Your Team Is Fighting AI Coding Tools, Not Leveraging Them

Find out exactly why your team is fighting these tools instead of leveraging them. In 24 hours, get a prioritized roadmap showing what's blocking effective adoption and how to fix it.

Understand What Blocks Your Team

AI coding assistant chat windows showing frustrated developer prompts

AI coding tools should feel like leverage, not another junior dev to manage.

Your team adopted Copilot, Cursor, maybe even experimented with AI coding agents. Yet engineers still don't fully trust the output and spend considerable time hand-holding the tools. Adoption happened. ROI-positive adoption didn't.

Quality Gaps

67% of developers spend more time debugging AI-generated code because it often requires significant human intervention.¹ 76% say it needs refactoring, contributing to technical debt.¹ AI-assisted PRs are 2.6x larger due to verbose code generation.²

Review Bottlenecks

AI-generated PRs wait 5.3x longer before review because reviewers distrust them and the code volume is larger.² Only 32.7% get merged vs 84.4% for human-written code.² Much of AI output is ultimately rejected or abandoned.

Insufficient Context

AI generates code that's syntactically correct but functionally wrong because it lacks awareness of system architecture or business logic.²˒³ Most tools work best on one repository at a time and struggle with cross-repository context.³

The Productivity Illusion

Studies show developers using AI tools take 19% longer on tasks despite believing they were faster.⁴ Teams see 7.2% lower delivery stability because code volume moves faster than the system's ability to verify quality.⁵

Sources:

¹ Harness, State of Software Delivery 2025 · ² LinearB, The DevEx Guide to AI-Driven Software Development · ³ Jellyfish, AI Transformation: Real-World Data and Productivity Insights · ⁴ METR, Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity · ⁵ DORA, 2024 DORA Report

Why Your AI Coding Investment Isn't Paying Off

My audit focuses on fundamental issues preventing your team from using AI coding tools to their full potential, both at the codebase and SDLC levels.

Inaccessible Coding Standards

Coding standards exist in developers' heads, outdated wikis, or aren't discoverable by AI coding tools during development. AI coding tools generate code that's syntactically correct but stylistically inconsistent, requiring rework during PR reviews.

Indicators

No .editorconfig, prettier.config, or equivalent in repo root.
Style guide exists but isn't linked in contributing docs.
ADRs aren't discoverable or accessible.

Poor Context Engineering

Core docs (README, ARCHITECTURE.md, AGENTS.md) are missing, stale, or don't communicate the mental model needed to contribute. AI coding tools lack context about module boundaries, dependency graphs, and workflows, producing solutions that work but violate design principles.

Indicators

README doesn't explain repo structure or key abstractions.
No agent instruction files (AGENTS.md, .cursorrules, etc.).
Missing or outdated onboarding process for an AI coding tool at the start of each working session.

Broken Feedback Mechanisms

Quality gates (linters, formatters, test suites) don't exist, aren't integrated into the AI coding tool workflow, or fail without actionable errors. AI coding tools introduce regressions that only surface in CI/CD or human review, creating redundant iteration cycles.

Indicators

Test coverage below 60% or missing completely.
No pre-commit hooks enforcement of linting/formatting.
AI coding tools can't execute test commands to validate changes.

Insufficient Product Context

AI coding tools get vague directives without business logic, user needs, or acceptance criteria. They deliver code that passes tests but misses intent, resulting in low-value output which requires significant rework.

Indicators

Task descriptions lack acceptance criteria or success metrics.
No project or feature docs explaining the WHY.
PRDs or specs not accessible or linked to tasks executed by AI coding assistants

How the AI Coding Tools Adoption Audit Works

Know exactly what's wrong and what to fix first in the next 24 hours.

Discovery Call and Access Sharing

We start with a short discovery call so I understand your team's workflow and constraints. You provide read-only repo access and sample tasks for me to begin the audit.

24-Hour Deep Dive & Interviews

I analyze your codebase for best practices already in place and gaps that remain. If needed, I send engineers a quick survey to understand how they currently use AI coding tools.

Get Your Roadmap

You receive a report with identified gaps and a prioritized roadmap: quick wins, medium-term fixes, and long-term improvements ready for immediate action.

Choose Your Path Forward

Understand what's blocking your team. Fix the blockers. Or unlock fully autonomous AI coding with unparalleled productivity gains.

Adoption Audit

Identify what's blocking efficient AI coding tool adoption in a single repository.

24-hour audit of a single repository
Blocker scorecard across all 4 categories
Prioritized roadmap with actionable recommendations
Post-audit walkthrough call

Get Your Roadmap

Audit + Implementation

Audit plus hands-on implementation of high-impact fixes.

Everything in Adoption Audit
Fix coding standards accessibility
Create or update context documentation
Set up feedback mechanisms
Comprehensive guides for your team

Fix What's Blocking You

Agentic Transformation

Transition from manual AI assistance to autonomous AI agentic coding.

Everything in Audit + Implementation, across all repositories
Custom context management system for your team's workflow
Structured workflows enabling AI to work with minimal oversight
Training session for the development team

Unlock Autonomous AI Coding

Built on Real Experience

Viktor Malyi

AI Engineering Leader with 16 Years Building Production Systems. Now Helping Teams Adopt AI Coding Tools.

I've been pioneering AI coding tools for 2 years (before wide market adoption) deploying them in real production environments. Vendors claim their tools work autonomously out of the box. I know what it actually takes to enable truly agentic coding capabilities and bridge the gap between marketing promises and production reality.

5 Production AI Systems Delivered16 Years Engineering LeadershipFeatured at Apple WWDC15+ Engineers Led & Mentored

FAQ

This approach is independent of team size. The changes live inside your codebase: documentation, context files, and feedback mechanisms that guide AI tools toward correct output regardless of how many developers use them. Large codebases benefit the most. The Agentic Transformation tier provides significant leverage when AI agents need to navigate complex architectures autonomously.

Absolutely. The audit is vendor-agnostic and focuses on prompts, context flow, guardrails, feedback loops, and workflows, regardless of which AI coding assistant you are using.

A blocker scorecard, prioritized roadmap, and concrete fixes mapped to impact, plus a live walkthrough (in a follow-up call) so your team knows what to do next.

Yes. Choose the Audit + Implementation or Agentic Transformation tier and I'll execute the high-impact changes with your team.

My analysis tools send code snippets to Anthropic's API for processing. Under their commercial API terms, this data is not used for model training and API logs are deleted after 7 days. Only files I explicitly read during the audit are transmitted. Your original codebase remains untouched on your systems. Data is never sold to third parties.

If the audit doesn't surface real blockers or opportunities, I refund the full price to you.

Ready to Get Answers?

In 24 hours, get a precise roadmap showing exactly what's blocking your team from leveraging AI coding tools to their full potential.

Get Your Roadmap