Move from isolated AI demos to governed production systems. Ghaim designs generative AI platforms on AWS that connect to enterprise data, use the right model for each workload and operate with security, observability and cost controls from day one.
We keep the work tied to business outcomes: security, availability, delivery speed, cost control and a platform that can keep evolving after launch.
We design model access, application patterns, networking, security, logging and cost controls around Amazon Bedrock and related AWS services.
The model is only one component; production AI also needs data, identity, evaluation and reliable operations.
We connect approved enterprise data sources to retrieval pipelines so answers can be grounded in current company knowledge rather than relying only on model training data.
Access controls and data boundaries are designed around the users and systems that should be allowed to retrieve each source.
Different models perform differently across language, reasoning, extraction, coding and cost profiles. We structure evaluation around the actual business task.
This avoids locking the architecture to one model before the workload is understood.
Guardrails, identity, encryption, logging, human approval points and data handling controls are designed into the application.
Enterprise AI should be observable and governed like any other production system.
We monitor usage, latency, quality signals and token economics, then iterate on prompts, retrieval, routing and model selection.
This is how AI systems improve after launch instead of becoming expensive proofs of concept.
Ghaim is an Advanced AWS Partner with AWS DevOps, Resilience Services and Cloud Operations Services Competencies, plus local presence through Riyadh and Dubai offices.
Validated capabilities across automation, CI/CD, Infrastructure as Code and cloud delivery practices.
Validated experience designing and improving reliable, recoverable AWS workloads.
Validated CloudOps capabilities spanning governance, operations, observability, auditing and cloud financial management.
Amazon Bedrock is an AWS service for building generative AI applications using foundation models and AWS-native capabilities for security, data integration and application development.
Yes. We design retrieval-augmented generation systems that connect approved enterprise data to generative AI applications.
Yes. We build agentic workflows that can use tools, APIs and enterprise systems in addition to conversational interfaces.
Yes. Architecture should account for data requirements, service availability, security and the organization’s compliance obligations. The upcoming Saudi AWS Region expands the local cloud and AI landscape further.
We use model selection, prompt and context optimization, caching or routing patterns where appropriate, usage monitoring and workload-specific evaluation to manage token and infrastructure cost.
We will route the conversation to the right AWS or AI specialist and define the fastest useful next step.