About ASP Lab

A practical AI engineering laboratory for organizations.

Purpose

ASP Lab (Automation of Systems & Processes) is a practical AI engineering laboratory. It turns an AI idea into a concrete solution: define the task, choose the approach, build a working solution, evaluate the result and determine the next step.

Strategic goals

Where the laboratory is heading, with the honest status of each goal. Only the first is current work.

  1. Practical AI engineering with verifiable results

    Build working solutions for concrete processes, test them and measure the result.

    Confirmed by projects

  2. Demonstrate before deployment

    A dedicated laboratory AI compute environment where a solution can be run and shown before an organization commits to infrastructure or a model.

    Demonstration concept

  3. From solution to the organization’s own environment

    Reproduce a successful solution where it belongs: local inference, private infrastructure, on-premise, hybrid or external APIs, depending on requirements.

    Planned / R&D

  4. Internal AI knowledge assistants

    Retrieval over approved internal documents, with source-linked answers.

    Developing

  5. Local / Private AI model engineering

    Model selection, local deployment, optimization and later LoRA / QLoRA R&D, with each stage evaluated before it is offered.

    Planned / R&D

Engineering principles

  • Start with one concrete process, not a model.
  • Measure against the starting process.
  • Show limitations next to results.
  • Keep external services visible as external.
  • Evaluation is a discipline, not a final check.

Initiative founder

Portrait of Alexandr Prokhoda

Alexandr Prokhoda

Initiative founder & Systems Engineer

With over 20 years of hands-on systems engineering experience, my work has ranged from assembling hardware, configuring networks and deploying local infrastructure to integrating systems. In recent years, my focus has shifted to practical AI, workflow automation with n8n and quality assurance (QA).

ASP Lab is not about abstract theory. We build working automation setups from real components, test them for reliability and implement them so technology works predictably and consistently.

I share practical experience, automation breakdowns and local LLM tests on my LinkedIn.

Automation and evaluation

The laboratory’s work rests on workflow automation and on testing AI systems: functional checks, adversarial checks and model comparison. It is developing toward Local / Private AI model engineering.

Current, developing and planned capabilities

Kept separate on purpose. “Confirmed by projects” does not mean deployed for customers.

Confirmed by projects

  • AI workflow automation
  • Document processing
  • Local transcription
  • Local LLM experimentation
  • Model comparison
  • AI evaluation
  • API-based AI tools

Developing

  • Retrieval / RAG
  • Systematic inference optimization
  • Specialized datasets
  • Domain adaptation

Planned / R&D

  • LoRA / QLoRA fine-tuning
  • Production local model deployment
  • Dedicated Private AI infrastructure
  • Supported local model API

Start with one process.

Describe what is currently done manually, what data is involved and what result you would like to achieve. You do not need a technical specification or a chosen model to begin.

Fill in the brief
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Please do not share trade secrets, source code, or sensitive personal data before an NDA (non-disclosure agreement) is signed.