Custom AI/ML Engineering SolutionsAri Harrison
I design and implement practical AI solutions—like intelligent search, automated document analysis, or predictive features—that solve specific business problems. I move beyond demos to build production-ready systems using LLMs (Claude, OpenAI) and frameworks like LangChain. My focus is on delivering measurable value, such as reducing manual review time or surfacing hidden insights from your data.
What's included
Working AI Feature Module
A specific AI solution (e.g., a RAG-based Q&A system, a text classifier) integrated into a test environment.
Format: API endpoint(s) or functional code module.
Details: Delivered with evaluation metrics (e.g., accuracy, latency). 2 refinement rounds based on test results.
Integration Code & Prompt Library
All necessary code to connect the AI model to the client's application, including a library of optimized prompts.
Format: Source code in a Git repository with a prompts/ directory.
Details: Includes environment setup, example API calls, and documentation for prompt variables.
Technical Specification Document
A report detailing the system architecture, model choice rationale, and maintenance procedures.
Format: PDF document.
Details: Covers limitations, cost estimates for API usage, and a roadmap for potential improvements.
FAQs
Example work
Contact for pricing
Service provided by
Ari Harrison proSan Francisco, USA
- $1k+
- Earned
- 7
- Paid projects
- 19
- Followers
Custom AI/ML Engineering SolutionsAri Harrison
Contact for pricing
I design and implement practical AI solutions—like intelligent search, automated document analysis, or predictive features—that solve specific business problems. I move beyond demos to build production-ready systems using LLMs (Claude, OpenAI) and frameworks like LangChain. My focus is on delivering measurable value, such as reducing manual review time or surfacing hidden insights from your data.
What's included
Working AI Feature Module
A specific AI solution (e.g., a RAG-based Q&A system, a text classifier) integrated into a test environment.
Format: API endpoint(s) or functional code module.
Details: Delivered with evaluation metrics (e.g., accuracy, latency). 2 refinement rounds based on test results.
Integration Code & Prompt Library
All necessary code to connect the AI model to the client's application, including a library of optimized prompts.
Format: Source code in a Git repository with a prompts/ directory.
Details: Includes environment setup, example API calls, and documentation for prompt variables.
Technical Specification Document
A report detailing the system architecture, model choice rationale, and maintenance procedures.
Format: PDF document.
Details: Covers limitations, cost estimates for API usage, and a roadmap for potential improvements.
FAQs
Example work
Contact for pricing