From Raw Chat Logs to Customer Stories using Hindsight.
When building AI tools for customer support, one of the primary technical challenges is handling long-term memory.
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Customer interactions occur over days, weeks, or months
Customer interactions occur over days, weeks, or months across different channels. A customer might report a shipping delay on Monday, express frustration over product setup on Wednesday, and request a refund by Friday.
Standard large language model (LLM) implementations struggle with
Standard large language model (LLM) implementations struggle with this pattern. Passing an ever-expanding array of raw chat transcripts into every prompt consumes excessive tokens and makes context extraction noisy. Conversely, stateless LLM calls forget past customer issues entirely.
To solve this, I built CustomerStory AI
To solve this, I built CustomerStory AI — an application that captures unstructured support notes, retains them in a persistent episodic memory layer using Hindsight, and synthesizes them into actionable customer background stories using FastAPI, Groq, and React.
The application allows customer interactions to be stored
The application allows customer interactions to be stored as memories and later recalled to generate a concise customer story.
In this article, I will walk through the
In this article, I will walk through the architecture, memory retention pipeline, prompt grounding strategy, and real-world frontend adjustments required to integrate Hindsight into a full-stack local setup. Technical Stack and Architecture Overview The system is structured as a decoupled full-stack application: Backend: FastAPI (Python 3.11+) handling REST API endpoints, memory operations, and LLM orchestration.
Memory Engine: Hindsight running locally at "http://localhost:8888", accessed
Memory Engine: Hindsight running locally at "http://localhost:8888", accessed through the "hindsight-client" Python SDK. LLM Inference: Groq API using the "openai/gpt-oss-20b" model for prompt completion.
Frontend: React application built with TypeScript and Vite
Frontend: React application built with TypeScript and Vite, featuring a dashboard that highlights customer stories, timelines, and analytical insights. The overall request flow is: React Frontend ↓ FastAPI Backend ↙ ↘ Hindsight Groq
The React frontend communicates with FastAPI, which coordinates
The React frontend communicates with FastAPI, which coordinates both long-term memory operations through Hindsight and LLM generation through Groq. Retaining and Recalling Customer Memories
Rather than managing custom vector embeddings or manually
Rather than managing custom vector embeddings or manually querying a relational database for past notes, I integrated Hindsight as a long-term memory engine. In my backend service layer ("services/memory.py"), I instantiated the Hindsight client targeting a local instance: from hindsight_client import Hindsight client = Hindsight( base_url="http://localhost:8888" ) BANK_ID = "customer-story"
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From Raw Chat Logs to Customer Stories using Hindsight.
When building AI tools for customer support, one of the primary technical challenges is handling long-term memory.
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Source: Dev.to