Stell AI Principles
Our approach to AI is grounded in our founding mission — to build tools that amplify systems engineer’s expertise and keep their work fast-moving and exciting. We're building for the people who design spacecraft, defense systems, and critical infrastructure. Their work requires perfection and collaboration in every detail.
We believe AI in aerospace and defense must be both capable and uncompromising. Capable in augmenting engineers' intelligence to work faster through tedious tasks while maintaining their domain expertise. And uncompromising in meeting the security, accuracy, and compliance standards that our customers' missions demand.
We approach this work by treating AI as a work partner, not magic. Our engineers invest in product features that lead to real productivity gains instead of chasing AI hype. We build systems that make AI outputs verifiable, traceable, and improvable.
Our approach to AI is built on the following three principles:
1. Security without compromise
Every AI feature must meet the same security standards as the rest of Stell. We operate in FedRAMP High, IL5, and Top Secret environments because our customers trust us with controlled information. That trust is non-negotiable.
Deploying AI only within authorization boundaries to maintain data isolation and meet government security requirements.
Treating AI-generated content with the same access controls, audit trails, and data handling procedures as human-generated content. Any write action taken by an agent is recorded just as human actions are on Stell - we date/time stamp, diff, and record who performed a change to a requirement.
2. Mission assurance through verification
AI must amplify engineers' judgment, not replace it. We design for appropriate human oversight at every risk level — from lightweight verification to explicit approval.
Engineering human-in-the-loop interfaces that are efficient, not burdensome — clear visual language for AI-derived content, streamlined approval flows, and progress tracking that shows what's been verified.
Investing in tools and context that improve AI accuracy for our domain — understanding how our customers organize projects, define requirements, and structure their work.
Measuring success by whether AI genuinely saves engineers time after accounting for verification effort, not by raw feature counts.
3. Engineering AI as a Platform
We route all AI capability through a single, well-engineered interface that gets better as models improve — allowing us to adopt new capabilities rapidly without rebuilding features.
Focusing engineering investment on search, context engineering, and better tools rather than proliferating AI features across the product.
Making Stell Agent the primary interface for interacting with requirements data, creating a positive feedback loop where easier input motivates more complete information.
Treating model selection as a commodity — we get smarter models for free as labs improve them — while owning the hard problems of domain context, verification interfaces, and tool design.
These principles mean we sometimes say no to AI features that would work elsewhere. We're building for an industry where "move fast and break things" has never been the motto. We move deliberately and build things that work.
