Built from production problems, not AI fashion
A brand of CCSolutions.io: the enterprise AI control plane on regional infrastructure we operate ourselves.
https://api.latam.ccsio.ai/v1
Why ccsio.ai exists
ccsio.ai is a brand of CCSolutions.io. The problems that shaped it are the ones we see every day in production infrastructure: where your data is allowed to live, how your GPUs are actually used, and who gets to run what. So we did not decorate the stack with a model gateway — we extended the control layer we already operate: dedicated GPU servers, region-pinned data, and governance you can audit.
From infrastructure to AI
ccsio.ai is not a product team that found a model. It is CCSolutions.io — an engineering organization — extending the infrastructure disciplines it already runs into the control of AI.
Kubernetes
Production clusters planned, deployed and operated as boring, repeatable infrastructure.
Cloud infrastructure
Multi-region environments built for regulated workloads, not demos.
Platform engineering
Internal platforms that let teams ship on shared foundations without shared risk.
DevOps
CI gates, GitOps and reversible deploys as the default path to production.
Distributed systems
Consistency, replication and recovery you can reason about under failure.
Observability
Metrics, logs and traces with SLOs — the instruments a control plane is judged on.
Security
Identity, isolation and least privilege treated as design inputs, not review findings.
Networking
Private, encrypted paths between your workloads, your models and your region.
Storage
Durable, region-pinned storage with retention you can audit and erase.
Sovereign infrastructure
Infrastructure operated inside the residency your contracts and regulators require.
Enterprise operations
On-call discipline, runbooks and incident response for systems other companies depend on.
The problems we kept seeing
Residency
Your data and your compute have to live where your contracts and regulators say they live.
GPU utilization
Shared, best-effort GPU pools waste capacity and break the latency promises your product makes.
Model fragmentation
Every model and provider wants its own integration, its own quirks, its own failure mode.
Permissions
In an enterprise, knowing exactly who — and which workload — may call which model is a first-class requirement.
Costs
Inference spend has to be measurable per tenant and per model, or it quietly becomes a leak.
Observability
You need the same signals from inference that you demand from every other production dependency.
Guardrails
Guardrails have to be enforced by the plane, not promised by a policy page.
Knowledge
Your organization's context has to reach the model without leaving your boundary.
Memory
Durable, scoped memory — with erasure you can prove — is infrastructure, not a feature flag.
Agents
Agents inherit your permission model or they break it; there is no third option.
Vendor control
You want to change models and providers without re-architecting — and keep the choice in your hands.
What you can verify
We do not publish numbers we cannot defend. These facts are checkable today in our documentation, our contracts and our infrastructure.
- Markets
- EU (Germany) · USA · LATAM (Colombia)
- Endpoints
- api.eu.ccsio.ai · api.us.ccsio.ai · api.latam.ccsio.ai
- Deployment
- Dedicated GPU servers, region-pinned
Every problem maps to the control plane
Each problem above is a module of the ccsio.ai platform. Follow the canonical platform pages and see it working.
The philosophy
Six phrases decide everything we build. If a design decision cannot be defended by one of them, it does not ship.
Your AI.
The models you run and the providers they come from are your choice — switchable without re-architecting.
Your Data.
Inference is not stored by default. Knowledge and Memory persist only when you enable them — encrypted, isolated to your organization, under keys only you control.
Your Knowledge.
Your organization's context reaches the model inside your boundary, pinned to the region you chose.
Your Agents.
Agents run under your identity and permission model — scoped, attributable, revocable.
Your Infrastructure.
Dedicated GPU servers in your region — capacity reserved for your workloads, not a shared pool.
Your Control.
Identity, guardrails and audit logs are enforced by the control plane — governance you can audit.
Infrastructure DNA
Availability
Targets you can hold us to, on capacity that is yours — not borrowed from a shared pool.
Latency
Compute in your region, short paths to your data, engineered for your millisecond budget.
Isolation
Dedicated servers and per-tenant boundaries: your workload does not share compute with strangers.
Residency
Region-pinned by default, verifiably: your data and your inference stay where you choose.
Failures
Regions fail independently, and your controls survive their failure.
Scaling
Scale is your decision — per tenant, per workload, with the spend attached to what scaled.
Access control
Every call authenticated, scoped and attributable. Least privilege, enforced in the plane.
Auditability
Decisions leave records. You can answer who called what, with which model, when.
Observability
Metrics, logs, traces and SLOs — the signals production infrastructure has always run on.
Security
The baseline, not a feature: layered on by policy, never bolted on as an option.
Lifecycle
Models are replaced and providers change. Your control plane lives longer than any of them.
Part of a larger ecosystem
AI advances where its community builds it: in summits, engineering events and partnerships across the regions where we operate.
From infrastructure to intelligence
This is where the control layer stands today: inference, models, agents, knowledge, memory, guardrails, governance, usage and the regional infrastructure behind it — one control layer you operate. Adaptation, fine-tuning and custom training are on the roadmap — planned, and nothing beyond V1 is live today: no stage carries a date commitment.