Neural engineering lab

Reprogram enterprise intelligenceon your terms.

Go beyond off-the-shelf models. We build private LLM architectures that preserve data sovereignty, with fine-tuning and continual learning pipelines that keep your AI ecosystem evolving.

Book a strategy session

On-prem & air-gapped ready

Engineering approach

Fine-tuning vs. full retraining

  • Domain-specific fine-tuning

    We recalibrate general models with your terminology, tone, and sector knowledge (legal, healthcare, finance).

    • PEFT / LoRA techniques
    • RLHF / DPO optimisation
    • Precise style alignment

    Business impact

    Up to 85% reduction in hallucination rates.

  • Full-scale retraining

    We retrain core layers on large internal datasets to build a company-specific base model.

    • Custom tokenisation
    • Custom architecture
    • Private cluster training

    Business impact

    Full integration of corporate memory into model architecture.

Model-agnostic infrastructure

Hardware- and vendor-independent. We optimise the latest open-source architectures for your organisation.

  • Logic & code

    DeepSeek-V3

    State-of-the-art performance for code and complex reasoning workflows.

  • Universal

    Llama 3.1 / 3.2

    Meta's strongest open family; ideal for general-purpose enterprise assistants.

  • Multilingual

    Qwen 2.5

    Alibaba models with strong multilingual and mathematical capabilities.

  • Enterprise

    Granite (IBM)

    Trained for enterprise compliance and transparency requirements.

  • Efficiency

    Gemma 2

    Google-architecture edge deployments with low latency and high throughput.

  • Sovereign AI

    Mistral Large

    European-hosted, high performance aligned with data sovereignty policies.

Continually learning enterprise intelligence

Static training (snapshot)

Models frozen on data from a defined period. High predictability and control for regulated industries.

Dynamic pipeline (continual)

Models updated on a schedule with new data, protected against catastrophic forgetting.

Training console v4.0

# Initializing PEFT/LoRA adapters...

# Base Model: DeepSeek-V3-Base

# Dataset: Internal_Wiki_2026 (4.2B tokens)

Loss rate0.0024
GPU clustersNvidia & AMD

Train your model on corporate DNA

Move to AI infrastructure that is fully yours without compromising data security. Speak with our specialists on process analysis and architecture design.