Fine-Tuned AI Agents for Product Recommendations
Trained AI agents using internal product logic and rules to recommend the best offers. Reduced manual decision-making and ensured consistent, personalized suggestions.
PROJECT CASE
6/16/20251 min read
Fine-Tuned AI Agents for Text Analysis and Business-Specific Categorization
In this project, we fine-tuned a lightweight AI model to help a company improve how it analyzes and understands text — while keeping the solution efficient and easy to run with minimal computing power.
The challenge was to create an AI agent that could perform specific business tasks such as:
Extracting key information from internal documents
Automatically classifying text into company-defined categories
Generating tailored summaries that follow internal communication style
Instead of relying on large, general-purpose AI models, we used fine-tuning to train a compact AI model on the company’s own data. This transformed a generic AI agent into a task-optimized assistant with improved understanding of the company’s specific vocabulary, patterns, and decision rules.
Key outcomes:
The agent became highly accurate in extracting structured data from unstructured text (e.g. product specs, regulatory notes)
It could categorize content according to the company’s internal taxonomy
It delivered custom summaries and responses aligned with the tone, formatting, and logic expected by the business
This approach allowed the company to deploy AI in real operational workflows without needing heavy infrastructure — all while ensuring results were faster, smarter, and perfectly tailored to their needs.