Know what to build before you build it.
The Private AI Readiness & Architecture Audit maps your current AI footprint, workloads, sensitive-data boundaries, infrastructure, hardware economics, and success criteria before you commit to a model, GPU purchase, or production architecture.
Approximately 1–2 weeks
The environment before the model.
AI footprint
Current APIs, models, experiments, vendors, internal use, ownership, and known blockers.
Business workloads
Representative use cases, users, context, concurrency, latency, quality, availability, and workflow requirements.
Data + trust boundary
Which information may leave, which must remain controlled, identity boundaries, network constraints, and audit requirements.
Infrastructure
Existing GPUs, CPU/RAM/storage, networking, cloud capacity, deployment tooling, observability, and operational maturity.
Model + runtime fit
Open-weight candidates, licenses, memory envelopes, quantization options, serving engines, and evaluation criteria.
Economics
Owned hardware, private cloud, and hybrid paths evaluated against utilization, growth, engineering effort, and risk.
A decision package, not a slide deck.
The output is designed to let a technical leader decide what to pilot, what not to buy yet, and which assumptions need measurement.
Discovery
Workloads, constraints, sensitive-data handling, current AI use, infrastructure, stakeholders, and business goals.
Technical analysis
Model fit, hardware envelope, serving paths, trust boundaries, operational dependencies, and cost/performance assumptions.
Decision review
Architecture options, risks, benchmark plan, pilot scope, priorities, and the explicit recommendation for what comes next.
Engineering assessment, not a certification audit.
TensorWard can design around environments handling ITAR-controlled technical data and CMMC/FIPS-related requirements. The TensorWard Audit is an engineering and architecture assessment; it is not a CMMC certification, legal determination, export-control opinion, or FIPS validation.