Landing Page Content Strategy: AI Engineering Agents

NAV

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1. Hero Section

Goal: Immediate hook and value proposition. Image A relaxed developer on his desk watching many screens to monitor what AI agents are doing.

Headline: Stop Using AI Just for Code Completion.
Subheadline: I help engineering teams build custom AI agents to x10 their output.
Supporting Copy: The tech limitations are fading away with the power of coding agents, even for very complex apps. You will have way more competitors in the future. The winners are going to be the ones managing agents to have more time spent with customers instead of code reviews.
Primary CTA: Book a Free 30-Min Discovery Call
Punchline: “Think twice before hiring this next dev.”

2. The Problem (The “Why”)

Goal: Agitate the pain points of the target audience (CTOs, VPs, Lead Devs).

Headline: Adding more developers doesn’t make you faster. Adding intelligent, context-aware automation does. Image 2 graphs next to each others, the first one is “adding more dev to the team” compared to productivity. It’s a O(logN). The second graph is “adding agents” and it’s exponentiel like a O(n2)
Key Pain Points:
- The Review Bottleneck: Senior devs lose 30% of their week reviewing trivial patterns that a machine should catch. Innovation stalls while PRs sit in limbo.
- The “Bus Factor” Risk: Knowledge is trapped in Slack threads and heads. Onboarding takes months because documentation is always outdated.
- The Legacy Fear: Vital parts of your system are “do not touch” zones. Upgrading feels too risky without perfect test coverage and domain knowledge.
Myth Busting:
- “AI is not ready yet.” -> False. This misconception harms your company because it’s not the case anymore.
Vision: “Imagine your devs deploying 30 pull requests a day instead of 3.”

3. The Solution: Core Pillars

Goal: Explain how the solution works and why it’s different.

Headline: Move Beyond Basic Linters and code Autocompletion
Pillar 1: Rule Agents (Zero Architectural Drift)
- “I deploy ‘Rule Agents’ that enforce your specific architectural patterns and business logic during the coding process.”
Pillar 2: Fuzzing Agents (90%+ Test Coverage)
- “Don’t wait for production bugs. I build ‘Fuzzing Agents’ that generate thousands of edge-case scenarios and synthetic user data to bulletproof your test suite.”
Pillar 3: The Review Bot (50% Faster Cycle Time)
- “A custom-trained review bot that learns from your team’s history. It handles style, utility extraction, and pattern matching before a human sees the PR.”

But, what is an agent?

An agent is a set of tools and instructions to perform a specific task. The agent is responsible to call tools, add the right context (your rules and code) to create a clear structured output usable by you or by another agent.

4. Detailed Services (The “What”)

Goal: List specific deliverables and capabilities.

Smart PR Reviews: Review pull requests based on rules you want to apply and your team’s comment history.
Better Tests: Generate massive amounts of data to find edge cases and replicate production data in your test suite.
Tackle Legacy Code: Refactor very quickly with high quality. Deploy more often.
Faster Onboarding: Generate clear visual docs on the obscure parts of your codebase.
Deploy Process: Review pipeline in depth and suggest speed improvements.
Bug Analysis: Find potential bugs before they arrive. And provide immediat solution when a bug occurs in production (connecting with your bug reporter).
Code Optimization: Find bottlenecks and suggest performance solutions.
Centralize Knowledge: Active agents that centralize team knowledge into rules that every developer can read and modify.
Security Audit: Generate reports of issues to fix first to avoid hacks.
QA Environments: Create QA environments for each branch and make the feature validation process faster.
Admin Panels: Create advanced admin panels so devs can focus on your core product and the rest of the team can read data without interupting them.

Advanced Capabilities:

Legacy Worker: Automated refactoring agents that systematically upgrade old class components or legacy syntax file-by-file.
Auto-Updating Docs: CI pipelines that auto-update your API specs and READMEs whenever logic changes.
Auto-Correct bugs: CI pipelines that creates a pull requests with an elegant solution when a bug occurs in production. Explaining in detail the bug and the solution.

5. Benefits & ROI

Goal: Justify the investment.

Financial: Avoid hiring senior devs (cost, time, stickiness).
Investor Metric: Increase your Revenue Per Employee (RPE), a critical investor metric, by scaling output without scaling headcount.
Asset Value: You are investing in CapEx. The agents you build are permanent assets that stay with the company, whereas developers may leave.
Speed: Respond to bugs in minutes instead of hours.
Code Privacy: If you want, you can run the agent on private servers with open source models running without sending your data to external providers.
Throughput: Deploy to production more than 10 times a day with confidence.
Efficiency: Have a smaller dev team that can focus on understanding product requirements.
Team Health: Give back time to devs for:
- Small internal projects.
- Technical surveys and future planning.
- Open source contributions to boost the street cred of your company.
Quote: “Compress a 12-month roadmap into 2 months.”

Does It Scale?

[note for agent: include ALL the metrics]

Goal: Prove real-world performance on complex, production-grade codebases.

