Classify → gate → audit
Evaluate agent events against policy, then allow, redact, block, or escalate before controlled content leaves an approved boundary.
Private AI agents for serious engineering work.
Agent platforms belong inside the engineering boundary.
ControlStackAI gives engineering teams a private runtime for long-running agents that operate MATLAB, Simulink, requirements, source control, and verification workflows. Not another chat window. Reviewable engineering output from agents that understand your toolchain and constraints.
Built for
Controls and dynamics: model analysis, linearization, trim, simulation review, test evidence.
Regulated teams: private deployments, audit trails, human checkpoints, artifact history.
Engineering context: agents grounded in repos, requirements, models, docs, tickets, and team conventions.
Product: Redline
Export-control guardrails for AI agents working around controlled technical data.
Redline is an export-control (ITAR/EAR) guardrail and tamper-evident audit layer for AI agents. It gives aerospace and defense teams a control point and evidence trail for agents that may read, summarize, or route sensitive engineering context.
As US policy gates frontier-model releases, value moves off the model layer and into private deployment, compliance, and model-agnostic orchestration. Teams are beginning to point agents at controlled USML technical data without a clear enforcement point.
Request early access
Model-agnostic · runs on your infra
Tamper-evident record
Append decision metadata to a hash-chained audit trail so reviewers can inspect what happened without storing raw controlled content.
Compliance support
Redline supports an export-control compliance program. It is not legal advice, an ITAR determination, or a replacement for your empowered official.
Platform layer
A private control plane for agentic engineering.
Coordinate long-running agent work on your infrastructure, with durable state, scoped tool permissions, checkpoints, traces, and review gates that match how engineering organizations actually operate.
Private runtime
Agents run where your IP is allowed to live
Deploy agent sessions inside approved infrastructure instead of pushing proprietary context through unmanaged public tools.
Context layer
Ground work in the artifacts engineers trust
Connect repos, requirements, models, design notes, test evidence, issue history, and local operating procedures.
Inference control
Route work across approved model backends
Prepare for mixed commercial, private, and local inference while preserving policy, cost visibility, and traceability.
Engineering workflows
Designed for models, evidence, and toolchains.
The first focus is engineering work where correctness, provenance, and reviewability matter: controls, avionics, robotics, industrial automation, flight dynamics, and verification-heavy teams.
MATLAB and Simulink
Inspect models, run scripts, summarize results, and generate reviewable technical artifacts.
Controls workflows
Support trim, linearization, controller iteration, simulation evidence, and technical writeups.
Requirements and V&V
Link agent output back to source requirements, test plans, tickets, and acceptance criteria.
Team memory
Preserve decisions, constraints, interfaces, and procedures so agents improve with context.
Engineering tools were built for human operators. Agentic AI needs a platform layer that can operate those tools, preserve reviewability, and respect the infrastructure boundaries serious teams live inside.
Read the manifestoOperating model
Agents that leave an audit trail, not a mystery.
01
Plan against real context
Agents read the repo, model structure, requirements, constraints, and prior decisions before touching work.
02
Execute through approved tools
Runtime permissions keep each agent inside allowed commands, files, systems, and approval gates.
03
Return reviewable artifacts
Engineers get diffs, reports, test output, traces, and provenance instead of opaque chat transcripts.
Early access
Design partners before public launch.
We are opening a small number of design-partner conversations with engineering teams that need agentic work to stay inside their boundary.