Seed Forty Two
INDEPENDENT ENGINEERING STUDIOAI / EMBEDDED / SOFTWARE
Seed Forty Two

Intelligence.
Engineered.

From models to machines.
We build the systems that make AI work.

Explore our capabilities
SCROLL TO EXPLORE
BUILT ACROSS BOUNDARIES

The model is only
part of the system.

Intelligence matters when it works in the real world. We connect AI, embedded systems, and software to build complete solutions.

Meet the studio

01 / CAPABILITIES

THREE DISCIPLINES. ONE SYSTEM.
01
FROM DATA TO DECISIONS

Applied AI

Models built around the task. Training, evaluation, and inference that fit the data, the device, and the way people work.

  • Model fine-tuning
  • Document intelligence
  • On-device inference
02
WHERE SOFTWARE MEETS MATTER

Embedded systems

C and C++ close to the hardware. Microcontroller firmware, barcode scanners, and real-time audio built for low latency and tight memory budgets.

  • C / C++ firmware
  • Barcode scanning
  • Hardware integration
03
CONNECT THE WHOLE SYSTEM

Software & automation

The software between an idea and a working operation. Tools, services, and workflows that turn complex tasks into clear actions.

  • Business workflows
  • Cloud services
  • Internal tools

02 / THE STUDIO

INDEPENDENT BY DESIGN

Deep in the details.
Clear on the whole.

Seed Forty Two is an independent engineering studio working where AI, hardware, and software meet.

From model training and LLM inference to devices and business automation, we work through the details that make the whole system useful.

C / C++ FIRST

Especially on microcontrollers. Direct hardware access, careful memory control, and performance you can measure.

Much of this work stays private.
The thinking behind it is what we bring to every collaboration.

03 / THE APPROACH

CURIOSITY, WITH A BASELINE.

A fixed starting point.
A result you can check.

Our name starts with random.seed(42). We use seed 42 in our own model training, too. From ML experiments to LLM sampling, a shared starting point helps us compare, test, and improve.

ML TRAINING LIBTORCH / C++
torch::manual_seed(42);
LLM SAMPLING LLAMA.CPP / C / C++
llama_sampler_init_dist(42);
01

Understand the real problem.

Start with the task, the constraints, and a clear baseline.

02

Build the right system.

Choose the model, hardware, and software that fit the work.

03

Let the evidence lead.

Test on real inputs. Measure the result. Make each change count.

THE NEXT GOOD QUESTION

What are
you building?

A hard problem. A new idea. A system that could work better.
Let’s find a useful starting point.