Custom AI/ML Engineering Solutions
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About this service
Summary
FAQs
How do you decide if we need a simple AI integration or a complex custom model?
We start with your goal and data. Often, a RAG (Retrieval-Augmented Generation) system using an existing LLM is the most cost-effective and powerful solution. I'll recommend the simplest architecture that reliably achieves your goal.
What's your process for ensuring the AI output is accurate?
I implement a robust evaluation framework from the start, using human feedback and quantitative metrics. We build in confidence scoring and human review checkpoints for critical outputs to ensure quality control.
Do you provide ongoing training or optimization for the AI?
I deliver a complete, optimized system with documentation. Ongoing fine-tuning or retraining with new data can be scoped as a separate maintenance or enhancement project.
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.
Example projects
Industries