My team is stuck with repetitive work
Leads, approvals, CRM, finance, inboxes and scattered tasks that should flow safely between tools.
AI × Automation × Agentic Systems
ALPER ZAKHER
Turn Ideas into Intelligent Systems
Alper Zakher — Senior AI Automation Engineer & Agentic AI Developer
I design and build AI agents, business automation systems, LLM-powered products, internal tools, RAG systems and AI-integrated applications for companies.
✓ Inspect real public code
✓ Transparent system architecture
✓ Direct collaboration
THE INTELLIGENT JOURNEYSystems that move ideas forward
◎Understand the challenge
⌘Design the workflow
✳Build intelligent systems
◇Deliver & monitor
What do you need your business to do better?
You don’t need to choose a model or tool first. Pick your challenge and explore an example of the engineering behind a practical solution.
I need answers from my company knowledge
Build useful, source-grounded assistants for internal knowledge, documents or customer support.
I want an AI agent or complete product
A real app with governed agents, integrations, user roles, data and a production-ready experience.
Not sure which path fits?
Tell me the challenge — let’s scope the first step ↗
Your challenge. A practical first architecture.
Choose a few non-personal details and explore a proposed starting architecture. This is a transparent, rules-based learning tool—not a live AI analysis, cost quote or ROI forecast.
Your first blueprint appears here
Select your situation and explore relevant system layers, safeguards and the next step.
ILLUSTRATIVE STARTING BLUEPRINT
THE INTERACTIVE SYSTEMS LAB
Don’t just read about AI. Step inside it.
Choose a business challenge, then click the architecture nodes to explore how information moves through checks, AI, human approval and controlled delivery.
INTERACTIVE WALKTHROUGH · NO LIVE DATA
SYSTEM STAGE 1 / 5
Capture
Accept a lead from an approved channel with a traceable request ID.
Concept simulation only. No real customer data or external actions.
SYSTEM BLUEPRINTFrom first enquiry to approved CRM handoff
INSPECTABLE ENGINEERING EVIDENCE
Go beyond project names. Inspect the thinking.
These diagrams illustrate each implementation’s workflow. They are not screenshots of deployed customer systems or performance claims. Every repository link leads to inspectable source and documentation.
MCP Composer
How can separate MCP tools be arranged into inspectable agent workflows?
01Approved tools
02Workflow plan
03Human review
04Audit output
Universal Doc Parser
How can inconsistent financial documents become reviewable structured records?
01Document input
02Field extraction
03Validation
04Reviewed export
Model Router
How can requests select models under policy, reliability and cost constraints?
01Request
02Policy rules
03Model choice
04Traceable result
LIVE PUBLIC PROJECTS · GITHUB
Explore real work, inspect the code.
Public repositories sync automatically from GitHub. Each card links directly to its source. Open-source implementations are engineering evidence, not invented client results or unverified performance claims.
Public source↗
MCP Composer
MCP tools to composed agent workflows
Last push:
View repository ↗
Public source↗
Model Router
Request to best model/provider
Last push:
View repository ↗
Public source↗
Universal Doc Parser
Arbitrary financial docs to schema-mapped JSON
Last push:
View repository ↗
Public source↗
Supplier Email Parser
Supplier thread to structured PO/procurement data
Last push:
View repository ↗
Public source↗
Shopify MCP Server
MCP tools to Shopify store operations
Last push:
View repository ↗
Public source↗
LLM Billing Engine
LLM usage to metering and billing
Last push:
View repository ↗
No matching projects. Try another search.
Beyond a few workflows. Explore the whole AI systems landscape.
From AI agents, CRM and data engineering to voice, vision, RAG, evaluation and model infrastructure: explore 24 concrete capabilities across eight disciplines. Each entry explains the business problem, engineering approach and safety constraints—not unverified promises.
24Capabilities
08Disciplines
AI +Systems + Automation
WORKFLOWSLead intake & CRM orchestration+Fragmented leads, slow qualification and duplicated follow-up.
Explore architecture & details ↗
The challenge
Fragmented leads, slow qualification and duplicated follow-up.
Engineering approach
Capture consented enquiries, deduplicate, enrich approved fields, qualify intent and route ownership across CRM and messaging.
Governance & safeguards
Consent, API scopes, deduplication, manual escalation and audit trail.
Typical building blocksCRM · Webhooks · LLM · API
Discuss your use case ↗
AGENTSTool-using AI agents+A model can answer questions but cannot safely perform bounded operations alone.
Explore architecture & details ↗
The challenge
A model can answer questions but cannot safely perform bounded operations alone.
Engineering approach
Design agents with typed tools, task limits, retries, stop conditions and verifiable outcomes.
Governance & safeguards
Least privilege, action confirmation, prompt-injection defenses and logging.
Typical building blocksMCP · APIs · Function Calling
Discuss your use case ↗
KNOWLEDGEEnterprise RAG assistants+Company answers are scattered across PDFs, policies and internal portals.
Explore architecture & details ↗
The challenge
Company answers are scattered across PDFs, policies and internal portals.
Engineering approach
Ingest approved documents, chunk, index, retrieve, rerank and answer with citations.
Governance & safeguards
Filter by access rights before retrieval and abstain when evidence is weak.
Typical building blocksVector Search · Reranking · RAG
Discuss your use case ↗
DATAData integration & ETL+Operational data is fragmented across CRMs, spreadsheets and databases.
Explore architecture & details ↗
The challenge
Operational data is fragmented across CRMs, spreadsheets and databases.
Engineering approach
Build event pipelines, normalization, validation, deduplication, warehouse models and monitoring.
Governance & safeguards
Schema contracts, PII minimization and failure replay.
