AI Infrastructure Readiness Assessment
A 4–6 week, vendor-neutral assessment of compute gaps, facility and network capacity, data boundaries and candidate use cases, delivering a roadmap and budget range.
Industry Manufacturing / retail / pharma / finance / professional services
01Customer situation
The enterprise has several AI needs (service knowledge base, document processing, visual inspection, operations assistant) but is unsure what compute it needs, whether existing facilities and networks can carry it, where data should live and which use case to start with. Vendor proposals contradict each other and there is no independent basis for a decision.
Typical issues- No workload-based estimate of whether to buy GPUs, rent compute or use APIs
- Existing facility power, cooling and network capacity for high-density GPUs has never been assessed
- Data-residency and transfer boundaries are undefined, so compliance cannot give an opinion
- Many candidate use cases, no method to rank them by value and risk
- Vendor proposals centre on their own products and lack neutral TCO comparison
02Business risk
- Buying before assessing leads to idle compute or facility rework
- Starting PoCs before the data boundary is set, then being stopped by compliance
- Choosing the wrong first use case and losing internal confidence in AI investment
- Network and storage become the bottleneck and GPU utilisation stays low
From customer-authorised cases; measurement basis under “Evidence”. Your results depend on the baseline assessed for your project.
03Target architecture
Deliverables
| Deliverable | Content |
|---|---|
| Current-state report | Assets and dependencies, facility power / cooling / network capacity, data-asset distribution |
| Compute sizing | Per-use-case workload estimates; 3-year TCO across self-built / hosted / API paths |
| Data-boundary design | Classification, residency and transfer controls (for legal review) |
| Use-case ranking | Scoring table, first-wave PoC recommendations and acceptance metrics |
| Roadmap | Phased architecture, budget range, selection criteria, procurement advice |
Our position
- Vendor-neutral: GSS is not tied to any GPU or model vendor; sizing is based on your workload.
- Network first: AI workloads demand more from bandwidth, latency and reliability than traditional applications, so network and compute are assessed together.
- No AI for its own sake: every candidate must have a measurable business metric or it does not reach the PoC list.
04GSS scope of responsibility
- Assessment method, interviews, sizing model and all deliverables
- On-site survey and capacity assessment of facilities and network
- TCO comparison of the three compute paths and neutral selection criteria
- If GSS later implements, the assessment fee can be credited as agreed
- Business goals, candidate use cases and data-asset information
- Final legal and compliance judgement on the data-boundary design
- Internal decision and budget approval on the roadmap
Solution components
Unified AI API Gateway
One API to govern enterprise AI: connect supported global and China-based models, and apply routing, budget, key-management, metering and audit policies centrally.
Learn moreAI Infrastructure
Production-ready AI infrastructure, delivered and operated — compute, high-speed networking, storage, facilities integration, edge inference and cross-border connectivity for private AI workloads.
Learn moreEnterprise Connectivity
Enterprise connectivity, delivered end to end. GSS coordinates carriers, local loops, cross-border segments, CPE and on-site work under one design for DIA, IEPL, MPLS, IP-VPN and SD-WAN.
Learn more05Engagement model
- Standard assessment (4–6 weeks)
- Fixed scope, deliverables and price; suits enterprises with 1–3 candidate use cases and 1–2 facilities.
- Extended assessment
- Multi-site, multi-business-line or cross-border estates, priced by scope; may include one small-scale PoC.
06Evidence and measurement basis
- The assessment is an independent consulting deliverable and is not conditional on buying further GSS services; deliverables belong to the customer.
- "Planning to delivery — 1 team" means assessment, design, implementation and operations can be carried by the same GSS team; it is not a commitment on implementation duration.
- "Infrastructure cost ↓ 20–35%" is the range of 3-year TCO improvement after assessment in consent-published cases; your result follows your own assessment.
- The assessment does not promise model quality or business ROI; these depend on customer data and application design.
Related cases
Cross-border AI Compute Circuits and Multi-cloud GPU Scheduling
The Shanghai R&D team trained on GPU clusters in Singapore and Tokyo, but moving 40 TB daily datasets over the public internet took 20+ hours and kept failing; China data-export rules required an auditable dedicated channel; GPU utilisation across the three sites sat at 35% with queues in one place and idle cards in another.
Auto-parts Manufacturer Multi-plant Smart Cloud and AI Inspection
Inter-plant MPLS was expensive and slow to scale, and ERP / MES moved to cloud with degraded experience; nightly data sync with German HQ took over six hours; manual visual inspection on the line missed defects and left no traceable quality data.
Multinational Pharma China AIOps and Compliant Data Centre Operations
China IT was managed remotely by global HQ with only six local staff covering GxP data rooms, a nationwide office network and cloud. Eleven monitoring tools generated 15,000 alerts a month with under 5% actionable, and major incidents took three hours to diagnose. Global HQ required English reporting and audit evidence from any local vendor.
Planning AI access, enterprise connectivity or nationwide operations?
We confirm your requirements and the next step within 1 business day · Sales +86 10 5913 8888 · sales@goldsunshine.net