Automates Content Categorization with Lamini

Lamini successfully implemented Lamini’s LLM inference and tuning solution to automate the categorization of vast amounts of data for their enterprise customers, significantly enhancing efficiency and reducing manual labor by over 1200 hours annually.

Overview is the first GTM AI platform designed to speed up go-to-market efforts for companies by generating a wide array of content, including product descriptions, sales materials, and marketing collateral. The company faced a significant challenge in automating content categorization for a Fortune 100 client, which involves over 900 categories. turned to Lamini’s LLM inference and tuning platform to address this issue.

Opportunity: Automating Tedious Categorization Tasks's enterprise client needed an efficient solution to categorize a massive volume of data across 900 categories. Historically, this task was performed manually, consuming significant time and resources. The company's co-founder and CTO, Chris Lu, explains, "Keeping 900 categories in their head and trying to find the best ones is very tedious. We found that a large language model could do a much better job." initially tried GPT-4 but faced issues with hallucinations and high costs due to the extensive context required for each category. This led them to explore alternatives and eventually choose Lamini for its superior performance and cost-effectiveness.

Solution: Leveraging Lamini’s inference and tuning capabilities used Lamini’s classifier SDK to tune a dataset of approximately 50,000 entries, enabling the LLM to categorize a large amount of content accurately. They then successfully deployed the tuned model into production in one day, with almost no engineering resources required.

"Lamini's classifier SDK is easy to use. It was also very easy to put the model into production, which is still in use today," says Chris Lu. 

Since the tuned model was released in late March, it has been run well over 2000 times, saving an estimated 1200 hours of manual work annually.

Outcome: 75% Time Reduction and 100% Accuracy

Since deploying the tuned model, has achieved remarkable results:

  • 75% reduction in manual categorization time: Each categorization task, which previously took 15-30 minutes, is now automated, saving over 1200 hours of manual work annually.
  • 100% accuracy with reduced hallucinations: The model provides the top five categories along with a confidence score, significantly reducing the time spent on verifying and correcting errors.
  • Set it and forget it: The solution requires minimal maintenance, allowing to focus on other critical tasks.
Chris Lu highlights the ease of use and implementation, stating, "Once [the LLM built with Lamini] was ready, we tested it, and it was so easy to deploy to production. It allowed us to move really rapidly."

Next Steps: More LLMs! and Lamini are excited to partner together to build more custom LLMs that empower GTM teams. Fun fact, we used’s workflow to draft this case study, try it yourself now!