01
Data Sovereignty
Regulated data cannot leave your perimeter. Private AI eliminates the structural risk of sending proprietary data, customer records, or clinical information to third-party inference endpoints — full stop.
Private AI Platform Engineering
Private AI, operated where your data already lives.
Able Cognition helps regulated enterprises modernize private infrastructure, deploy governed AI workloads, and operate Kubernetes, GPU, and hybrid platforms at production scale — wherever they are on the path today.
Financial ServicesHealthcareTelecommunicationsRegulated Enterprise
Infrastructure-to-AI operating path
Where are you on the path?
Two connected service lanes — VCF Adoption and Private AI Activation — share a single operating path. Customers may enter at Assess or Modernize, then accelerate into AI once the platform foundation is solid. Each stage flows forward until private AI becomes a governed operational capability.
Lane 1 — VCF Adoption & Modernization
Lane 2 — Private AI Activation
01
Assess
Both lanesPEnterprise problem
You know private AI is coming but can't answer the board: Is your infrastructure actually ready? Where are the governance gaps? What's the realistic path and timeline?
RAble Cognition's role
We run a structured readiness assessment — VCF estate discovery, workload placement analysis, GPU strategy, and governance gap mapping — and produce a prioritised, actionable roadmap.
OProduction outcome
A readiness scorecard and reference roadmap your team can act on immediately, with no commitment to further engagement.
VCF adoption services
- VCF readiness assessment
- Estate discovery & dependency mapping
- Consolidation planning
Private AI services
- GPU readiness review
- Private AI landing-zone scoping
- AI governance gap mapping
Each stage has defined entry criteria and exit artifacts
Customers may enter at Assess or Modernize without committing to further stages
Governance gates are embedded throughout, not bolted on at the end
Platform coverage
VCF is the foundation. Private AI is the destination.
VMware Cloud Foundation and VKS give us the private-cloud substrate. Our team designs, deploys, and operates on it — in partnership with VMware and NVIDIA, not as a layer bolted on top. Platform-agnostic beyond VCF — we work with what you already run.
VCF Adoption Lane
Private AI Lane
VCF Foundation
VCF lane — Private-cloud substrate
VMware Cloud FoundationCore
vSphere · NSX · vSAN · SDDC Mgr
VMware Kubernetes ServiceCore
VKS · Tanzu · Cluster API
Aria Automation
Service catalog · IaC · Blueprints
Aria Operations
vROps · Log Insight · Monitoring
GPU & AI Platform
AI lane — Private AI activation layer
NVIDIA AI EnterpriseCore
GPU Operator · DCGM · NIM
NVIDIA DGX / HGX
H100 · A100 · B200 · InfiniBand
GPU Scheduling
MIG · Time-slicing · vGPU
Model Runtime
Triton · vLLM · KServe
Hybrid & Connectivity
Both lanes — Cross-environment continuity
Air-Gap Environments
Registry mirrors · Offline ops
Hybrid Cloud Mesh
Service mesh · BGP · SD-WAN
Multi-Site HA
Stretch clusters · DR
Edge Infrastructure
Remote GPU nodes · MEC
Extended AI Stack
AI lane — Data & model services
KubeFlow / MLflow
Pipeline orchestration · Lineage
Vector Databases
Milvus · pgvector · Weaviate
Model Registry
HuggingFace · NGC · ONNX
NeMo Retriever
RAG patterns · Embedding pipelines
Platform coverage is not exhaustive. We assess your specific stack during the readiness engagement.
Engineering rationale
Why private AI is a durable infrastructure decision.
02
Compliance & Audit
HIPAA, SOC 2, FedRAMP, PCI-DSS, and emerging AI governance frameworks all require provable control over data processing. Private AI gives you the evidence trail that shared cloud AI cannot.
03
Latency & Reliability
Critical workloads — fraud detection, clinical decision support, real-time inference — require sub-50ms response with no external dependency. Private deployment eliminates the variable.
04
Cost Structure
At scale, inference costs on public APIs are unpredictable and compounding. Private deployment converts per-token costs into infrastructure capital — predictable, owned, and optimizable.
05
Platform Governance
Who approved this model? Which version is in production? What data did it train on? Private AI makes these questions answerable through your own control plane, on your own schedule.
06
Operational Ownership
AI capability that lives inside your infrastructure survives vendor changes, API deprecations, and pricing shifts. It compounds in value rather than creating a perpetual external dependency.
Delivery model
One integrated team across the entire path.
Enterprise AI infrastructure projects fail because accountability fragments across specialists who don't share context. You get handoff gaps, blame cycles, and a platform nobody fully owns.
Able Cognition delivers a single team that spans private cloud, AI platform, data, security, and SRE — with one engagement model, one accountable partner, and continuous context across every stage of the path.
