New Service For Embedded Engineering

Embedded AI development,
behind your firewall.

From AI infrastructure to team-wide adoption.
Bring AI to your embedded teams — without sending a line of code outside the company.

Get the brief Talk to us
Foundation

What it takes to run AI
inside an embedded team

Cloud AI is tempting. But your source code, designs, and customer specs can't leave the building.
You need an AI environment built around three pillars — security, model quality, and team adoption.

Security

Protect what is yours

Source code, design documents, specifications, customer data — none of it touches an external AI service.

Quality

Models you can ship with

Hundreds of models exist. Few survive production. We evaluate them so you can pick the right one for each task.

Productivity

AI for the whole team

Not just power users. Policies, training, and operations make AI part of how the team works.

Solution

Everything you need,
end to end

Infrastructure, coding tools, knowledge integration, training — we design, build, and run the whole stack so your team can focus on shipping.

What we deliver

  • Secure AI infrastructure

    On-prem, closed network, or cloud — designed and built to your security requirements.

  • AI coding environments

    AI chat, coding agents, and IDE integrations developers use every day.

  • Model selection and operations

    Choose and evaluate models against your use case, performance, cost, and security constraints.

  • Internal knowledge integration

    AI that reads your specs, design documents, internal rules, and past materials.

  • Training and adoption

    Developer workshops, usage policies, practical guides, and visibility into how AI is used.

Secure AI development environment — full architecture
The full secure AI development environment
Get Started

We'll design a configuration
that fits your environment and security requirements.

Get the brief Talk to us
Use Case

Where AI fits
in the embedded workflow

Combine AI chat, coding agents, and internal knowledge — and AI shows up across investigation, implementation, review, testing, and maintenance.

Understand legacy code

Map control logic, drivers, and middleware structures fast.

Reference specs and design docs

Surface related specs and past design decisions from your internal documents.

Generate and refactor code

Write and revise C/C++, Python, test code, and build scripts.

Review with rules in mind

Reviews shaped by your coding conventions, design rules, and past bug patterns.

Author tests and debug

Unit tests, edge cases, log analysis, root-cause investigation.

Improve performance

Bottleneck analysis and improvement proposals for models and code.

Security

Multiple deployment modes,
one secure stack

We combine open-model execution environments with closed-model access — sized to your data sensitivity and internal policies.

Open models — execution

On-premises

On-premises

We build dedicated hardware inside your facility. Code never leaves the premises.

Hosted private cloud

Hosted private cloud

We build and operate dedicated hardware in a partner data center. Less ops on your side.

Cloud (VPC)

Cloud (VPC)

We build a dedicated VPC environment on a cloud vendor's infrastructure. Lower upfront investment. Start in weeks, not months.

Closed models — access

Cloud AI services like Amazon Bedrock

Cloud AI services like Amazon Bedrock

Run the latest closed models — Claude Opus, Claude Sonnet, and others — in your own virtual private cloud. Your data is never used to train someone else's model.

Get Started

Tell us your environment and operational policy,
and we'll propose the right configuration.

Get the brief Talk to us
Quality / Models

Production-ready models,
matched to the task

We raise AI development quality across three dimensions — model selection, output code performance, and organizational knowledge. Starting with model selection: the right model depends on the task, security envelope, and quality bar. We continuously evaluate models and deliver a catalog matched to your work.

The right model for every task

Pick the right model from your security-cleared catalog based on task characteristics.

  • —Simple, repetitive work → open / lightweight models
  • —Complex, novel decisions → high-performance models
  • —Iterative trial-and-error → fast, lightweight models for efficient retries

Open models are a real option now

Open models have advanced enough to be a practical choice — even on-prem or in closed networks. For work where code can't leave the company, they often perform well enough.

Open vs. closed LLM benchmark performance
(As of April 2026, edited by Fixstars based on Artificial Analysis API Documentation)
Bubble chart comparing open and closed model performance

A catalog we keep current

Skip the evaluation grind. We verify each model and document use cases, speed, and licensing for you.

Verified open models

  • Qwen (Alibaba)
  • gpt-oss (OpenAI)
  • Gemma (Google)
  • Nemotron (NVIDIA)

* As of April 2026. New models are added regularly.

AIStation Supported LLM Dashboard — verified AI model evaluation results
Get Started

Tell us your tasks and security needs.
We'll pick the model setup that works.

