Ahmed Sedik — AI Engineer & Product Builder
AI systems that earn their place in the workflow.
I help companies move from AI experiments to operating systems—agents, RAG, voice and automation connected to real processes, metrics and adoption.
OPERATING MODEL / 01
Company process
→
AI system
→
Measured outcome
Human oversightProduction engineeringAdoption & GTM
Selected outcomes
Systems in the field
Proof over promises.
Product and engineering work spanning voice training, decision intelligence, content operations and enterprise knowledge workflows.
01
Voice AI · Training systems
RoleDrills
Building voice-first AI practice for customer-facing teams, with transcription, rubric-driven advisory feedback and human review designed into the workflow.
02
Decision intelligence · Natural language to SQL
PowerIBI
A privacy-aware analytics product that turns plain-language business questions into optimized SQL and decision-ready reporting.
03
AI operations · Content automation
Reelsaty
Production workflows that connect LLMs, backend services and automation to improve the speed, consistency and economics of real-estate content operations.
04
AI agents · RAG · Reporting
Alkarim
Agents and retrieval pipelines for business queries and automated reporting, engineered around measurable latency, cost and workflow improvements.
How I work
The model is only one component.
Successful AI adoption starts with the business process and ends with a system people trust, use and can measure.
Python · FastAPI · Go · PostgreSQL · Redis · LangChain · Vector search · n8n
- 01Map the process
Find the decision, delay or repetitive work worth changing.
- 02Engineer the system
Design the model, retrieval, backend, data and human controls together.
- 03Measure the value
Track quality, latency, cost, effort saved and operational reliability.
- 04Ship the adoption
Make the value clear to operators, buyers and internal champions.
Where I contribute
From architecture to adoption.
AI workflow integration
Connect agents and automation to the tools, approvals and operating rhythms a company already uses.
RAG & knowledge systems
Build grounded retrieval systems for reporting, decision support and internal knowledge—not generic chat wrappers.
Voice & training AI
Create consent-aware voice experiences with transcription, structured feedback and human oversight.
AI product & GTM
Translate technical capability into a product people understand, adopt and can connect to measurable value.
PYTHON / SYSTEMS / RUNTIME
Now writing
Python: From Metal to Mind
A runtime-first engineering book for Python developers—from execution and memory to concurrency, performance and production systems.
Read the book in progressThe engineer behind the systems
Backend foundations. AI focus. Product perspective.
I started in backend engineering, building services with Go, gRPC, PostgreSQL and Redis. That foundation now shapes how I build AI: production-first, measurable and designed for the reality around the model.
From 2024–2026, I made a deliberate investment in formal Artificial Intelligence study at IU International University while developing independent AI products and deepening my work with agents, RAG and LLM systems.
Based in Germany. Working across engineering, product and GTM with teams moving from AI curiosity to implementation.
Have an AI initiative that needs to become operational?