Cloud & Model Engineering
Deploy and scale securely in your cloud with the right LLM/SLM mix.
Cloud and model engineering deploys AI systems securely in your preferred cloud instance — selecting or developing LLMs/SLMs, applying inference engineering, and establishing governed production workloads.
What this accelerator delivers.
Model choice and cloud deployment are not afterthoughts — they determine whether agents are fast enough, accurate enough, and affordable enough for production. This accelerator selects the right LLM/SLM mix for each workload, applies inference engineering across the stack, and deploys securely in your preferred cloud — model-agnostic, governed, and built for enterprise production. Embedded engineers work within your VPC and security boundaries: no data leaves your environment, no vendor lock-in on model choice, and no compromise on latency or cost. Layer 04 of the 4-Layer Intelligence Stack depends on infrastructure that scales — this accelerator delivers the cloud and model foundation that makes governed agents operational at enterprise scale, with monitoring and cost controls built in from go-live.
Secure deployment in your VPC or preferred cloud
Optimal LLM/SLM mix for cost, latency, and accuracy
Model-agnostic inference engineering
Production infrastructure for Layer 04 outcomes
Concrete artefacts, not slide decks.
Model selection analysis
Workload-by-workload assessment of LLM/SLM options for accuracy, latency, and cost.
Cloud infrastructure
Production deployment in your VPC with security, networking, and scaling configured.
Inference engineering
Caching, routing, batching, and cost controls for production inference workloads.
Performance dashboard
Latency, cost, and accuracy monitoring for all production model endpoints.
Four phases to production go-live.
Embed & discover
FDEs embed inside your business, learn the domain, and scope the highest-value use case for this accelerator.
Unify context
Connect source systems into a governed context layer — MCP, knowledge graphs, and field mapping in your environment.
Configure & evaluate
Select models, deploy cloud infrastructure, configure inference engineering, and validate performance SLAs.
Deploy & monitor
Go live with monitoring, cost controls, and FDEE-led accuracy validation in production.
Where this accelerator applies.
VPC-native agent deployment
Secure agent deployment within banking cloud boundaries and regulatory requirements.
Multi-model claims processing
Right-sized models for document extraction, classification, and reasoning in claims.
Cost-optimised inference
SLM/LLM routing to minimise cost while maintaining accuracy SLAs.
Production outcomes, not pilot metrics.
Production inference latency for governed agent workloads.
Inference cost reduction via optimal model routing and caching.
Uptime for production model endpoints post go-live.
Related accelerators
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Frequently asked questions
- Which cloud platforms do you deploy on?
- We deploy in your preferred cloud — AWS, Azure, GCP — within your VPC and security boundaries. No data leaves your environment.
- Are you locked to specific model providers?
- No. Model selection is workload-specific and model-agnostic — optimising for accuracy, latency, and cost per use case.
- How do you handle inference cost?
- Inference engineering includes caching, routing, batching, and SLM/LLM selection to minimise cost while meeting accuracy SLAs.
- What about data residency requirements?
- All deployment stays within your cloud boundaries — meeting data residency and regulatory requirements for banking and insurance.