INITIALIZING CONNECTED SYSTEMS

INTELLIGENCE / IN MOTION

STARTUP SEQUENCE00%

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English hero board for the Sahab AI · SweTech Products illustrative design
SweTech Products · Illustrative image
SweTech Products · ST-AI-10260

Sahab AI

Detect cloud waste and recommend cost-saving actions.

Enterprise AICloud Cost OptimizationUAE / GCC / International

AI stack: Anomaly Detection + LLM + Agents

Project details

Project idea

Detect cloud waste and recommend cost-saving actions.

Business need

Enterprise AI teams currently lose time to fragmented tools, manual review and slow decision cycles around cloud cost optimization.

Solution overview

Sahab AI is an AI-first cloud cost optimization product designed to detect cloud waste and recommend cost-saving actions.

Intended users

Operational users, supervisors, managers and enterprise administrators Primary users are Operational users, supervisors, managers and enterprise administrators; secondary stakeholders include supervisors, auditors, administrators and decision makers.

Core workflow

The core workflow connects AI Chat, Suggested Questions, Knowledge Sources, Citations / Evidence with notifications, reports, audit history and role-based approvals.

AI capability

Anomaly Detection + LLM + Agents AI is used through Anomaly Detection + LLM + Agents to turn raw operational input into findings, explanations, recommendations and executable workflow steps. Multi-step planning, tool use, task delegation and human approval. Not core; camera input may support evidence capture only.

Data, integrations and deployment

Data layer combines User input, enterprise systems, operational records, approved knowledge sources; analytics surface trends, exceptions, confidence, workload and operational performance. Integrations: REST APIs, SSO, notifications, reporting/export. Keep integration states visible with sync status, source attribution and audit history. On-premises, private cloud or hybrid; edge deployment where latency/privacy requires it

Arabic and user experience

Bilingual-ready; Arabic RTL recommended for GCC deployment; use clear Arabic labels, RTL navigation, bilingual data fields and culturally neutral contemporary design. UX should prioritize a light premium interface, fast task entry, clear AI evidence, readable cards, realistic maps/charts and responsive mobile/web layouts.

Security and observability

RBAC, audit trail, encryption, data minimization, tenant isolation, human approval for sensitive actions Model telemetry, prompt/run logs, latency, cost, confidence/quality metrics, user feedback

Expected business value

Expected value: Reduce manual work, accelerate decisions, improve traceability and create measurable operational intelligence. Success is measured with Adoption %, task completion time, AI acceptance rate, exception rate, response time, accuracy/precision where applicable.

Illustrative design and folder setup only. Screens, architectures, and prompts in this package are proposed planning material; final visuals and technical behavior still require review. Do not present this illustrative material as a real client deployment. This page presents illustrative material, not a confirmed client deployment or a performance guarantee. Validate scope, data access, privacy, security and model behavior before implementation.