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
Operating path
Where are you on the path?
Enterprises are at different points on a single continuous path. Modernize and Assess are common, low-commitment entry points. Each stage flows forward — until private AI becomes an operating capability, not a one-off project.
01
Modernize
PEnterprise problem
Your private cloud is fragmented — legacy hypervisors, siloed networking, undocumented dependencies. Every AI initiative stalls before it starts because the platform underneath can't support it.
RAble Cognition's role
We consolidate and rationalize your private cloud estate. VMware, Nutanix, or bare-metal — we make every architectural decision AI-ready by design, without forcing a rip-and-replace.
OProduction outcome
A coherent, documented private cloud platform that can absorb AI workloads without heroics.
Each stage has defined entry criteria and exit artifacts
No stage requires completion of a prior stage to begin
Governance gates are embedded throughout, not bolted on at the end
Platform coverage
Built for infrastructure you already run.
Platform-agnostic by design. We work with what you have, not what we prefer to sell.
Private Cloud
VMware Cloud Foundation
vSphere · NSX · vSAN
Nutanix AHV
Prism · Flow · Objects
Red Hat OpenShift
OCP 4.x · RHACM · ODF
Pure Kubernetes
Upstream · Rancher · Tanzu
GPU & Compute
NVIDIA DGX / HGX
H100 · A100 · GPU Operator
GPU Scheduling
MIG · Time-slicing · vGPU
InfiniBand / RoCE
High-bandwidth fabric
CUDA / ROCm
Driver lifecycle management
Hybrid & Connectivity
Hybrid Cloud Mesh
Service mesh · BGP · SD-WAN
Air-Gap Environments
Registry mirrors · Offline ops
Multi-Site HA
Stretch clusters · DR
Edge Infrastructure
Remote GPU nodes · MEC
AI Platform
KubeFlow / MLflow
Pipeline orchestration
Model Registry
Version control · Lineage
Inference Serving
Triton · vLLM · KServe
Vector Databases
Milvus · pgvector · Weaviate
Platform coverage is not exhaustive. We assess your specific stack during 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
Readiness Scorecard
Assessment report·18–24 pp
Quantified assessment of private cloud maturity, data readiness, governance posture, and GPU strategy. Scored across 28 dimensions with gap mapping.
Structured document
DOC-002
Reference Architecture
Architecture document·32–48 pp
Detailed target-state architecture for private AI landing zone — compute topology, network isolation, storage tiers, control plane design, and integration points.
Structured document
DOC-003
Governance Control Map
Control framework·40–60 pp
Mapping of AI workload controls to regulatory requirements (HIPAA, SOC 2, PCI-DSS). Includes evidence collection procedures and audit-ready artifacts.
Structured document
DOC-004
Accelerator Catalog
Reusable patterns·Living catalog
Library of tested Kubernetes manifests, Helm charts, Terraform modules, and CI/CD templates for private AI deployment patterns.
Structured document
DOC-005
Runbook Library
Operations documentation·Living library
Operational runbooks covering incident response, GPU failure recovery, model rollback, observability baseline, and routine lifecycle procedures.
Structured document
DOC-006
Production Operations Model
Operating framework·24–36 pp
Documented operational model covering SLO definitions, alerting policies, change management, cost attribution, and team responsibility boundaries.
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