Philipp Bühler

Student · Entrepreneur

I design and builddecision systems —software, models,businesses.

Master’s student and founder of Bühler Digital Solutions. I build web platforms and AI-assisted workflows for real businesses, and I am writing a thesis on how a reinforcement-learning agent compares to classical optimization when the objective is risk-adjusted return.

Selected workResearchContact
Diagram: one path through five connected domainsFive stacked horizontal layers labelled Engineering, AI & Data, Product & Business, Entrepreneurship, Research & Finance, threaded by a single continuous line that passes through one node on each layer.ENGINEERINGAI & DATAPRODUCT & BUSINESSENTREPRENEURSHIPRESEARCH & FINANCE
One path — not five separate skills
02Profile

Short version

I work at the intersection of technology, business and analytical decision-making. What connects my projects is an interest in turning complex or uncertain problems into systems that are structured, understandable and useful in practice.

My approach begins with the structure beneath a problem: its assumptions, decisions and avoidable complexity. Clarity matters, but not when it comes at the expense of what is essential.

Technology should expand human capability while preserving understanding, judgment and responsibility. The strongest systems make their limits visible and remain open to question, adaptation and improvement.

“The best way to predict the future is to invent it.”

— Alan Kay

A boundary worth stating

This is a personal site. Bühler Digital Solutions is a separate company with its own website — mentioned here because it is part of the work, not presented here as an offer.

Based in
Germany, Baden-Württemberg
Currently
Master’s studies · entrepreneurship
Working on
Web platforms · AI workflows · portfolio optimization · trading bot
Background
Mercedes-Benz · KPMG · self-employed
03Selected work

What I have built

Three entries: the practice I run, the research I am writing, and the first site of mine that went live.

01Operating

Bühler Digital Solutions

Company

Context
Small and local businesses usually need one working digital thing — a site that earns its keep, a process that stops being manual, a workflow that survives a busy week. What they are typically offered is an agency retainer.
Approach
A one-person practice scoped per project rather than sold as packages: web development and design, digitalisation, AI-assisted solutions, automation of business processes, search visibility, and ongoing maintenance where it is actually agreed.
Role
Founder

Stack

  • Web development
  • Web design
  • AI solutions
  • Automation
  • SEO
  • Maintenance

Company website URL

02In progress — no results yet

DRL Portfolio Optimization

Master’s thesis · research

Context
Mean-variance optimization solves a static problem at every rebalancing date and leans hard on an estimated covariance structure. It is elegant, it is the benchmark for good reasons, and it assumes a stability that markets do not offer.
Approach
Train a PPO agent to allocate across assets under three risk profiles, then compare it against mean-variance optimization and a market portfolio on identical data and identical constraints. Explainable-AI methods are scoped as an optional extension, so the agent’s allocations can be interrogated rather than only scored.
Role
Author — thesis in progress

Stack

  • Python
  • PPO / reinforcement learning
  • Portfolio theory
  • Backtesting
03Live

Gesundheitspraxis Johe

Website · first project to go live

Context
A health practice with no web presence. This was the first site of mine to go live — built as a favour rather than as a commercial engagement, and listed here as a reference rather than as a case study.
Approach
A static, fast site on the stack I use for client work, with a content layer the practice can maintain without me. The full write-up — brief, constraints and outcome — is not published, and nothing is claimed here that has not been confirmed.
Role
Design and implementation

Stack

  • Astro
  • React
  • Tailwind CSS
  • Keystatic CMS
  • Cloudflare Pages

Visit (opens in a new tab)

04Research · master’s thesis

Reinforcement learning for portfolio allocation

The question is not whether a learned policy can beat a benchmark on one backtest — that result is cheap and rarely survives. It is whether a policy trained under an explicit risk profile allocates differently from a classical optimizer in a way that holds up when both are given the same data, the same universe, and the same constraints.

Schematic risk–return comparison, not measured resultsA risk versus return chart showing the comparison being set up. A curve marks the mean-variance efficient frontier and a single point marks the market portfolio. At each of the three risk profiles — Conservative, Balanced, Aggressive — the PPO agent is drawn as a vertical bracket with a question mark, because its return is not yet known. No result is shown.CONSERVATIVEBALANCEDAGGRESSIVEMVOMARKET???RISK →RETURN →
  • Mean-variance frontier
  • Market portfolio
  • PPO — not yet measured
Schematic, not measured. The axes show the comparison being set up — risk against return, three strategies across three risk profiles. Positions are illustrative and carry no claim about outcomes.