Headline: Battle-Tested on Million-Line Codebases
Context: I have already implemented workflows on large codebases. Here’s an example of a production Ruby On Rails application where agents work flawlessly:

Metric	Count
Lines of Code	1,287,971
Controllers	157
Models	141
Views	497
Services	211
Specs	514
API Serializers	50
Form Objects	69
Presenters	15
Database Tables	83

Result: The agents produce the expected output consistently—and we’re not even touching the limits of what’s possible.

Core belief

You have to internalize your agents
- Future proof: use the latest models whenever you can.
- Easy to understand and change
- Code privacy and data privacy when you need to
- Custom UIs
- Fully integrated with your existing systems
- Cost of doing code is close to Zero
You have to re-define the role of developers
- Moving from 80% work and 20% thinks, to 80% think and 20% work
- Define what should be done in this 80% think
- Make automation fun and not a thread
- Adding tools without proper training is useless

6. The Process (the “How”)

Goal: Transparent engagement model.

[instruction for page generation: the process should be a vertical timeline, big, with space for the images.]

Step 1: Discovery

Time:: 30 min call.
Goal: Understand pain points and tech stack.
Image [description of image: 2 people talking on the phone. One person (me) is taking note on a paper because I’m writing the requirements of the client, very calm, organised, draw diagrams of the solution. On the other side, the client is in his office, with a lot of people. The client is stressed because there is a lot fo code to do, bugs to fix, etc. That’s why he calls, he’s in panic mode.]

Step 2: The Audit

Time:: 8h.
Goal:: Analyse current codebases, write detailed implementation plan.
Image 1 [description of image: Me looking at various screens, concentrated, writing a detailed plan on my computer.]
Image 2 [description of image: Me being sitted next to a developer from the client team. Talking with him to understand him. He exposes the issues he has.]

Step 3: Implementation

Time:: 16h - 64h (depending on complexity).
Goal:: Implement all the agents. Create documentation on how they work. Train your team to use and debug them.
Image [descrition of the image: Me In focus mode, with headphones, coding alone, concentrated, with a whiteboard next to me where all my notes are written.]

Step 4: Maintenance & Evolutions

Time: 8h.
Goal:: ~1 month after implementation, verify with your team the work done by your agents to adjust the rules and context.
Image [descrition of the image: Me calling the client to see what is missing, what is working or not, having feedback, updating some agents.]

7. About the Expert

Goal: Build trust and authority.

Profile: Senior Software Engineer with 14 years of experience.
Key Skills: Ruby on Rails (Expert), Javascript, iOS (Objective-C), Python, Bash, Admin Sys.
Experience: Extensive web development background.
Perspective: “I know the industry from the inside. I’ve been working on complex codebases that all suffered the same pain points. And today, AI can solve all of them.”
Social Links: [instruction for page generation: insert the icons of each social links here]

It’s about the future

“Current employees with a bit of training and AI agents assistant will output 20x more than regular employees.”

8. FAQ

Goal: Address objections.

“We have a unique stack. Will this work?”
- Yes. The “Rules” and “Agents” are framework-agnostic. We build them to understand your specific patterns, whether you use Rails, JavaScript, Elixir, Python, Go, Rust, etc.
“We already tried many tools but the code is garbage.”
- It was the case 6 months ago, but the models are evolving very fast. The reason the code is often bad is because of bad directions, not bad models. We have super powerful models that just need better rules and data to work on. It’s all about adding context in a smart way.
“Why having custom agents and not using external services or MCPs?”
- Great questions, I see many good reasons to have yur own agents.
  - First, you don’t depend on external services changes. If your third party tool changes prices or their API, you have to adapt. And trust me, it will happen a lot.
  - You don’t send all you data to external services, you can run agents locally and send only what is necessary, not your whole codebase.
  - Agent are mostly python scripts done the right way. These script are easy to modify and maintain. They will be responsibe to call the LLM with the right context to generate the output expected.
  - You don’t depend on external tools like MCP. This help in making your agent faster and avoid using tools you don’t have control on. Also, MCP are going to die soon in favor of SKILLs and custom tools.
Do we have to implement all agent at once?
- No. We can start step by step and tacle the biggest bottleneck first. It’s normal to start with only 2 or 3 agents and then create new ones.
Can we do all the process remotely?
- Of course! I’ll schedule the calls and meeting whenever is the best for you.

Tech stack available

Image A grid of logos for all these tech stacks, Display ALL OF THEM with this exact name: Ruby On Rails, JavaScript, ReactJS, Vue.js, Next.js, Phoenix, Elixir, FastAPI, Flask, Django, Python, Go, Rust, Laravel, PHP, Java, Kotlin, Swift, Objective-C, React-Native.

Headline: Ready to scale your engineering without scaling headcount?
CTA: Book your free discovery call now. By the way, you’re talking to me directly not to a AI agent ;)

Metadata

The url of this page will be: https://damln.com/consulting

French version: https://damln.com/consulting-fr

Spanish version: https://damln.com/consulting-es

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