I build platform-agnostic AI agents, accountable in enterprise finance.
An SAP S/4HANA finance consultant who is not locked into one vendor's AI. I build with SAP, Microsoft Copilot Studio, or a local LLM depending on what the problem and the data demand, and I prove every layer myself: retrieval, agents, integration, deployment, on-premise when data cannot leave the building.
Day Job
SAP S/4HANA finance consultant. I work where process, compliance, and technology meet, which is exactly where enterprises are now putting AI agents to work.
How I Think
I frame the problem, design the solution, and direct AI to build it. The decisions are mine. I own architecture, testing, deployment, and the result. AI handles execution speed.
Cross-platform
Across platforms, not inside one
I build the same patterns on whichever platform fits the problem: SAP, Microsoft Copilot Studio, or open-source and local LLMs. That means I understand what sits under every vendor's agents, not just one. When data cannot leave the building, I build it to run entirely on local hardware.
How I Work
From problem to production
The discipline is the same whether the problem is a broken finance process or a missing data pipeline, and whether I build it in SAP, Microsoft, or a local LLM. Define it clearly, then build the right thing, not just the obvious thing.
Separate the stated problem from the real one.
Define the architecture before touching a tool.
Direct AI to build it, then test against real conditions.
Go live early, govern what runs, and treat first use as the start of learning.
Projects
Proof over theory
Each project is a complete end-to-end build: retrieval, an agent that acts, an integration pipeline, and an oversight layer. Individually each solves a real problem. Together they are one system, the building blocks of agentic enterprise finance.
Local LLM (RAG) for Immigration Questions
Retrieval and grounding: on-premise AI that answers only from cited source text and never invents it.
The Problem
Standard AI assistants either refuse to answer legal questions or invent rules that do not exist. German immigration law spans multiple statutes with hundreds of cross-referenced sections; a single eligibility question can require reading five different parts of the law at once.
What I Built
A system that runs entirely on local hardware, so no data ever leaves the machine, GDPR compliance by design. It retrieves the most relevant legal sections for any question and generates an answer grounded only in those sections, citing exactly where each point comes from.
Enterprise parallel: grounded assistants over SAP, finance, and compliance documentation, on-premise where data cannot leave the building.
Family Finance and Goals AI Bot
An agent that takes action: a conversational bot that turns a message or a receipt photo into structured data.
The Problem
Logging a family expense means opening an app, finding the right category, and typing it manually. The friction is small enough to skip, until weeks of spending disappear. Goals suffer the same way: written down once, never revisited.
What I Built
A Telegram bot that reads a message or a photo of a receipt and writes the expense directly into a shared Google Sheet in under a second. Goals work the same way: one natural-language message creates a tracked entry. Zero apps, zero manual effort.
Enterprise parallel: conversational agents that capture transactions and post them into enterprise systems.
Portfolio Rebalancer
An integration pipeline: it turns a broker PDF into priced holdings, risk metrics, and suggested actions.
The Problem
An ETF portfolio drifts away from its target allocation as prices move, but the holdings sit locked inside a broker PDF, and the maths to quantify the drift, and the risk that comes with it, is tedious and error-prone to do by hand.
What I Built
A Python tool that parses a broker statement into holdings, resolves each position to a live price from Yahoo Finance, computes risk metrics such as Sharpe, Sortino, max drawdown and VaR, then reports the buy and sell trades that would close the gap to target, with an optional LLM narrative. It is educational only: it never places trades and is not investment advice.
Enterprise parallel: document-to-decision pipelines that read statements and surface actions for finance teams.
TreasuryFlow - Governance and Visibility
The oversight layer: live AP, AR, and cash positions across systems, in one view.
The Problem
Once agents and pipelines are acting on finance data, the missing piece is oversight, one trustworthy view of what is happening across systems, so a human can see and own the result.
What I Built
A personal concept demo that pulls AP, AR, and cash positions via API into one live dashboard. It is the oversight layer over the other three building blocks. It is a personal concept demo, separate from any client or employer work.
Enterprise parallel: the governance and visibility layer over a system of finance agents.
Where this is going
The tools to build agents are arriving fast, from SAP and every other vendor. As they get more autonomous, the scarce role is not building one. It is understanding the finance consequence well enough to set the boundaries, prove the output is correct before it reaches the books, and own the result. That is where I focus.