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I found you through ChatGPT.
That's what my client's patient told them last week. Not Google. Not a Facebook ad. An AI model recommended them.
Here's what most people still get wrong about LLMO - they think it's some separate, exotic strategy. It's not. It's an extension of good SEO, done deliberately:
→ Strengthen brand presence on authoritative platforms (Reddit, Quora, Wikipedia) LLMs pull from these as trusted sources
→ Format content the way AI models parse it: clear headings, FAQs, structured steps
→ Answer the follow-up questions, not just the main query
→ Build real trust signals: verified author bios, cited sources, actual expertise (no fluff, no unverified claims)
→ Add original data/insights that make your content worth citing
→ Make sure AI crawlers can actually reach your content. Check your robots.txt
I've been applying this exact framework for a healthcare client, and it's already translating into AI-driven visibility, not just theoretical rankings.
LLM search isn't "the future" anymore. It's already sending real patients, real customers, right now.
If your business isn't showing up when someone asks ChatGPT or Gemini about your industry, that's a gap worth closing.
-Nayem
SEO & AI Consultant
The network for creativity
Join 1.25M professional creatives like you
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Creatives on Contra have earned over $150M and we are just getting started
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This entire energy drink campaign — the model, the outfit, the mountain,
the product shots, even the snowboard action — was built 100% with AI.
No casting. No location shoot. No studio. No crew.
Swipe through to see the full campaign 👉
A year ago this level of quality wasn't possible. Today it's a weekend
project.
This is where advertising is heading, and most brands haven't caught on yet.
What surprised you most — the model, the product shots, or the action shot?
Many companies are building AI applications with LLMs but struggle to understand what is happening behind the scenes. Without proper monitoring and tracing, teams cannot identify slow responses, expensive API calls, failed generations, prompt issues, or areas where AI performance can be improved.
I will integrate professional LLM observability solutions like Langfuse and Helicone to help you monitor, trace, debug, and optimize your AI applications. I will set up complete visibility into your LLM workflows, including prompt tracking, token usage analysis, latency monitoring, cost optimization, evaluation pipelines, and production performance insights.
I use modern AI monitoring and development tools including Langfuse, Helicone, LangSmith, Arize Phoenix, OpenLLMetry, OpenTelemetry, Weights & Biases, PromptLayer, Traceloop, and Grafana to build a complete LLM observability system.
My process includes SDK integration, API configuration, trace setup, prompt management, cost tracking, dashboard creation, evaluation workflows, error monitoring, and performance optimization. Whether you are building AI agents, RAG systems, chatbots, or enterprise LLM applications, I will help you reduce AI costs, improve reliability, and gain full visibility into your AI operations.
Here is my submission for the challenge #easylenscontra
I wanted to create an effect that makes people feel like they're discovering a hidden world within their everyday surroundings. The concept started with the idea of reality cracking open like glass, revealing magical dimensions beneath it. I focused on making the transformation feel cinematic and immersive by combining natural crack propagation, dynamic lighting, depth-aware effects, and seamless looping to create a smooth, believable experience.
my project link: https://easylens.snapchat.com/lens/06a6c97f-758d-7b25-8000-c7b8b27dd652