Delivery locations
Silicon Valley— Strategy, architecture, client engagement
Visakhapatnam— Platform engineering, SRE, delivery
Hyderabad— AI/ML engineering, data, security
Team disciplines
01
Private Cloud Infrastructure
VMware, Nutanix, bare-metal, storage, and networking platform engineering
02
AI/ML Platform Engineering
Kubernetes, GPU scheduling, model serving, MLOps pipeline infrastructure
03
Data & AI Architecture
Data estate design, vector infrastructure, RAG architecture, model evaluation
04
Enterprise Security
Zero-trust networking, RBAC/IAM, secrets management, compliance posture
05
Site Reliability Engineering
Observability stacks, incident response, SLO frameworks, runbook engineering
06
Governance & Compliance
Policy engines, audit evidence, regulatory mapping, continuous control monitoring
Operational artifacts
Deliverables an operator can act on.
Credibility is concrete. Every engagement produces structured artifacts — not slide decks, not generic frameworks, but documents and catalogs a platform team can actually use to build, operate, and defend their environment.
DOC-001
VCF Readiness Map
AssessVCF lane
Assessment report·18–24 pp
Quantified assessment of private cloud maturity, VCF estate coverage, vSphere/NSX/vSAN gap analysis, and workload placement scoring. Includes consolidation opportunity map and migration sequencing.
Structured document
DOC-002
Consolidation Roadmap
ModernizeVCF lane
Architecture document·24–36 pp
Structured migration-factory plan covering source estate discovery, dependency mapping, wave planning, and rollback procedures. Designed for teams executing VCF adoption at scale.
Structured document
DOC-003
VKS Adoption Blueprint
ModernizeVCF lane
Reference architecture·28–40 pp
Target-state architecture for VMware Kubernetes Service — cluster topology, namespace design, RBAC integration, ingress patterns, and the automation layer that makes VKS self-service.
Structured document
DOC-004
Day-2 Operating Model
OperateBoth lanes
Operating framework·24–36 pp
Documented SRE operating model for VCF and AI platforms — SLO definitions, alerting policies, change management, incident runbooks, cost attribution, and team responsibility boundaries.
Structured document
DOC-005
GPU Readiness Plan
AssessAI lane
Assessment report·16–22 pp
Assessment of GPU infrastructure strategy — hardware selection, MIG/vGPU partitioning model, DCGM observability design, scheduling policies, and total cost of ownership analysis.
Structured document
DOC-006
Private AI Landing-Zone Reference Architecture
DeployAI lane
Architecture document·32–48 pp
Detailed target-state architecture for Private AI on VCF — GPU node integration, NVIDIA stack deployment, network isolation zones, storage tiers, control plane design, and integration points.
Structured document
DOC-007
AI Governance Control Map
GovernAI lane
Control framework·40–60 pp
Mapping of AI workload controls to regulatory requirements (HIPAA, SOC 2, PCI-DSS). Covers model versioning gates, inference audit logging, drift monitoring, and evidence collection procedures.
Structured document
DOC-008
Accelerator Catalog
DeployBoth lanes
Reusable patterns·Living catalog
Library of tested Kubernetes manifests, Helm charts, Terraform modules, and CI/CD templates covering both VCF automation and Private AI deployment patterns.
Structured document
DOC-009
Runbook Library
OperateBoth lanes
Operations documentation·Living library
Operational runbooks covering incident response, GPU failure recovery, model rollback, VCF platform health, observability baseline, and routine lifecycle procedures.
Structured document
Regulated industries
Built for sectors where data control is non-negotiable.
FS
Financial Services
Banking, insurance, capital markets, payments
- Model risk management and SR 11-7 alignment
- Data residency and cross-border restrictions
- Fraud and AML inference at sub-50ms SLA
- Audit trail for all AI-assisted decisions
HC
Healthcare
Health systems, payers, pharma, medical devices
- HIPAA and PHI containment — no external inference
- Clinical decision support with explainability
- FDA 21 CFR Part 11 for software-as-a-medical-device
- Radiology, pathology, and genomics workloads
TC
Telecommunications
Carriers, MNOs, infrastructure providers
- Network operations AI on proprietary topology data
- Customer data sovereignty and DPA compliance
- Real-time inference for network anomaly detection
- Edge AI on private 5G and MEC infrastructure
RE
Regulated Enterprise
Defense contractors, utilities, federal agencies
- Defense, energy, critical infrastructure, government
- FedRAMP, CMMC, ITAR, and sector-specific frameworks
- Air-gap deployment and no-cloud mandates
- Multi-classification data handling
Start here
Build private AI as an operating capability, not a one-off project.
The first step is small and diagnostic — a readiness map that tells you exactly where you stand, regardless of where you are on the path today. No prerequisite infrastructure state required.
Typical readiness engagement
2–3 weeks
Commitment required
None beyond engagement
Output
Scored roadmap + reference architecture
Start a readiness map