Business Overview

Starday is an AI-powered food and beverage (F&B) innovation company that combines deep consumer packaged goods expertise with AI and predictive analytics to enhance F&B decision making. After successfully launching four brands and 16 products of their own to retail shelves, Starday partnered with F&B brands, manufacturers, distributors, and retailers to help them drive organic growth through high-potential, risk-reduced innovation opportunities.

Starday required a solution to manage a massive, unstructured dataset of social media content from platforms such as TikTok. With millions of posts generated daily, traditional methods were inefficient and couldn’t keep pace with emerging trends. The company partnered with NIX for an automated solution that categorizes content, identifies key trends, and generates actionable reports to guide future product development.

Challenge

The primary challenge was the sheer scale and complexity of social media data, and with trends changing by the second, speed was paramount. The client needed to move beyond simple keyword tracking to a more nuanced understanding of conversations. The specific pain points included:

  • 1

    Scalability and data volume: Required a 4x increase in data capacity (25,000 → 100,000+ posts/day) to keep pace with the millions of daily posts and ensure efficient categorization.

  • 2

    Trend detection: Lack of an automated system to continuously update their ontology of keywords and topics, leading to missed opportunities.

  • 3

    Insight extraction: Difficulty in extracting complex relationships and actionable insights from unstructured text.

Project Scope

NIX AI engineers needed to create an AI solution from scratch that would:

  • Efficiently categorize a high volume of TikTok posts with a target of 100,000 posts per day
  • Expand the content categorization system using LLMs to identify new topic-keyword relationships, which would improve post-classification accuracy and trend analysis
  • Generate comprehensive reports from TikTok data to define key organic content themes and provide actionable insights into sponsored content performance
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Solution

Our team designed a three-pipeline system, strategically leveraging AI models within the client’s existing AWS infrastructure. This integrated AI solution was engineered for raw power and efficiency, ensuring the client could scale their social media analysis without a significant increase in operational costs.

  • 01

    Optimizing Value with a Tiered LLM Approach

    NIX AI engineers implemented a tiered model-selection strategy to optimize both speed and value. We utilized Anthropic Claude 3 Haiku as the primary workhorse for high-volume, repetitive tasks, automating functions such as domain relevance validation and term categorization to expedite data ingestion.

    When a higher level of reasoning was needed, we deployed Anthropic Claude 3.5 Sonnet. Its advanced capabilities were critical for synthesizing insights from the knowledge graph and generating the final reports that provided our client with the strategic intelligence needed for data-driven decisions.

  • 02

    Pipeline Architecture for Actionable Insights

    We built the AI solution on top of specialized pipelines designed to handle the full data life cycle, each addressing a specific business need.

    • Embedding and vector search pipeline: Our core pipeline uses vector embeddings and semantic search to analyze millions of TikTok posts. Instead of just matching keywords, it leverages the semantic meaning of text to categorize posts more accurately, leading to more reliable insights.
    • Automatic ontology maintenance pipeline: Built on the LangChain framework, this pipeline continuously discovers, filters, and adds new topic-keyword combinations to the client’s ontology. This ensures the ontology remains current, providing a competitive edge in a rapidly changing market.
    • Automated analysis pipeline: We developed an automated analysis pipeline to address the challenge of analyzing unstructured social media data. The pipeline first uses BERTopic for topic modeling and then employs GraphRAG to extract entities and relationships. This process creates a structured knowledge graph, unlocking powerful, query-based insights that provide a comprehensive view of social media trends and the effectiveness of sponsored content.

Trend Tracking Process

Outcome

By implementing this AI-driven solution, Starday fundamentally transformed its ability to process and act on social media data.

The solution, developed by NIX, streamlined the entire analysis workflow, leading to a significant increase in operational efficiency and a deeper, more granular understanding of market trends.

Key Outcomes:

  • Accelerated analysis: The solution enables the client to classify 100,000 posts by topic in under an hour, delivering near real-time insights for timely, strategic decisions.
  • Rapid reporting: Comprehensive reports from hundreds of thousands of posts are now available in just two to three hours, enabling a faster, more agile response to market shifts and emerging trends.
  • Actionable insights: By transforming unstructured data into meaningful intelligence, we empowered the client to make faster, more confident decisions based on a deeper understanding of audience behavior.
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Team:

Team:

( Project Manager, QA Engineer , 2 Data Science Engineers, Data Engineer, DevOps )
Tech stack:

Tech stack:

Python, AWS GraphRAG Toolkit, Langchain, Anthropic Claude, S3, Lambda, Bedrock, EventBridge, AWS Batch, BERTopic, PostgreSQL, PGvector

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