AI infrastructure that actually runs in production.
Most AI systems fail because the infrastructure wasn't designed for real workloads. This platform exists to run AI securely, reliably, and at scale — self-hosted models, vector databases, and inference infrastructure across multiple Tier III colocation facilities and an on-premises CT server room, connected by private fiber. Deployed in our cloud, your cloud, or your data center.
Why most AI projects fail
Most AI projects do not fail because the models were not good enough. They fail because the infrastructure underneath was not designed for production. The same four problems show up in almost every AI engagement we walk into:
- Built on top of outdated systems — AI deployed on legacy applications produces brittle results that do not survive contact with real workloads.
- No integration between tools — fragmented data across CRM, ERP, M365, and line-of-business systems means AI sees only part of the truth.
- No real data foundation — duplicated records, inconsistent schemas, and undocumented data flows produce AI outputs that are confidently wrong.
- No plan for production or scale — capacity, monitoring, model updates, security, and operations get bolted on after launch instead of designed in.
We solve all four before writing a line of AI code. This platform exists to make that possible.
Why private matters now
The default AI architecture for most firms today is a stack of public API calls — OpenAI, Anthropic, Google, sometimes all three. That architecture is excellent for experimentation and reasonable for small-volume production, and Skyview Labs uses public APIs where they're the right tool for a specific job.
But for a growing number of workloads, public-only is not the right answer:
- Your data is commercially sensitive, and sending it to a provider that may log it, cache it, or evaluate it for policy violations is unacceptable.
- Your data is regulated — healthcare records, legal matter content, financial customer data, public-sector information — and a provider's terms of service do not survive a procurement review.
- Your inference volume has grown to the point where per-token pricing is no longer predictable, and your finance team wants a capacity-based cost model.
- Your workload requires low and consistent latency, and a shared public API's tail latency is a business problem.
The Skyview private AI cloud exists to solve those problems directly.
What runs in the private cloud
Architecture diagram. Client request enters through Cloudflare Tunnel and reaches the Kubernetes cluster, which orchestrates self-hosted LLMs, vector databases, retrieval pipelines, and observability. Selected workloads route to external reasoning APIs over a documented path.
Self-hosted language models.
We operate open-weight models — Llama, Mistral, Qwen, DeepSeek, and others — in Kubernetes clusters within our data centers. Model selection is per-workload: we run what's appropriate for the task, not a single model for everything.
GPU capacity, provisioned to your workload.
We acquire and deploy GPU capacity as engagements require it, rather than maintaining a shared, noisy-neighbor pool. Your inference runs on hardware scoped to your needs, with performance characteristics you can plan around.
Vector databases and retrieval infrastructure.
Embedding stores, semantic retrieval, and the supporting data layer for RAG — all running inside our perimeter, on infrastructure we operate.
Orchestration and scaling.
Every component runs in Docker containers managed by Kubernetes — horizontal scaling, zero-downtime updates, workload isolation, and the operational rigor expected of enterprise platforms.
Edge protection.
All public surfaces are fronted by Cloudflare Tunnels. Our data center edge carries no open inbound ports for client workloads. WAF, DDoS mitigation, and bot management run at the edge, in front of every request.
Integration with public APIs, where appropriate.
For workloads that benefit from best-in-class external models — frontier reasoning, high-quality vision analysis on low-frequency operations — we integrate thoughtfully with providers like Anthropic and OpenAI. When we do, we do it transparently: you know exactly what runs where, and why.
Our footprint
A Tier III TierPoint facility in the 495 Technology Corridor — a 115,000+ square-foot data center.
- Tier
- III
- Footprint
- 115,000+ sq ft
- Power
- N+1 generators · 48-hr fuel
- Connectivity
- 10G private fiber · multi-PoP
- Security
- 24/7 on-site · 2FA biometric
- SLA
- 100% uptime
A second Tier III TierPoint facility, providing geographic redundancy and capacity headroom.
- Tier
- III
- Footprint
- Multi-MW campus
- Power
- N+1 redundant
- Connectivity
- Private fiber · backbone peering
- Security
- 24/7 on-site · 2FA biometric
- SLA
- 100% uptime
The two colocation footprints are connected by private fiber, giving us geographic redundancy, capacity headroom, and a documented inter-site path for workloads that require it.
