Khaled Medhat MahmoudOperations & Automation

Operations improvement · Workflow automation · Analytics

Operations improvement and workflow automation, built from the front line.

I am Khaled Medhat Mahmoud, Team Leader, Case Validation at a Global Primary Source Verification Provider. I directly lead an 8-performer core team and mentor an additional 8 Arabic-language specialists (16 case handlers), while designing and maintaining production workflow automations in Google Apps Script that govern and serve the entire 80-performer department across 4 countries.

I work by finding the rule underneath the symptom, writing it down as an explicit condition, and automating the work around it — while deliberately keeping a human on the final judgment.

Read the six case studies Professional profile
SLA & queue design Google Apps Script automation API-connected workflows Operational analytics & reporting Rule codification AI-directed development

What I help businesses do

Three problems I have repeatedly solved

Each capability below is tied to real projects in which I owned the analysis, the design and the build — and the evidence and its limitations are stated rather than implied. The six individual projects are set out card by card in the next section. I improve performance at three connected levels: the people and the conditions they work in; the processes, rules and systems; and the data and implementation that make an improvement measurable and durable.

Stabilize a failing SLA and rebuild capacity when demand outruns the process

A client cut its per-case deadline from 24 hours to 3 hours while volume rose about 15-fold, and four separate queues each ran to their own clock behind a view that refreshed twice a day.

Approach: Diagnosed the slow-case causes, then rebuilt the queue and ordering, made allocation rule-based with caps and eligibility, staggered shift coverage to the real arrival curve, pushed aging alerts to the team chat and corrected an SLA clock that started at the wrong point in the process.

Evidence: Department turnaround moved to 1.5–2 hours against the 3-hour target and held for 12 months (owner-reported, not audited); the queues now refresh every 30 minutes around the clock, up to 48 automated runs a day (documented).

Limitation: The turnaround figure comes from my own operating reports. No audited SLA-compliance percentage exists.

SLA Operations Automation → · Insufficiency Workflow Control →

Remove hand-assembled work and make the operation visible

Two recurring problems: work that had to be assembled by hand from several separate systems, and an operating picture scattered across live sheets and reports, so supervision began with reconciliation.

Approach: Joined the data on its shared key and turned a multi-hour manual routine into one reviewed batch, deliberately leaving the eligibility decision with a person; and built a rule-driven supervision view over the existing live data instead of creating a new data store.

Evidence: 250–300 reminders per run in about five minutes (owner-reported), replacing an estimated full employee-day per cycle; about 80 performers covered across three views and seven management rules (documented), with an estimated 70% less time assembling the view.

Limitation: The time baselines are estimates, not time studies; delivery and response rates were never measured; and the dashboard was received positively by managers but was not adopted into their routine.

Applicant Reminder Automation → · Live Operations Dashboard →

Fix quality at the rule level, not the field

Error reports showed that a performer repeated mistakes, but not which rule they were applying incorrectly — and more than 100 conditional client rules had never been written down as rules at all.

Approach: Consolidated four API-fed sources into one reporting routine, added a short daily assessment that converts results into per-person rule-level coaching, and derived the rule book from 20,000 correct historical cases into a knowledge hub and a grounded assistant with a human audit step.

Evidence: About 8,000 assessment responses analyzed across an 80-performer base (documented); 100+ rules codified, and in a random 100-case sample 95 carried no field error (owner-measured sample, point in time).

Limitation: The assessment-to-error link is a prioritized hypothesis, reported quality movement is shared across three concurrent initiatives, and no time saving, cost saving or ROI is claimed.

Operational Analytics & Quality → · AI-Assisted Validation Engine →

Selected evidence

A few figures, each with its basis

These are the figures that best explain scale and outcome. Every one carries the classification used in the source documents.

6–8 h → 1.5–2 h
Department-average turnaround, against a 3-hour client SLA, sustained for 12 months.
Owner-reported
Up to 48
Automated queue refreshes a day — every 30 minutes, around the clock, across four queues.
Documented
95 / 100
Cases in a random sample handled with the validation assistant that carried no field error.
Owner-measured sample
~80
Department case handlers governed by my systems — 16 performers directly led and mentored.
Documented / operational scope

How to read a number here. Documented means the structure, rules or cadence are established from the live work. Observed means it was seen in the running system. Owner-reported means it comes from my own operating reports and has not been independently audited. Owner-measured sample means a manual sample at a point in time. Estimate means avoidable effort, not a measured or financial result. No on-time percentage, cost saving or ROI is claimed anywhere on this site.

