Ideas into requirements
Structure tasks, acceptance criteria and architecture decisions. Connect business context to an actionable implementation plan.
AI SDLCNEW PRODUCT
Your complete development lifecycle
powered by AI
Connect requirements, architecture, code, testing and operations through a coordinated AI workflow. Automate repetitive work, keep decisions traceable and give your engineering team control over the reviews that matter.
For CTOs, engineering leaders and platform teams
LPS / DEVELOPER LIFECYCLE
CONNECTED DELIVERY CONTEXT
From idea to operation
workflow enterprise.release {
context: «business_requirements»
agents: [«build», «test», «review»]
approval: required
traceability: enabled
}
Plan
→Code
→Test
→Ship
Every change traces to a requirement
INTERFACE CONCEPT · SAMPLE DATA
Full developer lifecycle AI automation
Less manual work.
More room for your team.
Code with context
Prepare changes that reflect your repository and team conventions. Explain decisions and assemble a reviewable pull request.
Quality before release
Prepare tests, analyze results and assist reviewers. Your existing quality checks remain mandatory parts of the process.
Delivery and operations
Prepare release notes and deployment plans. Connect production incidents to tasks, tests and the next round of improvements.
FULL LIFECYCLE. SHARED CONTEXT.
AI at every stage.
Your team stays in control.
STAGE 01 / 06
Turn business goals into clear work.
AI helps structure requirements, uncover contradictions and define acceptance criteria. Your team confirms the context before development begins.
Output: an agreed specification
STAGE 02 / 06
Decisions grounded in your context.
Compare approaches, constraints and dependencies. Agents help prepare architecture decisions and documentation for your engineers to evaluate.
Output: architecture and a delivery plan
STAGE 03 / 06
Code with the bigger picture in mind.
Agents help implement changes in the context of the repository and agreed architecture. Every change stays connected to the original requirement.
Output: changes ready for code review
STAGE 04 / 06
Make quality part of every step.
Generate scenarios, test data and result analysis from requirements. Your team reviews coverage and validates critical paths.
Output: verifiable quality criteria
STAGE 05 / 06
A controlled path to production.
AI helps review changes, prepare release notes and outline rollback plans. The accountable team decides when to release.
Output: a release ready for approval
STAGE 06 / 06
Close the loop with real feedback.
Operational incidents and observations flow back into the backlog. L2/L3 agents help collect context and prepare the next improvement.
Output: evidence-informed development tasks
GOVERNED AUTOMATION
AI that works
within your rules.
Agree data sources, permissions, autonomy boundaries and quality criteria before implementation. Start with a focused use case and expand based on pilot results.
- Access controlOnly the permissions and sources needed
- Human approvalYour team defines autonomy boundaries
- Traceable decisionsContext, evidence and an activity trail
THE DETAILS MATTER
Good questions.
Let’s talk through your infrastructure and use cases.
Does this replace our engineering team?
The solution handles selected repetitive tasks and helps engineers work with context. Your team retains responsibility for architecture, change approval and releases.
Do we need to change our development tools?
We start with your repositories, issue tracker and build process. Discovery establishes compatibility, connections and access policies; the integration scope depends on the chosen workflow.
How do we evaluate a pilot?
Before starting, we select a bounded category of tasks and establish a baseline. Evaluation covers lead time, rework, the quality of checks and the proportion of changes accepted by your team.
WHAT’S NEXT
Your next step
toward automation.
Tell us what you’re working on. We’ll explore the approach and define the first step together.