoscaling
Capacity follows demand, up and down, without anyone watching it
Security Policies
Access control, image scanning, network isolation and secrets, all handled
Cost Insights
See exactly which app, agent or team is spending what
Data Infrastructure
Vector databases, storage and data pipelines, all inside your own account
Upgrades & Patching
Versions and security patches applied without taking anything offline
Incident Response
We watch it around the clock and fix it when it breaks. Our pager, not yours.
Compliance & Audit
Every action logged and traceable, so the security review is a shorter conversation
Common Questions
The Questions That Come Up
In the Approval Meeting.
The questions procurement, security and engineering raise before sign-off. Answered directly, without spin.
Managed AIPrivate AI
No dedicated platform team required.
XePlatform replaces the heavy lifting of
Kubernetes operations, release engineering, GPU infrastructure management, and AI platform
lifecycle.
Your existing engineers focus on models and
applications, not infrastructure.
This removes the single biggest hiring blocker
in enterprise AI deployment.
Managed AIPrivate AI
No. XePlatform never requires access to your
code repository at any point.
We deploy using your container images,
deployment artifacts, or approved CI outputs.
Your source code, intellectual property, and
repositories remain fully under your control.
We never hold credentials, access, or visibility
into your codebase at any stage.
Managed AIPrivate AI
Two line items only: a platform subscription
fee, plus your cloud infrastructure costs billed directly to your own account with no markup.
Your compute, GPU, storage, and networking stay
in your cloud bill. We never see or mark up those costs.
XePlatform is available on AWS Marketplace,
eligible for EDP committed spend, meaning most enterprise customers can approve it against existing
AWS budget.
Managed AIPrivate AI
That is exactly the right time to engage.
The most expensive mistake in AI is completing a
successful pilot and then spending 12 months trying to reach production.
XePlatform makes production the starting point,
not the destination. The platform is running in your account within hours of kickoff, and most teams
are live the same week.
Starting during the pilot means your production
environment is already operational the moment the business case is approved.
You avoid the gap between "this works" and "this
is in production" that kills more AI initiatives than model quality ever does.
Managed AIPrivate AI
Cloud: AWS EKS, Azure AKS, or
Google GKE in your choice of region. EU regions fully supported. Multi-cloud supported.
Kubernetes: XePlatform
provisions and operates the cluster. You do not need an existing Kubernetes team.
GPU: Optional. Required for
Private AI tier. XePlatform handles GPU scheduling, autoscaling, and MIG partitioning.
Air-gapped: Supported for
regulated environments with no public internet egress requirements.
Tenant isolation: Full
namespace isolation per team or workload. RBAC enforced at the platform layer.
SLA model: XePlatform operates
the platform layer. Availability tied to your cloud account's SLA, not a shared vendor managed
service.
Time to production: Most teams
are live within hours of kickoff.
Managed AIPrivate AI
No, and this is enforced by architecture, not a
contractual promise.
Your S3 buckets, Secrets Manager entries, RDS
and vector databases, and ECR container registries all live inside your cloud account under your IAM
policies.
XePlatform has no credentials, no IAM role, and
no network path into those resources.
We operate a separate control plane that handles
orchestration, scheduling, and lifecycle operations only, with no technical ability to read, write, or
access your data, secrets, images, or databases.
Managed AIPrivate AI
Very straightforward. XePlatform provides IaC
golden paths specifically designed for EC2-to-Kubernetes modernisation.
What typically takes 6–12 months of planning,
tooling selection, and implementation is compressed into a guided, automated process.
You containerise your workloads. We handle
Kubernetes infrastructure, networking, GPU scheduling, and production readiness.
Private AI
Karpenter autoscaling scales
GPU nodes to zero when idle, eliminating wasted spend on unused capacity.
MIG partitioning shares GPUs
across teams and workloads so each GPU earns its cost.
Real-time cost telemetry
surfaces waste instantly with full visibility into which workload, model, or team is driving each
cost.
Customers typically see 30–40% reduction in GPU
spend compared to self-managed clusters.
Managed AIPrivate AI
XePlatform is a PaaS control plane — a fully managed platform engineering layer
provisioned and operated inside your own cloud account.