Typical building blocksSQL · Python · ELT · Warehouses
Discuss your use case ↗
CUSTOMEROmnichannel customer support+Customer context is lost between chat, email, helpdesk and voice.
Explore architecture & details ↗
The challenge
Customer context is lost between chat, email, helpdesk and voice.
Engineering approach
Classify and route conversations, draft grounded answers, maintain tickets and escalate complex cases.
Governance & safeguards
Consent, escalation, identity verification and safe refusal.
Typical building blocksHelpdesk · CRM · RAG
Discuss your use case ↗
MULTIMODALDocument intelligence & OCR+Forms and contracts arrive as inconsistent formats and manual scans.
Explore architecture & details ↗
The challenge
Forms and contracts arrive as inconsistent formats and manual scans.
Engineering approach
Classify pages, extract fields, validate schemas, highlight uncertainty and create reviewer queues.
Governance & safeguards
Redact sensitive fields and require approval for consequential documents.
Typical building blocksOCR · Vision · Schemas
Discuss your use case ↗
PRODUCTSCustom AI applications+A chatbot alone is rarely a complete business product.
Explore architecture & details ↗
The challenge
A chatbot alone is rarely a complete business product.
Engineering approach
Build dashboards, review queues, user roles, APIs, durable databases and production UX around AI.
Governance & safeguards
Authentication, authorization, audit logs and graceful failure states.
Typical building blocksReact · Node · Python · SQL
Discuss your use case ↗
TRUSTAI quality, evaluation & monitoring+Promising demos can silently fail on real workloads.
Explore architecture & details ↗
The challenge
Promising demos can silently fail on real workloads.
Engineering approach
Define success criteria, test datasets, groundedness metrics, traces, alerts and regression tests.
Governance & safeguards
Avoid fabricated benchmarks; report uncertainty and incident pathways.
Typical building blocksEvals · Tracing · Monitoring
Discuss your use case ↗
No matching capabilities. Try another search or category.
FROM DISCOVERY TO PRODUCTION
What turns an AI demo into a dependable system?
A useful AI system needs the complete engineering foundation—not just a model or prompt.
- 01Discover
Map users, workflows, data boundaries and measurable outcomes.
- 02Data foundation
Prepare approved sources, quality checks, retention and access rules.
- 03Intelligence
Choose models, retrieval, tools and integration patterns.
- 04Control plane
Add validation, approvals, failure recovery and execution limits.
- 05Product experience
Build usable interfaces, explanations and error-handling paths.
- 06Operate & evaluate
Test outcomes, monitor quality, cost, security and reliability.
Engineering the ideas into real-world systems.
Alper Zakher designs AI agents, business automation systems, RAG systems, internal tools and AI-integrated applications from Cairo for companies in Egypt, Saudi Arabia, the GCC and internationally.
01 / 08
What I build
AI agents that act through approved tools; workflow automations that connect business systems; RAG and document systems grounded in company knowledge; and production-minded internal tools and AI products.
02 / 08
Business problems I solve
Lead qualification, CRM handoffs, document extraction, support triage, internal knowledge retrieval, reporting, approvals and operational alerts can be mapped into controlled, reviewable workflows.
03 / 08
AI Automation
Connect triggers, APIs, rules, data stores, approvals and reporting. Deterministic workflow logic stays in control while models handle bounded interpretation, extraction, drafting or classification.
04 / 08
AI Agents
Build tool-using agents with scoped permissions, retrieval, structured outputs, approval gates, audit trails, stop conditions and human escalation.
05 / 08
AI Systems
Create the product and data layers around AI: RAG, document pipelines, internal tools, APIs, model routing, evaluation and observability.
06 / 08
Selected case studies
Evidence-led notes explain the problem, constraints, architecture, implementation and available public proof. Results remain qualitative whenever a defensible benchmark is unavailable.
View case studies ↗
07 / 08
Flagship public projects
MCP Composer, Model Router, Universal Document Parser, Invoice AI, Supplier Email Parser and developer tools demonstrate concrete system patterns without being presented as client engagements.
Explore the portfolio ↗
08 / 08
Process
Discovery maps the workflow. Scope defines users, data, permissions and success. Build starts with the smallest useful path, followed by evaluation, staged release, monitoring and iteration.
CLARITY BEFORE COMMITMENT
Good questions. Clear answers.
Who is Alper Zakher?
Alper Zakher is the professional name of Alper Nabil Gabra Zakher, a Cairo-based Senior AI Automation Engineer, Agentic AI Developer and AI Systems Builder.
Does Alper work with companies in Saudi Arabia?
Yes. Alper supports Saudi and GCC companies remotely from Cairo and does not claim a physical Saudi office.
Can Alper connect AI to existing systems?
Yes. Work can include APIs, CRMs, WhatsApp-enabled workflows, databases, document pipelines and internal tools, subject to provider access and security requirements.
A clear path from first conversation to a usable system.
No technology shopping list up front. First we map the workflow, data, permissions and evidence of value—then decide what is actually worth building.
Discovery
Understand the people, bottlenecks, outcomes and systems involved.
Scope & architecture
Define a practical scope, data sources, integrations, approval gates and risks.
Build & validate
Build a focused path, test with approved data and review failures before expansion.
Release & improve
Release with permissions, logs and monitoring; iterate from observed evidence.
Timing, pricing and delivery depend on system complexity, data access and integration scope. We define those before making commitments.
Book a discovery conversation ↗THE NEXT CHAPTER
Your next big idea deserves a real system.
Bring the business challenge, the constraints and the goal. We’ll explore the smallest useful system, with safeguards and a clear next step.