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AI isn't just for writing code. It reshapes the entire SDLC.

DevObsessed integrates LLMs across every phase of the software development lifecycle — from requirements gathering through QA — using structured workflows, not ad-hoc prompting.

REQGROOMDEVQADOCPLAN~20%EFFICIENCY GAIN

Every phase. Structured workflows. Measurable gains.

Requirements & Elaboration

30% faster requirements gathering

AI-assisted mob elaboration transforms how teams decompose epics into stories. LLMs draft acceptance criteria, identify edge cases, and surface missing requirements in real-time — cutting elaboration time by 30% across a 70-team enterprise engagement.

Story Grooming

40% reduction in grooming time

LLM-powered decomposition breaks complex stories into implementation tasks, suggests technical approaches, and pre-populates subtasks. Engineers spend grooming sessions refining AI-generated plans instead of starting from scratch.

Development

18% faster with Code Captain workflows

Code Captain and agentic workflows turn AI from autocomplete into a development partner. Engineers use structured prompting patterns and LLM Kanban boards to maintain context across sessions — compounding efficiency gains over time.

QA & Documentation

50%+ faster bug analysis

AI generates test cases from acceptance criteria, automates regression analysis, and produces documentation from code changes. One team went from 40% to 80% test coverage in a single day using AI-generated test suites.

How AI integrates across your SDLC.

01

Map your current workflow

We audit each SDLC phase — requirements, grooming, development, QA, documentation — and identify where AI creates the highest leverage with the lowest friction.

02

Deploy structured AI workflows

Each phase gets purpose-built AI workflows: mob elaboration for requirements, LLM decomposition for grooming, Code Captain for development, and automated test generation for QA.

03

Measure phase-by-phase gains

We track efficiency improvements per phase — not just overall velocity. This surfaces where AI is working and where human processes need to adapt.

04

Compound gains across sprints

AI workflows improve as teams build institutional knowledge. Documentation compounds, test suites grow, and elaboration patterns become reusable — delivering ~20% overall SDLC efficiency.

Find out where AI moves the needle in your SDLC.

60 minutes. A senior engineer audits your development lifecycle and identifies the highest-leverage AI integration points for your team.

Book a Strategy Session

Free. You'll leave with a phase-by-phase efficiency roadmap.