Case Study: Personalizing Chatbots with Timeseries, Behaviors, and More

Beyond direct Text-to-SQL querying, LLMs can power chatbots for customer service, internal knowledge sharing, and personalized recommendations. In financial services, pharma, and even other industries, these chatbots can leverage time-series data, user behavior, and business logic to provide more relevant, customized interactions.

Scenario: An investment advisory chatbot assists clients with portfolio decisions. Queries might include:

• “What’s my portfolio’s performance trend over the last year?”
• “Suggest adjustments to reduce volatility while maintaining similar returns.”

Challenges:

• Dynamic Personalization: Each user’s investment history, risk profile, and interactions form a personal data layer.
• Temporal Data Understanding: Time-series analysis is needed to interpret trends, volatility shifts, and performance changes over specific periods.
• Multi-Modal Context: The chatbot should integrate behavior analytics, market conditions, and portfolio constraints into a cohesive response.

Solution with KGs & Contextualization: By building a KG enriched with user segments, product categories, and historic interaction patterns, the LLM can provide responses rooted in the individual’s context. When paired with Text-to-SQL capabilities, it can surface data-driven recommendations, filtering queries through the lens of each user’s unique financial journey or, in the case of pharma, clinical patterns relevant to individual practitioners or researchers.

An example with cognee

TBD