AI-Native Engineering Platform

Make AI-Assisted Delivery Predictable.

One platform. Three capabilities. Prevention builds new features with quality gates. Detection diagnoses what's slowing you down. Correction fixes it safely. Each one feeds the next.

Prevention

Build new features with quality built in — no rework, no surprises

Detection

Connect your repo — see exactly what's slowing delivery and where to start

Correction

Fix what Detection found — small, tested changes, verified after every fix

The Problem

Your Team Ships Faster Every Quarter. Your Codebase Gets Worse.

AI generates code without engineering discipline

AI agents produce working code fast — but without TDD, Clean Architecture, or quality gates. You ship more, but every merge adds structural debt.

Code review can't keep up with AI velocity

Your best engineers already couldn't review everything. Now AI multiplies output 3-5x. Issues slip through because humans can't scale with the volume.

Refactoring AI-generated code is a black box

AI writes code nobody fully understands. Teams can't prove fixes improved anything, so tech debt from AI output stays unfunded and grows.

Prevention

Build it right the first time.
  • Spec-driven development — define what, the agents handle how
  • 28 specialist agents — strategy, discovery, implementation, quality, deployment, and maintenance
  • 5 quality gates — vision, plan, acceptance tests, implementation, code review — no skipping
  • Structural DORA enforcement — scope analysis, elapsed time warnings, release size checks
$npm i @avesta-hq/prevention
Learn More
prevention — claude code
register user with email and password
●prevention — avesta_dispatch (MCP)
Example Mapping complete
Rules (3):
1. Valid email + password → user created, JWT returned
2. Duplicate email → rejected
3. Weak password → rejected
Scope: 3 rules, 9 examples — good size for single cycle
Starting TDD. Layer: Domain → /avesta-red
demo-app|feat/user-auth|Opus 4.6 (1M context)|
7%

Detection

Diagnose what's killing your delivery.
  • DORA + value stream metrics — lead time, failure rate, cycle time, review wait
  • 4-dimension code health scoring — architecture, maintainability, complexity, test effectiveness
  • AI readiness assessment — is your codebase ready for AI agents, or will they amplify the mess?
  • Tech debt with numbers — debt density, trend tracking, and the priority list that unlocks the budget
Learn More

Health Score

Needs Attention
6.1/10

Code health progression

0510Feb 25Mar 10Mar 14Mar 2524.35.96.1
Health improved by 205%
Feb 25 - Mar 25

Debt Density (kLOC)

Needs Attention
320/1000

Technical debt reduction

05001000Feb 25Mar 10Mar 14Mar 25650500450320
Debt reduced by 50.8%
Feb 25 - Mar 25

Test Effectiveness

Needs Attention
55%/100

Test suite reliability

050100Feb 25Mar 10Mar 14Mar 2534.2555
Effectiveness rose by 1733%
Feb 25 - Mar 25

Correction

Fix what's broken. Prove it worked.
  • Diagnosis-guided — fix what moves DORA metrics most, not what's most visible
  • Characterisation tests first — safety net before any structural change
  • TDD refactoring cycles — small, tested, committed, always green
  • Closed feedback loop — Detection verifies every fix actually worked
$npm i @avesta-hq/prevention
Learn More
correction — claude code
●Detection — prioritized by DORA impact
Top issues slowing delivery in user-service:
#
Rule
Violations
Impact
1
Depend on abstractions not concretions
464
Major
2
Class has single reason to change
338
Major
3
Anti-Corruption Layer for External Systems
351
Major
Starting with #1: Depend on abstractions not concretions — highest DORA impact
Health: 2.1/10 · Debt: 78.9 pts/kLOC
user-service|fix/tech-debt|Opus 4.6 (1M context)|
10%

Results

What Three Months Looks Like

No rewrite. No big-bang migration. Incremental improvement guided by data.

18.7 days

7.2d

Lead Time

52%

18%

Change Failure Rate

2.1 / 10

4.8

Health Score

Low

Medium

DORA Classification

Who It's For

Different Roles, Different Value

< 01 >

CTOs & Engineering Managers

Elite DORA metrics, AI readiness visibility, measurable ROI on engineering investment. Data for the board.

< 02 >

Tech Leads

Enforced best practices without being the bottleneck. Prioritised tech debt. Automated code review at staff-engineer level.

< 03 >

Developers

Learn TDD and Clean Architecture by building. Safe refactoring with characterisation tests. Concrete file/line references.

< 04 >

Product Managers

Ship features in hours not weeks. Specs in your language. Delivery metrics improving without pausing feature work.