For consultancies, agencies & infrastructure providers

Independent AI implementation partner for client projects.

I collaborate with firms whose clients are asking for practical AI systems: private RAG, document workflows, customer-channel automation, production hardening, and deployment on infrastructure the client controls. I can work as a named specialist, alongside your team, or quietly in the background, in German, English, or Spanish.

Portrait of Daniel Panea, AI implementation partner for client delivery
Daniel Panea, independent AI implementation partner
01 - When collaboration makes sense

Situations where a technical implementation partner helps.

Your client is asking for AI implementation

You advise, sell, host, or integrate systems, and now a client needs someone to turn the idea into a working private AI system.

The client needs controlled infrastructure

Your client requires on-prem, private, or GDPR-conscious deployment, and a generic cloud setup is off the table.

A pilot is stuck before production

A promising pilot is stuck on the way to production and needs hardening, evaluation, and a path to reliability.

You need technical depth in pre-sales

You need a technical voice in a pre-sales meeting to scope honestly and win the client's confidence.

02 - How collaboration works

Built to protect your client relationship, not compete with it.

I collaborate around your client relationship. The terms are designed so you can bring me into client work without turning the relationship into a risk.

Named or behind the scenes

I can work as a named specialist, alongside your team, or quietly in the background, whichever fits the client relationship.

Written no-client-poaching commitment

I sign a written commitment never to approach your client directly, so the relationship stays yours.

NDA and DPA as default

I treat confidentiality and data-processing agreements as the starting point, not an afterthought.

SSH/VPN on end-client infrastructure

I work through SSH or VPN on the end client's own infrastructure, with no confidential data copied off-site.

Documented handover

Your team receives documented architecture, decisions, and operations so the system stays yours to run.

03 - What I deliver

Private AI systems that survive contact with production.

Private RAG & knowledge systems

Internal knowledge assistants and retrieval systems that answer from the client's own data and cite their sources.

Document intake & triage pipelines

Document intake, extraction, and triage pipelines, including OCR for scanned and mixed-media inputs.

Production hardening

Observability, restartability, and evaluation so a pilot becomes a system a team can rely on and maintain.

Customer-channel automation

WhatsApp and chat automation that handles real customer conversations across languages.

Inference & GPU optimization

Open-source model serving, latency, cost, and GPU optimization when private deployment economics matter.

Live demo

A working private-AI demo you can show your client.

Private company-knowledge AI, running on synthetic data with a citation for every answer. Point to it in pre-sales to make private RAG concrete, with no login and no email gate.

Synthetic data No login Cited answers
04 - Delivery stories

Two anonymized deliveries.

Details are anonymized to respect client confidentiality. Both ran inside infrastructure the end client controlled.

Public sector delivery

ITSM ticket intelligence pipeline

Problem
A chaotic backlog of mixed-media support tickets hid recurring problems, root causes, and operational priorities.
Constraint
Internal operational data with strict data-control requirements, so a generic cloud workflow was not an option.
Outcome
A secure local pipeline using OCR and LLMs to summarize, cluster, and surface root causes, running inside controlled infrastructure and delivered under the partner's brand.

Customer-channel automation

Multilingual WhatsApp booking system

Problem
Customers wanted to book and manage appointments by chat, across languages, without staff handling every message.
Constraint
It had to run on open-source models for cost and data control, while staying reliable enough for real customer traffic.
Outcome
A multilingual WhatsApp booking assistant on open-source models, with human fallback and evaluation, handling real conversations end to end.
05 - Working models

Engagement models that fit how service providers buy specialist capacity.

Day rate

On request, for defined delivery work where you need technical implementation capacity by the day.

Retainer

4–8 days per month of reserved capacity, so you can promise clients a reliable specialist resource.

Paid scoping

A paid scoping engagement to support fixed-bid proposals with a realistic architecture and estimate.

Free fit call

A free 30-minute call to check whether a specific project is a fit before anyone commits.

Next step

Talk through an AI project or client request.

Tell me what your client is asking for, where the project stands, and what kind of implementation depth would help. In 30 minutes we can usually tell whether collaboration makes sense.