Company knowledge matters
Customer records, contracts, financials, source code, operations, and internal know-how are not casual inputs.
Private AI implementation
Private AI for companies that value their data.
Systems built around your workflows and deployed where your data needs to stay.
PhD Theoretical Physics / High-performance computing / AI engineering / Based in Europe
01 - Fit
This is for companies that understand their data, internal knowledge, and processes are strategic assets worth protecting.
They want the productivity of modern AI, but they need clear control over what happens to company knowledge, prompts, logs, outputs, access rights, and operational data.
AI needs boundaries
Leadership needs to know where data goes, what gets stored, who can access it, and how it is governed.
Informal use is already happening
Teams experiment with AI because it is useful. The company needs a controlled path before shadow AI becomes normal.
Systems must fit the business
The AI system needs to connect to existing tools, review steps, permissions, and everyday work.
02 - Problem
AI is useful. Uncontrolled AI is risky.
Modern AI can improve speed, quality, and decision-making. But serious companies cannot build around tools where the data boundary is unclear.
Customer records, legal documents, financial information, internal knowledge, source code, operational processes, and strategic plans need explicit decisions about storage, logging, access, retention, and governance.
The solution is not to avoid AI. The solution is to build private AI systems with clear workflow boundaries, data control, human review, and maintainable architecture from the beginning.
03 - Services
A practical path from AI uncertainty to private, production-ready workflows.
Private AI becomes valuable when it reaches real work. You do not have to start at step one. If you already have a use case, a pilot, or an internal technical team, I can meet you where you are. Focused assessments typically start around 2.500€ after a free, no-commitment discovery call.
Assess
Private AI Workflow Assessment
For companies that know they need AI but are not sure where to start, what is safe, or what is worth building.
Outcome: workflow map, risk review, recommended first pilot, and implementation roadmap.
Find the first safe workflowPilot
First Private AI Pilot
For teams ready to build a constrained system around one real workflow.
Outcome: working pilot, data boundaries, review logic, evaluation criteria, and architecture that can grow.
Build the first pilotProductionize
Production AI Workflow System
For companies that have a pilot and need to make it reliable, integrated, maintainable, and usable by a team.
Outcome: permissions, observability, quality checks, integrations, fallback paths, and handover.
Move to productionOptimize
AI Infrastructure & Inference Optimization
For technical teams where latency, cost, private deployment, GPU usage, or open-source model serving matters.
Outcome: serving architecture, cost and latency review, deployment plan, and performance improvements.
Optimize the stack04 - Approach
Private by design. Useful in practice.
The point of private AI is not ideology. The point is control. The work is to turn that control into systems that fit your tools, rules, review steps, and operational constraints.
Control the data boundary
Decide where data, prompts, logs, outputs, embeddings, access rights, and retention policies live before the system becomes operational.
Build around real workflows
Start from the work people already do: documents, handoffs, exceptions, approvals, internal knowledge, and existing software.
Keep humans in the loop
Use automation where it helps, and preserve review, judgment, escalation, and accountability where the business needs them.
Make it maintainable
Production means evaluation, permissions, observability, fallback paths, documentation, and handover beyond the first demo.
05 - Selected work
Practical AI systems, built around real constraints.
Document-heavy workflows, private data, internal knowledge, production pipelines, and systems that need to be useful beyond the demo.
Public sector & enterprise
IT Service Management Automation.
In collaboration with DigitFlow. Now available as a product package.
- Problem
- A chaotic backlog of mixed-media support tickets made it hard to identify recurring problems, root causes, and operational priorities.
- Constraint
- The workflow involved internal operational data and had to respect strict data-control requirements, so a generic cloud workflow was not appropriate.
- System
- A secure local AI pipeline using OCR, LLMs, summarization, tagging, clustering, orchestration, and dashboard output for automated root-cause discovery.
- Result
- Manual ticket triage and root-cause discovery were replaced by an automated clustering pipeline running inside controlled infrastructure, so IT teams could act on cluster-level patterns instead of reviewing tickets one by one.
Privacy tech
Synthetic Data Engine.
Privacy-first model validation for teams that cannot use real records in development or testing.
- Problem
- Models and pipelines needed to be validated, but using real data was impossible because of privacy constraints.
- Constraint
- The generated data had to preserve realistic structure, distributions, and edge cases without exposing sensitive source records.
- System
- Statistical and logical analysis of real data structures to recreate high-fidelity synthetic datasets at scale.
- Result
- Synthetic datasets replaced production records in pre-production testing while preserving realistic structures, distributions, and edge cases.
Education tech
AI for Education.
Backend and AI infrastructure for Mark My Words, supporting real-time student feedback.
- Problem
- Educators needed to provide scalable, personalized feedback on student writing without being buried in repetitive grading work.
- Constraint
- AI could support the workflow, but educator judgment and review needed to remain central to the learning process.
