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Process Project-Based Contracts

Scoped. Priced.
Delivered.

I take on project-based contracts. We scope the work together, agree on a fixed price, and I deliver. No hourly billing surprises, no scope creep, no ambiguity.

Pricing
Fixed Price
No hourly surprises
Discovery
Free
30-min call, no obligation
Communication
Direct
You talk to me, not a PM
Post-Delivery
2 Weeks
Support included free
// The Process

Four Steps to Shipped

Every engagement follows the same structure. You always know where we are and what comes next.

01

Discovery

We start with a free 30-minute call. You tell me about your problem, your data, and your constraints. I ask questions to understand what "done" looks like for you.

Duration 30 min call, free
You bring Problem description, data samples if possible
You get Honest assessment of feasibility and fit
02

Scoping & Proposal

I write a detailed proposal with clear deliverables, a timeline, and a fixed price. No vague estimates. You know exactly what you're paying for before we start.

Duration 2–5 days
Includes Technical approach, milestones, fixed price
Your cost Free — no obligation
03

Execution

I build. You get regular updates with demos and progress reports at agreed milestones. If something changes, we discuss it before adjusting scope or timeline. No surprises.

Updates Weekly updates, milestone demos
Tools Slack / Teams / Email — your preference
Transparency Shared repo, documented decisions
04

Delivery & Handoff

You get production-ready code, documentation, and a handoff session. I make sure your team can maintain and extend what I built. I don't build black boxes.

Deliverables Code, docs, deployment guide
Handoff Live walkthrough with your team
Support 2 weeks post-delivery included
// What You Get

Working With Me

Direct Communication

You talk to me, not a project manager. I explain technical decisions in plain language and flag risks early, not after they've become problems.

Research-Backed Decisions

As an MSc AI candidate at the University of Freiburg, I bring academic rigor to model and architecture choices. I don't guess — I benchmark.

Full-Stack ML

From data annotation to CUDA-optimized inference. I don't hand off parts of the pipeline to other people. One person, one coherent system.

Production Mindset

I build systems that run in production, not notebooks that demo well. Licensing, cost, latency, and maintainability are part of every decision.

// Best Fit

Ideal Projects

Great fit

  • Computer vision systems (detection, segmentation, tracking)
  • ML pipeline optimization — speed up inference, cut costs
  • Edge deployment with TensorRT / ONNX / Triton
  • AutoML and hyperparameter optimization
  • Technical due diligence on AI products
  • Taking a prototype to production

Less ideal

  • Pure LLM wrapper / chatbot applications
  • Data engineering without ML component
  • Frontend-only work
  • Hourly consulting without deliverables

Ready to start?

Book a free 30-minute discovery call. No commitment, no pitch — just an honest conversation about your project.