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$
USD
I will reduce your ai API costs in half with context optimization
Andrew L
About this gig
The problem isn't your AI. The problem is your data.
When you feed an LLM raw PDFs, wikis, and messy codebases, you are paying for thousands of useless tokens. The AI gets confused by the noise, resulting in hallucinations, sluggish response times, and massive API bills.
I am a Context Architect. I don't just extract text; I mathematically compress and structure your raw data into highly optimized .mem rulesets designed natively for LLM consumption.
The Results:
- Zero Hallucinations: Your AI receives pure, hierarchical logic.
- 50% API Cost Reduction: By stripping conversational filler and formatting noise, token usage plummets.
- Plug-and-Play: Drop the file directly into your Custom GPT, RAG pipeline, or Cursor IDE.
The Packages:
- Basic (Context Audit): Optimization of up to 5 documents. Perfect for testing the waters.
- Standard (Context Architecture): Full processing of up to 50 documents, including semantic mapping.
- Premium (Enterprise Memory Map): Complete contextual mapping for large codebases and multi-repo systems.
Stop paying API fees for formatting noise. Let's optimize your Context Architecture today.
Get to know Andrew L
Andrew L
AI Systems Architect
- FromUnited States
- Member sinceOct 2015
Languages
English
I am an AI Systems Architect & Data Optimization Specialist. If your Custom GPT, RAG pipeline, or AI Agent is hallucinating or costing too much in API fees, the problem isn't the AI—it's the messy data you are feeding it. I use custom extraction algorithms to transform your unstructured corporate data (PDFs, wikis) into dense, highly efficient AI Rulesets (.mem files). This reduces token costs by up to 98% and eliminates hallucinations by giving the AI strict, hierarchical logic. I am also the creator of the AjaxSpeaks Autonomous AI Swarm architecture.
My Portfolio
FAQ
Why choose this service over standard data extraction or OCR?
Standard extraction gives you messy text that confuses AI and wastes API tokens. I deliver mathematically compressed, hierarchical logic. Your AI gets pure context with zero hallucinations, your API costs drop by up to 50%, and the file is instantly ready for your Custom GPT or RAG pipeline.
How does this reduce my API costs?
Raw corporate data is full of redundant, useless tokens (conversational filler, page numbers, bad formatting). By extracting only the high-signal semantic logic, the file size shrinks dramatically. Fewer tokens sent to the API per query equals drastically lower costs.
What exactly do I receive at the end of the gig?
You receive a mathematically compressed .mem or .json ruleset containing the hierarchical logic of your data. You can instantly upload this file to your Custom GPT, Cursor IDE, or backend RAG database to begin querying immediately, without writing any extra code.
Will this work with OpenAI, Claude, or my Custom GPT?
Yes! The final deliverable is a highly-optimized file that can be dragged and dropped directly into any Custom GPT knowledge base, RAG pipeline, Claude Project, or Cursor IDE environment. It is universally compatible.
Is my proprietary corporate data secure
Absolutely. I operate under strict confidentiality. Your files are processed in a secure, sandboxed environment, used solely for the context extraction process, and permanently deleted upon delivery. Your proprietary data is never used to train public AI models.
What languages can you process and optimize?
My extraction pipeline is powered by advanced LLM architecture, meaning I can process documents in virtually any language (English, Spanish, French, German, Mandarin, Japanese, Arabic, etc.). Furthermore, I can perform cross-lingual compression—meaning you can provide me with a messy document in Ger
What do you need from me to get started?
I just need the raw data you want your AI to understand. This can be PDF documents, a GitHub repository link, API documentation, or wiki exports. I handle all the extraction and formatting from there!
What is a "Context Architecture" file?
It is a mathematically compressed, hierarchically structured text file designed specifically for Large Language Models. Instead of feeding your AI raw, messy PDFs that cause it to hallucinate, I deliver a clean, optimized ruleset that the AI understands perfectly.
What is a RAG pipeline
RAG (Retrieval-Augmented Generation) is how an AI searches your private documents to answer questions without hallucinating. My service mathematically compresses your documents so your RAG system can retrieve facts perfectly, drastically reducing your AI API token costs.
