Farouk Hajjej
GenAI Solutions Architect | AI Automation Consultant
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Farouk is ready for their next project!
AI Automation & Copilot Enablement
Original framework • Capability overview — not a client case study.
A practical method for identifying repeatable work that can be improved with AI and automation while preserving necessary human judgment.
The framework maps triggers, inputs, decisions, approvals, outputs, exceptions, and system integrations before selecting the technology. The solution pattern can combine Microsoft Copilot, Copilot Studio, Power Automate, Azure OpenAI, APIs, and existing business tools.
Typical deliverables include a workflow map, automation architecture, Copilot behavior design, integration requirements, a governance checklist, an adoption plan, and a phased implementation roadmap.
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AI Agents, APIs & MCP Integrations
Original framework • Capability overview — not a client case study.
A controlled integration blueprint for agentic workflows that connect models to approved tools, data, and business systems through APIs, webhooks, function calling, and MCP servers.
The design defines task boundaries, decision points, tool contracts, data exchanges, authentication concepts, workflow state, retries, error paths, logging, evaluation, and human approval checkpoints.
Typical outputs include an orchestration graph, API and MCP integration map, tool schemas, guardrails, and an implementation plan. The goal is useful automation with permission boundaries, observability, and human oversight—not autonomy for its own sake.
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RAG Knowledge Systems
Original framework • Capability overview — not a client case study.
A blueprint for converting trusted documents and data into useful, traceable AI-assisted answers. The flow begins with the questions users need answered and the sources allowed to support those answers.
It maps ingestion, parsing, chunking, embeddings, retrieval, context assembly, response generation, and citations—alongside freshness, access boundaries, evaluation, observability, and failure handling.
Typical deliverables include a system architecture, document pipeline, retrieval strategy, evaluation plan, and proof-of-value roadmap. The aim is a grounded, governable knowledge system that can improve iteratively.
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GenAI Strategy & Architecture
Original framework • Capability overview — not a client case study.
A practical way to move from a business objective to a buildable GenAI delivery plan. I use this structure to frame the use case, prioritize value and feasibility, define the target architecture, map data and integration boundaries, and sequence a proof of value.
Typical outputs: discovery notes, use-case prioritization, a solution blueprint, governance and evaluation checkpoints, dependencies, risks, and a phased delivery roadmap.
My role sits between business value and technical delivery: shaping the workflow, architecture, and implementation path so teams can build the right system with appropriate guardrails.
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