Project idea
Convert discovery notes into proposal structure and follow-up actions.
Naseeh AI | AI Project Concept
AI project concept · ST-AI-10219
Naseeh AI
Convert discovery notes into proposal structure and follow-up actions.
AI stack: LLM + CRM RAG + Agents
Project details
Business need
Enterprise AI teams currently lose time to fragmented tools, manual review and slow decision cycles around sales proposal copilot.
Solution overview
Naseeh AI is an AI-first sales proposal copilot product designed to convert discovery notes into proposal structure and follow-up 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
LLM + CRM RAG + Agents AI is used through LLM + CRM RAG + 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, CRM. 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.
Concept 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 fictional concept as a real client deployment. This page describes concept material, not a confirmed client deployment or a performance guarantee. Validate scope, data access, privacy, security and model behavior before implementation.