Our project demonstrates building enterprise AI systems cost-effectively, focusing on matching merchant descriptors to known businesses. Using fine-tuned LLMs and advanced search, we created a solution rivaling alternatives at minimal cost. The system works in three steps: A fine-tuned Llama 3 8B model parses merchant descriptors into standardized components. A hybrid search system uses these components to find candidate matches in our database. A Llama 3 70B model then evaluates top candidates, with an AI judge reviewing results for hallucination. We achieved a 400% latency improvement while maintaining accuracy and keeping costs low and each fine-tuning round cost hundreds of dollars. Through careful optimization and simple architecture for a balance between cost, speed and accuracy, we show that small teams with modest budgets can tackle complex problems effectively using this technology. We share key insights on prompt engineering, fine-tuning and cost and latency management.