Working title

Deep-reinforcement-learning-based portfolio optimization using PPO, compared against mean-variance optimization and a market portfolio.

Strategies compared

  • PPOLearned allocation policy, trained per risk profile
  • Mean-VarianceClassical optimizer — the benchmark
  • Market portfolioPassive reference

Scope

  • Proximal Policy Optimization (PPO)
  • Mean-variance optimization (MVO)
  • Market portfolio as a passive reference
  • Three risk profiles
  • Risk / return trade-off
  • Explainable AI (optional extension)
05Experience

Where the work happened

Roles and periods are as supplied by me. Where tasks and outcomes are not published yet, that is stated rather than filled in.

  1. 2026 — presentCurrent

    Bühler Digital Solutions

    Founder

    Web development and design, digitalisation, AI-assisted solutions, automation, search visibility, and maintenance for small and local businesses.

  2. 2026 — 2027Current

    Master’s thesis · research

    Author — deep reinforcement learning for portfolio optimization

    PPO-based portfolio allocation across three risk profiles, benchmarked against mean-variance optimization and a market portfolio. In progress; no results yet.

  3. 2024 — 2027Current

    Reutlingen University

    M.Sc. Business Informatics (Wirtschaftsinformatik)

    Ongoing master’s programme; the thesis above is part of it.

  4. 03/2025 — 03/2026

    KPMG

    Working Student

    Tasks, focus and outcomes are not published here.

  5. 2022 — 2023

    Mercedes-Benz AG

    Working Student · Intern

    Tasks, focus and outcomes are not published here.

  6. 2020 — 2024

    Reutlingen University

    B.Sc. Business Informatics (Wirtschaftsinformatik)

    Undergraduate degree preceding the master’s programme.

06Capabilities

Four kinds of problem

Grouped by the kind of problem rather than by tool. Areas of active work — not a certification list, and deliberately not a wall of logos.

01

Engineering

Building the thing so it survives contact with a second developer.

  • Java · JavaScript · TypeScript
  • HTML & CSS
  • Astro & static site generation
  • Component architecture
  • Database design & management (SQL)
  • Accessibility and performance budgets
  • Git-based workflows
02

AI & Data

Getting useful, checkable work out of models and data.

  • Machine learning & data science (Python)
  • Reinforcement learning (PPO)
  • LLM-assisted engineering workflows
  • Agent orchestration & separated responsibilities
  • BI dashboards (Power BI, Tableau, SAP Analytics Cloud)
03

Product & Business

Deciding what is worth building, and for whom.

  • Project management
  • Scoping and requirement clarification
  • Business process automation
  • Consulting
  • Search visibility (SEO)
  • Teamwork & communication
04

Research & Finance

The quantitative side, and the honesty it demands.

  • Academic research & scientific writing
  • Portfolio theory
  • Mean-variance optimization
  • Risk / return analysis
  • Backtesting methodology
  • Explainable AI
07Development

Build activity

Public GitHub activity, read at build time. Only what the API actually returns is shown — no invented figures, and no vanity numbers padded to look busier.

Public repositories

Languages in use

  • Python
    96.2%
  • PowerShell
    3.8%

Profile

@
Belaunsch
On GitHub since

View profile on GitHub (opens in a new tab)

Data fetched ·
Refreshed each time the site is rebuilt.

08Current focus

Open right now

What is actually open on the desk right now.

  • Ongoing

    Building Bühler Digital Solutions

    Turning a practice into something repeatable: offer structure, delivery standards, and the maintenance side that most one-person operations under-plan.

  • In progress

    Master’s thesis — DRL & portfolio optimization

    Setting up the PPO agent, the risk profiles, and a comparison against mean-variance optimization and a market portfolio that is fair by construction.

  • Continuous

    AI-assisted engineering workflows

    Working out where an agent genuinely reduces effort and where it just moves the review burden somewhere less visible.

09Contact

Get in touch

LinkedIn is the fastest route. The email address is deliberately not printed here yet — see below.