Featured case study

Start here: stabilizing a 3-hour SLA at 15× volume

Selected as the strongest single example

SLA Operations Automation

August 2025 – October 2026 · Live since 1 October 2025 · Owner-reported outcomes

A client cut its per-case deadline from 24 hours to 3 hours while daily volume surged from about 100 cases to peaks above 2,000. For two months the department average stayed at 6–8 hours and expiry notices kept arriving.

I diagnosed the causes rather than the symptoms, then rebuilt the operating model around them: a self-refreshing queue, oldest-first ordering with a live elapsed clock, rule-based allocation with caps and eligibility, automated two-hour aging alerts, and shift coverage staggered to the arrival curve across four countries. I also built and maintained the internal knowledge base the team relies on.

Why this one leads. It has the highest business significance, the broadest personal ownership — sole analyst, designer, builder, tester and maintainer — and the longest sustained operating outcome of the six. It also shows the thing I actually do: combine operational judgment with automation, instead of treating them as separate jobs. The honest caveat is that its headline result is owner-reported; the validation engine carries the strongest measured evidence of the six.

Read the full case study Compare all six

The full body of work

Six projects, one consistent method

Six real systems, all designed, built or maintained by me between 2025 and 2026 inside the same Google Workspace environment. Across all six the pattern is the same: find the rule underneath the symptom, redesign the flow, automate the retrieval, make it visible, add the control, and codify the knowledge so the improvement stays.

Live since Oct 2025

SLA recovery & capacity

SLA Operations Automation

A client cut the per-case SLA from 24 hours to 3 hours while daily volume grew from about 100 cases to peaks above 2,000. I rebuilt the queue, allocation rules, shift coverage and alerting around the new deadline.

  • 6–8 h → 1.5–2 h average TAT (owner-reported)
  • 15× volume growth
  • Rule-based allocation at 70–80 scale
Read the case study
In use since Aug 2025

Workflow automation

Applicant Reminder Automation

A new client went live without the automatic missing-document follow-up. I built the join across three data feeds and a reviewed batch-reminder workflow, so prepared reminders stopped being assembled by hand.

  • 3 API feeds joined on the barcode key
  • 250–300 reminders per run (owner-reported)
  • Eligibility stays a human decision
Read the case study
Live since Jan 2026

Queue control & SLA visibility

Insufficiency Workflow Control

Four insufficiency queues with client-specific deadlines were tracked in a workbook assembled by hand twice a day. I replaced that with scheduled refresh, queue routing, per-queue SLA clocks and deadline alerts.

  • 30-minute refresh, 24/7 (documented)
  • Up to 48 automated runs a day
  • Alerts one hour before each deadline
Read the case study
Deployed Apr 2026

Management visibility

Live Operations Dashboard

An 80-performer department's operating picture was split across live sheets, daily reporting and separate turnaround reports. I built one rule-driven supervision view for productivity, quality, SLA exposure and performer status.

  • ~80 performers, 3 views (documented)
  • 7 automatically evaluated rules
  • ~70% less assembly time (estimate)
Read the case study
In daily operation

Analytics & quality improvement

Operational Analytics & Targeted Quality Improvement

Leadership needed one reliable daily picture, and error reports stopped at the field level rather than the rule level. I built one reporting routine for the management view plus an assessment loop that turns error patterns into a rule-level coaching agenda.

  • 4 API-fed sources consolidated
  • ~8,000 assessment responses (documented)
  • Rule-level coaching, not general retraining
Read the case study
Live · team use

Knowledge codification & AI

AI-Assisted Validation Engine

A validation team transcribed 15–20 fields per case under more than 100 conditional rules that had never been written as rules. I wrote the rule book and built the tools that apply it, moving the team from transcribing to auditing.

  • 20,000 cases as the evidence base
  • 100+ rules codified (documented)
  • 95 of 100 sampled cases error-free
Read the case study
Open the project index

Capabilities

What I bring, and what I do not claim

A curated view of what the six projects actually demonstrate, grouped the way the work connects: people, processes, systems, data and delivery.