- System
- Backend infrastructure integrating RAG models, fine-tuned LLMs, and asynchronous pipelines.
- Result
- Personalized feedback on student writing now moves through an automated pipeline with explicit educator review before final delivery.
06 - Speaking
Talks on private AI in European companies.
Conference keynote
Private AI in Europe.
Presented at the Spanish National Conference on the Future of AI in Europe: how companies can adopt AI while keeping control over sensitive data, internal knowledge, and deployment choices.
07 - In their words
What clients say.
"Daniel is helping us as a freelancer to build AI and machine learning solutions for our clients. He is delivering exceptionally good work. He is very intelligent and knows what he is talking about."
"Daniel is a brilliant freelancer. Hardworking, smart, creative and personable. I could not recommend him more."
"Professional, smart and friendly. Daniel delivers outstanding work, and has a solid grasp of technical domains. We value his work, and hope to continue engaging with him in future."
"It has been a privilege to have Daniel on our team. From day one, he proved himself an exceptional professional, excelling in every task and constantly improving our projects."
"Daniel is a great asset to have on any team ! He's very knowledgable and smart, always gets the client's needs and delivers the right solution for them. Working with him has been very enjoyable."
"I was consistently impressed by his dedication, professionalism, and ability to quickly develop effective solutions to complex problems. A highly skilled professional and valuable team player."
08 - AI Use-Case Scorecard
Is this workflow a good AI use case?
A 5-minute scorecard to evaluate whether a real business process is frequent, painful, clear, data-ready, and safe enough for a first private AI pilot.
Have a promising use case? Book the assessment09 - Blog
Thinking on private AI, data control, and production LLM systems.
Essay / 2026-04-27
Private AI: What It Means
The flagship definition: private AI means control over the model and control over the data you feed it. Everything else follows from that.
Read full articleEssay / 2026-04-28
Shadow AI: The Disaster Already Happening
What happens when employees are already using AI on company work, and why blocking it is weaker than building a sanctioned private path.
Read full articleEssay / 2026-04-30
Why Small Open-Source Models Often Win in Business
Most business workflows need instruction following, structured outputs, cost control, and low latency. That often points to smaller open-source models.
Read full article10 - FAQ
Questions buyers usually ask before we talk.
What does an engagement usually cost?
The first discovery call is free and carries no commitment. Focused Private AI Workflow Assessments usually start around 2.500€ for a one-week, 25-30 hour engagement. Pilots, production builds, and optimization work are scoped after we understand the workflow, data, risk profile, and internal team capacity.
Do we need to share company data with you?
It is strongly preferred, because I can give a much better assessment when I can inspect representative data directly. If the data is too sensitive to share, we can work around it: your team can run internal analysis and walk me through data quantity, quality, structure, and examples. In that case, the quality of my recommendations depends heavily on the quality and completeness of that internal analysis. For pilots, I usually need controlled access, but I can work through SSH or VPN on your own servers without copying confidential data locally.
Can you sign an NDA or DPA?
Yes, when the terms are properly drafted and make sense for both sides. I expect confidentiality requirements in serious company work. GDPR, the EU AI Act, and processor obligations are treated as design requirements, but legal compliance should still be reviewed by your legal or compliance team.
How long does this take?
A focused assessment is usually about one week. A first pilot often takes a few weeks if the workflow owner is available and the team can move quickly. Production systems and infrastructure optimization depend on the number of integrations, review steps, permissions, and deployment constraints.
What do we need to bring?
At minimum: a workflow owner, access to people who do the work, and a clear view of the data involved. For build work, we also need the right technical access, deployment constraints, and a decision-maker who can resolve tradeoffs around risk, usability, and scope.
What if we already have a technical team?
That is often ideal. I can advise your team on architecture and implementation, collaborate as an AI engineering specialist, or take ownership of a focused build. If the project becomes larger than one person should handle, I can also pull in trusted partner companies.
Who is this not for?
This is not for companies looking for a generic chatbot demo, a loose AI strategy workshop, or a tool recommendation with no implementation path. It is for teams that see data, internal knowledge, and processes as strategic assets and want AI systems that respect that reality.
About
I build AI systems for companies that need both usefulness and control.
I am Daniel Panea, an AI engineer with a PhD in Theoretical Physics and a background in high-performance computing and machine learning.
I work with companies that want to use AI in real operations but cannot ignore privacy, compliance, data ownership, or maintainability. My strength is working across the full path: understanding the business workflow, identifying the right use case, designing the architecture, and building the system end to end.
TechnicalPhD Theoretical Physics / HPC / ML
LanguagesEnglish / Deutsch / Español / Italiano
GeographyBased in Europe. Remote across the world.
Worked with
Next step
Want to build a private AI workflow?
If your company wants AI productivity without giving up control over data, internal knowledge, and business processes, that is usually the right place to start.