I’m Billy, a BMX rider turned AI engineer. I built OpenClaw, an autonomous agent that runs three of my brands without me lifting a finger. If you want to take your own data and turn it into a self-learning AI that can handle copywriting, social media posts, and ad campaigns, this post is for you.
Client Acquisition
First up: the data. I have 12,000 Instagram images, 8,500 product descriptions, and 4,200 customer reviews. All of it lives in a private S3 bucket mounted into a Docker container. It’s simple: raw files, a JSON manifest, and an SQLite index for metadata.
Next step: preprocessing. I run a Python script that extracts EXIF data, normalizes image dimensions to 1024×1024, and strips watermarks. For text, spaCy handles tokenization, lemmatization, and tagging. After cleaning, the tokens go into a vocabulary of 30,000 sub-word chunks. The whole preprocess batch takes 45 minutes on my 16-core AMD Threadripper.
Tools and Setup
Model selection is next. I use Claude 3 Opus from Anthropic and Gemini Pro from Google. I load both into an vLLM server for easy swapping. I test each with a prompt like, “Write a 150-character product title that highlights durability and style.” Claude scores 8.7, Gemini 7.9. So, Claude is my primary generator, with Gemini for multimodal tasks.
Training loop time: OpenClaw really shines here. I use LangChain’s Retrieval-Augmented Generation (RAG) pattern via n8n:
- Trigger Node: A webhook fires when a new file lands in S3 and pushes the key to a Redis queue.
- Ingest Node: Pulls the file, preprocesses it, writes vector embeddings into a Pinecone index.
- Generate Node: Calls Claude 3 Opus API with a prompt referencing retrieved embeddings; sets temperature to 0.3, top_p to 0.9, max_tokens to 256.
This runs on a three-node Kubernetes cluster with A100 GPUs. I use horizontal pod autoscaling to keep latency under 150 ms per request. Pushing 50 new product images per hour takes 30 seconds total for copy, hashtags, and ad headlines.
Results and Impact
Numbers matter. Over the last quarter, OpenClaw processed 2,400 new images, generated 1,800 unique product descriptions, and authored 1,200 ad captions. My Instagram click-through rate jumped from 1.3% to 2.8%, a 115% lift. The whole pipeline costs around $350 per month in cloud compute.
What makes this system automatic is the feedback loop. After each batch, I run sentiment analysis on generated captions against historical benchmarks. If a caption scores below 70%, it gets flagged for human review and re-queued with higher temperature. Otherwise, it’s pushed to scheduled posts: @ClawWear, @ClawGear, and @ClawCo.
Lessons learned:
Implementation Details
- Don’t trust one LLM for everything; use Gemini for multimodal tasks.
- Keep prompts tight. Adding a “style guide” snippet improves consistency.
- Monitor token usage—tightening max_tokens from 1,000 to 256 cut costs by 80% without hurting quality.
Ready to take your data and make it work? Check out my real AI tools at axon.nepa-ai.com.