AI Agents & Assistants
Multilingual assistants that answer questions, pull information from internal systems and hand over to a human with full context when it matters.
AI & Automation · Data Science
I'm Deniz — a data scientist who spent almost a decade automating processes where being wrong is expensive: credit risk at Deloitte, data analytics in risk management. These days my work is mostly AI agents and the kind of automation that still behaves on a bad day.
Focus
The problems I keep coming back to — from a single assistant to a back office that runs without anyone retyping anything.
Multilingual assistants that answer questions, pull information from internal systems and hand over to a human with full context when it matters.
Invoices, onboarding, approvals, follow-ups, reporting — repetitive workflows rebuilt to run from trigger to result without manual steps.
Getting CRMs, ERPs, mailboxes, spreadsheets and internal tools to talk to each other, so data flows instead of being copied by hand.
Reading, extracting, validating and classifying documents, emails and contracts automatically — with human review kept where it counts.
Dashboards, forecasts and risk alerts that turn raw data into decisions, built with the rigour that regulated risk work demands.
Credit risk and scoring models in Python, including explainable-AI methods — the field I spent most of my career in.
Why automate
Every hour spent copying data, answering the same question or chasing a follow-up is an hour the work itself never needed. Most of it can run on its own.
Examples
Illustrative scenarios, not client work — to show the shape of the problems rather than claim results.
Sales & Real Estate
Response time from hours to seconds
Hospitality & Healthcare
Fewer repetitive calls, more bookings after hours
Finance & Back Office
Manual data entry reduced to exception handling
Approach
Map how the work actually happens today, not how the documentation says it does.
Separate the steps that genuinely repeat from the ones that only look like it. Not everything should be automated.
Implement in small, verifiable pieces, with the people who do the work checking each one.
Compare against the manual baseline. If it does not actually save time, it goes back on the bench.
About
For almost a decade I have automated complex processes where precision is non-negotiable: as a Data Scientist at Deloitte for leading international banks, and in data analytics for risk management. Data must be right, processes must be auditable and mistakes are expensive.
That background shapes how I think about AI: a demo that works once is easy, a system that still behaves on a bad day is the actual work. I care about the second kind — and about being honest when a process is better left to a person.
M.Sc. Business Mathematics · Credit Risk & Data Science
Graduated with distinction (1.2); thesis on modelling market dynamics with neural networks.
Deloitte · Data Scientist · Credit Risk & Machine Learning
Automated and modelled credit risk processes in Python for leading international banks, including explainable-AI models.
Banking · Data Analytics in Risk Management
Builds the analytics and tooling behind risk reporting, and turns manual risk processes into fast, automated pipelines.
Today · AI & Automation
Building AI agents and automations, and writing about what actually holds up outside a demo.
Contact
Questions, ideas or just want to say hello — write me a line and I'll usually get back to you within a day.
Frankfurt am Main · Germany