AI Engineering Capability
The operating model for enterprise software delivery
IDLC bridges business strategy and AI engineering: pairing real-world software execution with an operating model you own, installed directly around your delivery.
How we run it
We install IDLC directly on your delivery streams, not as a separate initiative running next to them. You get project outcomes right away, plus an operating model you own — one that keeps your engineers focused on architecture, intent, and verification instead of babysitting AI output.
"When code generation becomes trivial, the real bottleneck in software delivery becomes human clarity and validation"
- Vlad Medvedovsky, Founder & CEO Proxet
WHAT IS Intent Driven Lifecycle
The operating system for AI-assisted delivery
IDLC turns business intent into structured context, controlled execution, and verifiable outcomes.
Development stays human-led: engineers own the decisions and the reasoning; AI accelerates execution under human direction.
01.
Intent
Business goals, captured as clear specifications.
02.
Context
Knowledge structured so AI stays grounded, not guessing.
03.
Execution
AI generates content under explicit rules and controls.
04.
Verification
Outcomes proven by tests and metrics.
WHY IT MATTERS
Solving for the AI enablement gap
Most teams treat AI as an isolated copilot. We install it across your entire delivery lifecycle. We call it a shared second brain: a persistent context system connecting business stakeholders, product managers, QA, and engineers — so every AI-assisted session builds on the same knowledge instead of guessing at it.
Cross-Functional Context Alignment
Creates a shared knowledge foundation across business, product, data, QA, and engineering — preventing AI tools from inventing inconsistent architectures or ungoverned pipelines.
Upstream Work Acceleration
Reorients team workflows upstream toward problem definition, domain constraints, and outcome design before execution begins.
Automated Outcome Verification
Replaces manual code reviews with automated quality gates and verification metrics that clear downstream delivery bottlenecks.
Re-Skilled Delivery Teams
Replaces rigid team structures with versatile, high-performance delivery units— spanning software and data engineers alike — that orchestrate AI execution under strict human governance.
THE FRAMEWORK
What makes IDLC operational
Knowledge
What the project knows
Requirements, architecture, ADRs, domain language, plans, and decisions are kept in durable, version-controlled artifacts.
Delivery Workflows
How work progresses
Defined paths for features, bugs, architecture changes, testing, and validation.
Operational Instructions
How AI operates
Persistent project instructions tell agents what context to load, which workflow to follow, and what must be validated.
One source of truth. Three parts, all of which you own. Knowledge gives it context. Delivery workflows give it structure. Operational instructions make it apply automatically, in every AI session.
DURABLE KNOWLEDGE SYSTEM
AI is only as good as the context you give it
IDLC captures your institutional knowledge in three layers — so every agent and every engineerworks from the same source of truth.
WHY
Business intent
Business intent — Product requirements (PRDs) capture what the business is trying to achieve and why.
HOW
Architecture & decisions
Design docs and Architecture Decision Records (ADRs) preserve the reasoning history, not just the result.
WHAT
Features & work
Features, tickets, and tasks that translate intent into executable, trackable units.
Persist the knowledge. Load only the relevant context needed for the task at hand.
Path to adoption
Start small. Prove the economics. Scale only what survives quality and governance. Each stage has a measurable exit criterion and produces a client-owned asset.
Executive Workshop (Align)
2 hours
Diagnostic & Blueprint (Assess)
2–3 weeks
Lighthouse Transformation (Prove)
6–8 weeks
Operating Model (Operationalize)
8–12 weeks
Enterprise Enablement (Scale)
Ongoing
Proxet's conservative lighthouse targets
01.
Speed
3–4× faster development; 50–70% shorter time to ship.
02.
Quality
15–30% higher automated test coverage; 20–35% less rework.
03.
Economics
50–70% lower delivery cost (measured as cost per accepted outcome).
let's begin!
Bring your engineering and delivery leaders. Leave with a common operating model and a practical path to start.
Recommended Entry Point: Executive Workshop
Book our IDLC Workshop