Independent AI systems practice

Custom AI systems, commissioned for the real world.

We advise, architect, and build model-integrated products for organizations with real technical constraints—from PyTorch and fine-tuning to open-weight inference and the GPU infrastructure underneath it.

Commission a system Examine the stack ↓
Practice
Model systems
Material
Code + compute
Scope
Weights → production

Image commission / 001

The Model Assembly

Handcrafted architectural model illustrating model, adaptation, runtime, and infrastructure layers

Section drawing

A system is only as resolved as its layers.

We work through the full assembly—making the boundary between model, runtime, and infrastructure explicit enough to operate.

  1. MODEL

    Weights with a known purpose.

    Commercial and open-weight selection · architecture fit · model interfaces

  2. ADAPTATION

    Behavior shaped to the work.

    Fine-tuning · LoRA and adapters · dataset design · evaluation

  3. RUNTIME

    Inference made dependable.

    PyTorch and CUDA · resident model services · stable APIs · observability

  4. INFRASTRUCTURE

    Compute under deliberate control.

    GPU provisioning · scheduling · liveness · recovery · cost visibility

Disciplines

Engineering beyond the demo.

Model integration

Purpose-built product systems around commercial and open-weight models, with clear interfaces and operational boundaries.

Adaptation & fine-tuning

Dataset, training, checkpoint, and parameter-efficient adaptation workflows—including LoRA—designed for repeatable decisions.

Inference engineering

PyTorch and CUDA pipelines, resident model services, stable APIs, liveness, and production observability.

GPU systems

Control planes for provisioning, scheduling, scaling, recovery, and cost visibility across GPU workloads.

Evaluation systems

Deterministic sweeps, artifact provenance, comparable outputs, and runtime metrics for defensible experiments.

Technical advisory

Model and provider selection, build-v-buy, inference economics, system architecture, and implementation roadmaps.

Provenance

Every output should have a legible history.

Production systems need more than an endpoint. Inputs, load decisions, runs, and outputs should remain inspectable as the system changes.

  1. WEIGHT FILE sha256:8f31…a62c
  2. LOAD PLAN plan:4c9e…11b7
  3. RUN run:72ad…09ef
  4. ARTIFACT asset:d104…c83a

Illustrative identifiers / abbreviated for display

Engagement

Three ways to enter the work.

Advise

Clarify model choice, economics, constraints, and the technical path forward.

Architect

Resolve the system boundary, interfaces, infrastructure, and implementation plan.

Build

Engineer the working system alongside your team, through production.

New commissions

Bring us the problem that resists the obvious answer.

For model systems, GPU infrastructure, and technical advisory where the details matter.

hello@aiosoftware.io