Capability groupDemonstrated in practice
People performance & enablement Directly leading 8 performers plus functional mentorship and training for 8 Arabic specialists (16 performers); building department-wide operational systems, knowledge bases, and rule-level coaching routines that serve all ~80 case handlers and operational managers.
Operational improvement & process design Root-cause analysis and structured problem solving; SLA clock-origin and capacity design; demand-curve to shift design; queue and eligibility design; prevention over correction; metric and denominator design.
Workflow automation & data flow Production Google Apps Script automation with time-driven triggers; fixed-cadence API retrieval; multi-source joins; rule and eligibility engines; state write-back and grace-window reconciliation; error handling and human-in-the-loop gates.
Operational analytics & reporting Consolidating multiple API-fed sources into one routine; KPI and threshold design; management reporting written for a one-minute read; productivity, quality, inflow and turnaround measurement; auto-generated summaries.
Quality & knowledge systems Rule codification and knowledge engineering; deriving rules from 20,000 historical cases; assessment and question-bank governance; rule-level coaching with re-test; single-source-of-truth data governance.
Project delivery & continuous improvement Requirements definition and scoping; risk and control decisions; testing and validation against live data; phased implementation; DMAIC used as a working method (not a certification); a completed Google Project Management Professional Certificate.

AI-directed development runs through all of the above: I specify the requirements, rules and acceptance conditions; AI generates the Apps Script; I review it, test it against live data, correct it and maintain it in production.

Technical Agility & Execution. While my documented production systems operated within my employer's mandated Google Workspace infrastructure, my core capability is stack-agnostic. By defining explicit business rules and directing AI as an implementation partner, I can architect and adapt automated solutions across modern operational platforms, APIs, and scripting environments as business needs dictate.

Roles this maps to: Operations Improvement · Process Improvement · Business Analysis · Business Process Automation · Operations Analytics · Business Systems. Operational Excellence and operations management are directions I am still testing, not titles I claim.

Background

The operating record behind the work

Four years with one employer, moving from front-line verification to team leadership while building the systems the work runs on.

February 2022

Associate I — Verification

Joined on the front line: receiving case documents and entering data into the internal case system. This is where I learned the pipeline that every later redesign depends on.

2023 – June 2024

Quality, then Validation & Insufficiency

Reviewed case quality across earlier stages — and first noticed the difference between a field-level error and a rule-level error that later shaped the analytics work. Promoted to Senior Associate II in June 2024.

April 2025 – October 2025

Lead Associate III, then Team Leader

Led operational improvements while still a lead performer, shipping the reminder workflow and starting the SLA rebuild. Promoted to Team Leader, Case Validation in October 2025.

October 2025 – today

Team Leader — and the automation function

Directly lead 8 performers and mentor 8 additional specialists, while serving as the operational automation function for the entire 80-performer department. Designed and deployed 6 production systems that govern departmental queue flow, SLA exposure, and management reporting across all shifts.

Read the full professional profile

Education & professional development

Credentials, with their real status

Completed

The Google Project Management Professional Certificate is a six-course Google program. It is not the PMI PMP and is never referred to as one.

In progress

  • Six Sigma coursework
  • Six Sigma Green Belt program
  • Google Data Analytics Professional Certificate
  • Google AI Professional Certificate

All four are underway, with completion expected around early 2027. No belt or certificate is claimed until it is earned and dated.

Languages

Arabic (native) and English (professional working proficiency, self-assessed).

Environment

Google Workspace: Sheets, Apps Script, Forms, Slides, Chat, Gmail, Sites, AI Studio and NotebookLM, working against the company's operational database and API endpoints.

Contact

Let us talk about the operation you need to fix

If you are hiring for operations improvement, process design, workflow automation, business analysis or operational analytics — or you want a specific bottleneck diagnosed — the fastest route is to send me a short description of the problem and the deadline it has to meet.

I am also open to focused freelance engagements in operations improvement, workflow design and automation — starting from one specific bottleneck rather than a broad program.

Location

Egypt · remote and flexible working
The company operates across Egypt, Jordan, India and the Philippines.

Download Public CV (PDF) Download Executive Profile (PDF)