It is not software you install, a SaaS tool you
log into, or a shared cloud service. It is a dedicated operational layer that lives entirely within
your cloud account and is operated on your behalf.
The control plane handles orchestration,
scheduling, release engineering, observability, and lifecycle management. It has no data plane access
— it never touches your data, models, secrets, or application traffic.
You get the operational outcome of a world-class
platform engineering team — without the hire, the ramp-up, or the ongoing overhead. Your
infrastructure, fully operated. Inside your cloud account.
Managed AIPrivate AI
Compliance is not an assertion. It is a
structural property of how the platform is built.
Every action is logged, every decision is
traceable, and every audit trail lives in your environment, not ours.
RBAC, secrets management, image scanning,
network segmentation, and policy enforcement are built into every deployment.
XePlatform does not hold compliance
accreditations — your platform runs in your cloud account, so the accreditation responsibility sits
with you. What we give you is the auditable, traceable, encrypted foundation that makes achieving
those accreditations structurally easier.
Managed AIPrivate AI
Most customers reach a production-ready
environment within days of onboarding, not the 6–18 months typical of building AI infrastructure from
scratch.
Initial provisioning (Kubernetes cluster, GPU
nodes, networking, secrets, observability stack) is automated via IaC golden paths.
Model deployment and first inference follow
within hours of cluster provisioning.
A typical enterprise pilot runs in 4 weeks, at
the end of which you have a production-grade environment already operating.
Managed AIPrivate AI
No lock-in. XePlatform is open-source at its
core, Kubernetes-native, and IaC-first. The entire platform is defined in declarative code that is
portable by design.
AWS is the recommended starting point for its
native EKS depth, Karpenter autoscaling, and Marketplace procurement.
The same platform runs on Azure AKS, Google GKE,
or on-premises Kubernetes with no replatforming required.
Managed AIPrivate AI
Staging-to-production mismatch is one of the
most expensive causes of AI deployment failure. Industry-average downtime costs $300K per hour, with
version drift as the leading trigger.
All dependencies are pinned and environments are
validated in CI/CD before promotion. Nothing reaches production that has not passed parity
checks.
Canary rollouts limit blast radius on every
release.
Auto-rollback fires immediately if any anomaly
is detected during a rollout.
Drift detection runs continuously, catching
inconsistencies before they ever reach production.
This eliminates the category of incidents
entirely, rather than just reducing MTTR after they happen.
Managed AIPrivate AI
Yes. High MTTR and recurring incidents are
almost always symptoms of the same root causes: environment drift, hidden dependency mismatches,
undocumented hotfixes, and inconsistent configuration management.
XePlatform addresses these at the platform
layer, not through better monitoring after the fact, but by structurally preventing the conditions
that cause incidents in the first place.
Customers see 75% lower MTTR, with the most
significant gains in teams that previously had no governed release process.
Managed AIPrivate AI
You can, and some do. Anthropic, H2O.ai, and
Fireworks AI all run substantial platform engineering operations on EKS.
Each required a world-class platform team and
months of buildout. For them, that infrastructure is the product.
The question is whether it is for you too. If
your competitive advantage is the AI application you ship, not the Kubernetes layer beneath it,
XePlatform compresses that buildout from 12–18 months to hours.
Every sprint your team spends on Kubernetes is a
sprint not spent on the model, the product, or the customer.
Managed AIPrivate AI
Good. XePlatform makes them more effective, not
redundant.
Golden paths eliminate Kubernetes maintenance
overhead so your team focuses on AI products, not infrastructure boilerplate.
GPU scheduling eliminates cloud bill
surprises.
Release engineering eliminates the category of
production incidents caused by staging-to-production drift.
Your platform engineers own the strategy. We
handle the operational weight.
Managed AI
Vertex AI gives you a managed model endpoint,
not a managed AI systems platform.
The operational complexity doesn't disappear. It
moves. You still need agent orchestration, observability, release engineering, autoscaling, cost
controls, and security governance around those API calls.
XePlatform runs entirely inside your cloud
account alongside your existing Vertex endpoints and adds the full operational layer you're currently
missing.
Your Vertex contracts, fine-tuned models, and
endpoint configuration stay exactly as they are.
See It Running
in Your Own Cloud Account.
A working environment, in your AWS or GCP account, with your team. Bring the pilot that stalled.