Our on-premises server room at the Spectrum Virtual headquarters in Cheshire, Connecticut — the parent IT services organization that has been operating production infrastructure since 2013. Smaller than the TierPoint colocation footprints, but real generators, redundant business fiber, and a NOC sitting next to the racks. Used for development, staging, internal tooling, and regional capacity.
- Tier
- On-prem · office room
- Power
- On-site generators
- Connectivity
- Redundant business fiber
- Security
- Office access controls · CCTV
- Workloads
- Dev · staging · internal · regional
- Compliance
- See attestations note below
// Honest note: CT-01 is a server room at our office, not a Tier III audited colocation facility. Workloads with formal compliance requirements (HIPAA, SOC 2, public-sector procurement) run in mrl-01 or chi-01. CT-01 is the right place for development environments, internal tooling, and regional capacity where the team being physically next to the hardware is a feature.
Four ways to deploy
Where the workload runs is your call. We build private AI for any of four deployment configurations, and we're transparent about the tradeoffs of each. The right answer depends on your data sensitivity, your existing cloud standardization, regulatory posture, and operational risk tolerance — not on what's convenient for us to host.
On-premises at your site
We design, build, and install the Skyview stack — models, vector DB, retrieval, observability — inside your perimeter. Air-gapped supported. Your hardware, your network, your security boundary. Right answer when sovereignty, ITAR, or compliance constraints rule out external hosting.
- Air-gapped or restricted-network deployments supported
- We spec, procure, and rack hardware at cost
- Managed operations available remotely or on-site
In your Azure / AWS / GCP account
Architected, deployed, and operated inside your existing public cloud — wherever you've standardized. We work within your IAM, network controls, and existing commitments, and integrate with the workloads already running there. Right answer when AI should live inside the cloud boundary you already operate.
- Native to your existing cloud spend + commitments
- Integrates with managed AI services (Bedrock, Azure OpenAI, Vertex) where relevant
- Tenant-isolated configuration · documented data flows
- Same engineering team across all three hyperscalers
Hosted in our facilities
Self-hosted models, vector DB, retrieval, and observability running in Kubernetes across our Tier III TierPoint colocation footprints (mrl-01, chi-01) with regional capacity at our CT office. Right answer when you want a turnkey hosted environment without standing up your own AI infrastructure.
- Fastest to launch — weeks not quarters
- SOC 2 / HIPAA / PCI / ISO 27001 facility attestations
- Capacity, availability, and operations are our problem
A mix of the above, per workload
Most enterprise deployments don't fit a single box. Sensitive workloads on-prem, hot-path inference in your public cloud, frontier reasoning routed to a public API by exception — all under one engagement, with documented data flows across the boundaries.
- One engagement spans multiple deployment surfaces
- Same engineering team across all configurations
- External dependencies scoped, documented, and approved per call
- Right answer when latency, sovereignty, or cost applies to part of the stack, not all of it
Built for procurement review
Our colocation facilities operate under the following attestations and registrations. These cover the physical, environmental, and operational controls of the facilities our platform runs in.
Skyview Labs' own security program is aligned to SOC 2 and ISO 27001 controls at the application and operations layer. Formal attestation at the corporate level is on our roadmap. We're transparent about this distinction because procurement teams will — and should — ask. For documentation, see Trust & Security or contact us for a security review packet.
When Skyview makes sense
The private AI cloud is the right fit if your organization has any of the following:
- Sensitive data that shouldn't be sent to public AI APIs — commercial IP, client matter data, health records, financial records, constituent data, internal systems of record.
- Regulatory or procurement requirements that rule out public multi-tenant AI services — HIPAA, StateRAMP, FedRAMP-adjacent work, financial industry compliance, state-level data residency rules.
- Inference volume at which per-token pricing has become a line-item concern.
- Latency requirements that a public API's tail latency cannot meet consistently.
- A clear AI capability you want to deploy but no internal team to build and operate it.
Let's talk about your workload
Every organization's AI infrastructure needs are different. A 30-minute conversation is usually enough to tell whether our platform fits yours.