Security
Protect what is yours
Source code, design documents, specifications, customer data — none of it touches an external AI service.
Foundation
What it takes to run AI
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, performance, and team adoption.
Quality
Performance you can ship
Running in development isn't enough for production. We optimize AI-generated code to the performance your embedded devices demand.
Productivity
AI for the whole team
Not just power users. Policies, training, and operations make AI part of how the team works.
Solution
Secure AI development
Secure AI development
by Fixstars Vega
So your embedded team can use AI without code or design data leaving the company, we deliver the whole stack — secure AI infrastructure, coding environments, internal knowledge integration, model selection, and training and adoption.
What we deliver
- Infrastructure design & deployment
- AI platform design & deployment
- AI model selection, evaluation & operations planning
- AI coding environment setup
- Internal knowledge integration design
- Training & adoption support
Get Started
We'll design a configuration
that fits your environment and security requirements.
Use Case
Where AI fits
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,
Multiple deployment modes,
one secure stack
We combine open-weight LLM execution environments with closed-model access — sized to your data sensitivity and internal policies.
Open-weight LLMs — execution
On-premises
We build dedicated hardware inside your facility. Code never leaves the premises.
Hosted private cloud
We build and operate dedicated hardware in a partner data center. Less ops on your side.
Cloud-based 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
Run the latest closed models — Claude, GPT-5.x, Nova, and others — in your own virtual private cloud. Your data is never used to train someone else's model.
Access Control
Model selection based on code sensitivity
The Model Orchestrator securely routes requests to the appropriate model based on your security policy.
Available AI models can be controlled based on code sensitivity. For example, customer code that cannot be sent to the cloud can be restricted to Internal Local AI only.
Available AI environments, managed by security level
-
Level 0
External Cloud AI
Training may be used
-
Level 1
External Cloud AI
Not used for training
-
Level 2
Internally Managed Cloud AI
(Closed AI via Amazon Bedrock)
-
Level 3
Internal Local AI
Get Started
Tell us your environment and operational policy,
and we'll propose the right configuration.
Quality / Models
Production-ready 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.
Open-weight models are a real option now
Open-weight 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/closed-weight LLM benchmark performance
Response times, engineered
Running an open-weight model as-is may not deliver production-ready performance. Fixstars Vega ships models with performance engineering applied by default.
Time to first token (TTFT P90)
Before optimization
long queues
After optimization
virtually no queueing
~3.8 sec
(low response times even at 90 concurrent users)
Measured by Fixstars on GLM-5.1 (results on GLM-5.2 forthcoming), 8x NVIDIA H200 GPUs. See the service brief for detailed results.
Validated models we provide
You don't have to evaluate models from scratch. Our engineers validate each one on real embedded development projects and hand it over with use cases, performance, and licensing already mapped out.
Models we provide
GLM-5.2
- A top-performing open-weight model for coding tasks
- Long-context support, well suited to large codebases
- Best for text-based work such as code generation and analysis
Qwen3.6-27B
- A relatively lightweight 27B model with practical coding performance
- Multimodal input, including images and video
- Useful for document analysis and work involving screens and diagrams
As of July 2026. Available models are updated continuously. GLM-5.2 availability may depend on your hardware configuration.
The right model for each task type
Different tasks suit different models — spec writing from text alone, E2E testing that needs image input, and so on. You can configure the assignment per agent.
Example model assignment per task
Quality / Code
Optimization
High-performance output code,
High-performance output code,
powered by a performance-engineering harness
Fixstars Vega's performance-engineering harness packages more than 20 years of know-how from embedded development projects into a form AI agents can apply. The code it produces matches — and in some projects exceeds — the optimization work of veteran engineers.
Capabilities
tune
General performance optimization — automatic rewriting for CPU-GPU sync reduction, memory layout, torch.compile, and more.
convert
TensorRT acceleration for PyTorch inference — automatic, functionally equivalent rewrites that avoid graph breaks.
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.
Speedups achieved with Fixstars Vega
Image segmentation foundation model (inference)
Large-batch processing, prompt pre-caching, TensorRT conversion, etc.
4.7x
SparseDrive (inference)
TensorRT conversion, PTQ, custom CUDA kernels as plugins, etc.
3.95x
ResNet-50 (training)*
bf16 mixed precision, NHWC memory-format fix, torch.compile, etc.
2.11x
* Measured on Fixstars internal benchmarks. For reference only.
Case Studies
See the full methods and results
behind each speedup in our service brief.
Quality / Knowledge
Bring your team's 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 build the mechanisms for AI to reference your veterans' know-how and internal standards.
Know-how and rules AI will reference
Coding conventions & design policies
Naming rules, design judgments, quality criteria — the team-wide ground rules.
Operating procedures
How routine work — reviews, code analysis, documentation — gets done.
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
Prepare
Organize internal know-how and rules into reusable knowledge
-
2
Configure
Set AI roles and rules to match the project
-
3
Reference & decide
AI references knowledge to make decisions and suggestions
-
4
Generate
Code or suggestions in a form ready for real work
-
5
Improve & grow
Update results and grow the knowledge base
Productivity
Built for the long run,
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 situation
Practical training that helps AI-driven development take root across the whole team.
Sample program
- — Fundamentals of AI-driven development
- — Using the secure AI development environment
- — Working with AI coding agents
- — Team rules and knowledge use
- — Operational design that ensures quality
Content is customized to your situation.
Visualize usage to drive team-wide adoption
- — Team-wide AI usage, visualized across a range of metrics
- — User-experience metrics such as average response time and error rate
- — Per-model request counts, by share and over time
- — Usage cost, with per-model and per-user breakdowns
Keep models current — even in local environments
When a better-suited model or new version appears, we validate it and update the models we provide. From the admin console, you can check model status, and stop or swap models.
Implementation
How we get you live
From build-out to adoption — tailored to your situation.
* Timelines vary by project scale and requirements
-
1
Discovery
1–2 weeksWe map your security requirements, development phase, and existing environment.
-
2
Environment design
2–4 weeksDesign the infrastructure, models, tooling, and knowledge structure.
-
3
Build-out
2–4 weeksStand up infrastructure, models, tools, and management features.
-
4
Pilot
1–2 monthsRun with a small team. Surface issues. Refine operations.
-
5
Rollout & adoption
OngoingRoll out org-wide. Workshops establish usage patterns. Operations get optimized.
Get Started
Tell us your current setup and goals.
We'll propose how to get there.
Get Started
Start with the brief
From secure infrastructure to team adoption of AI-driven development.
One-stop support for AI in embedded development.
The service brief
One PDF: what we deliver, sample configurations, the model list, and operational rules.
Get the briefTalk to an engineer
For configurations, quotes, and technical questions.
Get in touch