Product
Automate the entire software delivery lifecycle across six functional personas — from requirements and stories to code, tests, database artifacts, process models, and release governance.
The problem
Teams adopt copilots and coding agents quickly. What stays tribal is the work that actually moves delivery: requirements, stories, implementation plans, code, tests, schemas, process models, and audits. Without a shared system, every persona reinvents AI usage — and quality stays uneven.
What it is
A workspace-installed AI operating layer: standards, skills, and well-tested workflow prompts that automate real SDLC work inside the repositories and IDEs your teams already use.
Not a chatbot. Not a generic prompt dump. Not an unmanaged zip of tribal knowledge. It is a governed delivery system that happens to include a battle-tested prompt and skill library as the execution layer.
Built and proven in the field — used to enable structured AI-assisted delivery across 50+ practitioners and 6 functional domains.
Lifecycle automation
Not a prompt library sitting on a shelf. An operating system for AI-assisted delivery that runs proven, role-specific workflows from requirements through code, tests, process models, and release governance.
Persona 01
Turn briefs, Figma, and legacy systems into structured requirements and stories.
What it automates
Persona 02
Plan and build UI features from stories and Figma — with tests attached.
What it automates
Persona 03
Plan and implement services from stories — including unit test generation.
What it automates
Persona 04
Automate schema, procedures, dictionaries, and change impact work.
What it automates
Persona 05
Generate tests, automation, and triage workflows from design and failures.
What it automates
Persona 06
Extract and generate process workflows from BRDs and stories.
What it automates
Across the lifecycle
Full-stack workflows connect personas so a single intake of story and design context can drive planning, implementation, tests, and governance — instead of disconnected chat sessions.
Plan and build end-to-end features spanning frontend, backend, and data from a single story-and-design input set.
Every workflow resolves common, domain, capability, and client-overlay standards so outputs stay consistent and enterprise-fit.
Run full-codebase and uncommitted-change audits, evaluate output quality, and report prompt adoption across the team.
Stand up an AI-ready engineering workspace quickly — prompts, skills, standards, templates, outputs, and reports in a repeatable structure.
How it works
01
Put the platform into the team workspace so AI work runs inside existing repos and IDEs — not a separate silo.
02
Resolve engineering standards by technology and client overlay so generation follows your conventions.
03
BA, engineering, database, QA, and process personas execute proven workflows that produce real delivery artifacts.
04
Audit quality, measure adoption, and keep AI-assisted delivery under control as the team scales.
Why teams adopt it
The platform includes a curated library of well-tested workflow prompts and skills — the execution layer that makes lifecycle automation real. Those workflows have already helped teams standardize AI-assisted delivery across business analysis, engineering, database, QA, and process modeling.
Next step
Discuss the AI SDLC Automation Platform — lifecycle automation across BA, engineering, database, QA, and process teams.
