The principle Vela is built on.
Most organizations bolt AI onto individual developers and hope for the best. Vela does the opposite. It turns AI powered delivery from an experiment run by individuals into a standardized organizational capability.
It takes a change request or an idea and moves it through the entire lifecycle analysis, design, development, testing, deployment and operations with AI driving each stage and our engineers in control at every decision that matters.
The result is delivery that is faster, more predictable, better documented and easier to govern at a quality and consistency a single expert with a copilot can't match.
Not a single tool, a way of working backed by tooling and a growing asset library.
From intake to production and back, code generation is only one step of many.
Every step versioned and logged. People approve the high stakes decisions.
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Merged from three internal factories already used on live engagements.
How software gets delivered and how it gets bought is changing fundamentally. The edge is moving from smart people to the discipline of applying AI across delivery in a repeatable, governed way.
We map AI adoption across four levels. Vela targets Level 3: AI runs a complete, well defined process end to end, while people set direction and approve the decisions that carry risk. We deliberately stop short of full autonomy, that restraint is a design choice, not a limitation.
Vela is the constellation of sails. The name reflects how we work: AI creates momentum, while people set the course, define the boundaries and adjust along the way.
That is where the difference lies. Vela brings AI, agents, delivery context and automation into one way of working, helping Trask experts turn intent into faster, controlled software delivery from specification to operations.
A standard production line, not individuals improvising with copilots. A predictable tempo on small and large work alike, with quality enforced by automated checks at every step.
Four human approval gates (G0 G3) that are never automated away. Every artifact is versioned, every step logged, delivery you can fully audit, built for regulated environments.
Writing code is only 25 to 35 percent of delivery effort. Vela covers the whole lifecycle, analysis, design, testing, deployment, operations, where most of the value and risk actually live.
Expertise is captured as reusable specs, skills and assets, shared across teams and reused on the next engagement, instead of being trapped in one person's head.
Security and compliance built in, with in region data residency by design across the EU, US and Canada. Engineered for brownfield estates and real world integration, not just greenfield demos.
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Trask Vela analyzes its own runs and reports back, so the factory gets measurably better over time, and so does the team that operates it.
Vela starts with a standard SDLC pipeline and extends outward, adding the engineering depth and enterprise context that real delivery demands.
Analysis → requirements → overall architecture → the work broken down into stories. Recalculated only when something changes.
Design → build & test → deploy, in the same order every time. The big picture is set once; the detail is handled story by story.
People approve at the high stakes points, and those gates are never automated away. Between them, AI checks its own work in loops with a retry limit.
Software is delivered story by story to the finish. If a run fails, it resumes where it stopped, completed steps are skipped.
A handful of commands runs the line, or an engineer can step in at any point and let Claude execute the next step in full factory context.
An operator starts and steers the line. An orchestrator coordinates specialized agents, all wired into the systems delivery already runs on, with observability for both the classic stack and the AI itself.
A modular architecture designed to swap orchestrators and plug into existing enterprise systems.
We're deliberate about what we claim. Here's where Vela already moves the needle on specific work, and the target we're engineering toward.
After an upgrade, a Java service had a TLS issue that standard troubleshooting could not solve. Using debug logs, environment details and earlier tests, AI identified the root-cause RNG issue and the exact fix much faster than originally estimated.
Three weeks before release, a blue-green deployment requirement was added, requiring synchronization across 250 database tables. Working iteratively with AI, the team prepared and validated the scripts in time and kept the original release plan on track.
A seven-year-old system needed a structured analysis and an extension plan covering functional and non-functional requirements, security, integration and scalability. The resulting document was then used to pre-generate code and tests.
A single high-profile AI failure can sink an entire enterprise AI program. So we engineer the creation process deliberately — and treat restraint as a feature.
Partnerships: built on our work with Microsoft and Anthropic, within Trask's Responsible-AI framework.
Bounded over open-ended. AI agents run on defined, scoped tasks inside a strict pipeline, not open ended autonomy that runs up cost without proportional value.
Gates and diligent review. Stronger QA gates and human review at every team-to-team handoff, so volume never overwhelms quality.
Gradual adoption. Roles are augmented and then evolved step by step, never replaced overnight, and never at the expense of control.
Compliant by design. In-region data residency across the EU, US and Canada, with full auditability and security woven into the pipeline from the first step.
...and many more
Because delivery becomes measured and predictable, Vela opens the door to a different conversation: buying finished, defined units of software against a clear Definition of Done, not hours on a timesheet. Trask carries the productivity risk; clients gain predictability, quality and transparent metrics. Want to see what this could look like in your project?