Get the brief Talk to us
Quality / Code Optimization

High-performance output code with APEX

APEX (Agentic Performance Engineering eXperience) is our framework that captures 20 years of Fixstars performance-engineering know-how in a form AI agents can leverage autonomously. With this optimization know-how built into the framework, the code AI produces achieves performance on par with veteran engineers.

Capabilities

apex tune

General performance optimization — automatic rewriting for CPU-GPU sync reduction, memory layout, torch.compile, and more.

apex convert

TensorRT acceleration for PyTorch inference — automatic, functionally-equivalent rewrites that avoid Graph Breaks.

apex tune-discover

Discover new optimization patterns — the LLM autonomously finds and accumulates novel optimizations not in the existing playbook.

* More capabilities are added on an ongoing basis.

Performance gains from autonomous optimization

resnet50 Training

bf16 mixed precision, NHWC memory format fix, torch.compile applied

x2.11

SparseDrive Training

CPU-GPU sync reduction, CPU affinity tuning

x2.04

DETR Inference

TensorRT compatibility, dynamic shape removal, TensorRT backend

x2.12

OpticalFlow

OpenCL UMat enabled, persistent UMat

x7.67

* Measured on Fixstars internal benchmarks (reference values).

Quality / Knowledge

Bring your team's knowledge
into the AI

We apply our knowledge-loop methodology to your organization, raising development quality across the team. Throughout the engagement, we cultivate the mechanisms for AI to reference your veterans' know-how and internal standards.

Know-how and rules AI will reference

01

Coding conventions & design policies

Naming rules, design judgments, quality criteria — the team-wide ground rules.

02

Operating procedures

How routine work — reviews, code analysis, documentation — gets done.

03

Project-specific rules

Role assignments, decision criteria, the reasoning behind past decisions.

* At launch, we support the setup. Through the project, your knowledge base grows.

How AI uses knowledge

  1. 1

    Prepare

    Organize internal know-how and rules into reusable knowledge

  2. 2

    Configure

    Set AI roles and rules to match the project

  3. 3

    Reference & decide

    AI references knowledge to make decisions and suggestions

  4. 4

    Generate

    Code or suggestions in a form ready for real work

  5. 5

    Improve & grow

    Update results and grow the knowledge base

Productivity

Built for the long run,
not just rollout

An AI environment isn't done at launch. Hands-on developer workshops, regular model updates, and visibility into usage and cost — we keep AI part of how your team works, day in and day out.

Hands-on workshops, tailored to your team

Practical training that turns AI-driven development into a team habit.

Sample workshop topics

  • —Prompt engineering
  • —Designing multi-agent AI workflows
  • —Inputting embedded-specific requirements
  • —Token cost optimization
  • —Securing AI usage
  • —Human-in-the-loop
Hands-on workshops, tailored to your team

Models that stay current — even on-prem

Run the latest models flexibly and continuously, even in local environments.

  • —One-click upgrades to the latest AI models
  • —Easy switching between models with different characteristics
  • —Latest LLMs configured for your environment
  • —Verified model catalog, always updated
Models that stay current — even on-prem

See usage. Optimize automatically.

Visualize per-model usage, cost, latency, and throughput — and tune the environment as it runs.

  • —Per-user usage and cost, per model
  • —Automatic inference engine tuning for concurrent sessions
  • —Latency and throughput visibility (coming soon)
See usage. Optimize automatically.
Implementation

How we get you live

From build-out to adoption — tailored to your situation.

* Timelines vary by project scale and requirements

  1. 1

    Discovery

    1–2 weeks

    We map your security requirements, development phase, and existing environment.

  2. 2

    Environment design

    2–4 weeks

    Design the infrastructure, models, tooling, and knowledge structure.

  3. 3

    Build-out

    1–2 months

    Stand up infrastructure, models, tools, and management features.

  4. 4

    Pilot

    1–2 months

    Run with a small team. Surface issues. Refine operations.

  5. 5

    Rollout & adoption

    Ongoing

    Roll out org-wide. Workshops instill use patterns. Operations get optimized.

Get Started

Tell us your current setup and goals.
We'll propose how to get there.

Get the brief Talk to us
Get Started

Start with the brief

From secure infrastructure to team adoption of AI-driven development.
One-stop support for embedded AI.

Download

The service brief

One PDF: what we deliver, sample configurations, the model list, and operational rules.

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Contact

Talk to an engineer

For configurations, quotes, and technical questions.

Get in touch