The Problem: Routine Work Consuming Senior Staff Time
A 20-person B2B SaaS company in Switzerland faces a common problem: senior staff spend too much time on routine tasks, such as answering order and shipment status queries. This reduces their capacity for high-value work, such as product development and strategic account management. The solution is a Retrieval-Augmented Generation (RAG) assistant that can handle these routine queries autonomously. The assistant retrieves relevant documents from a vector database and uses them to ground the LLM’s response, ensuring accuracy and reducing hallucinations. The goal is to free up senior staff from routine work, allowing them to focus on complex issues. This deep dive explores how to implement such a system in 4 weeks, using pgvector for embeddings search and integrating with Google Workspace.
Mechanism: How the RAG Assistant Works
The RAG assistant works by retrieving relevant documents from a vector database and using them to ground the LLM’s response. The process starts with ingesting documents, such as order records, shipment logs, and policy documents. These documents are split into chunks, and each chunk is converted into an embedding using a model like OpenAI’s text-embedding-3-small. The embeddings are stored in pgvector, a PostgreSQL extension that enables vector similarity search. When a user asks a question, the question is also converted into an embedding, and the vector database retrieves the most similar chunks. These chunks are then passed to the LLM, which uses them to generate a response. The LLM is prompted to use only the retrieved chunks, reducing the risk of hallucination. The response is then sent to the user via Google Workspace, such as Gmail or Chat.
Trade-offs: Model Choice and Data Privacy
The main trade-off is between using a third-party API (like OpenAI) and an open-weight model on your own hardware. Third-party APIs offer higher quality and lower maintenance but raise GDPR concerns due to data leaving your control. Open-weight models (like Llama 3 or Mistral) can run on your own hardware, ensuring data stays in Switzerland, but require more technical expertise and may have lower quality. For a small company, a hybrid approach is often best: use third-party APIs for non-sensitive tasks and open-weight models for sensitive data. Another trade-off is between accuracy and speed. More complex retrieval strategies, such as hybrid search (combining vector and keyword search), improve accuracy but increase latency. For a 20-person company, a simple vector search is often sufficient.
Recommendation: A 4-Week Implementation Plan
Week 1: Conduct a process audit to identify high-volume, low-complexity tasks. Define success metrics: cycle time, error rate, and customer satisfaction. Build a baseline by measuring current performance. Week 2: Ingest data, generate embeddings, and set up pgvector. Test the retrieval process to ensure accuracy. Week 3: Integrate with Google Workspace and test the assistant with internal users. Refine prompts and data sources based on feedback. Week 4: Conduct user acceptance testing and GDPR compliance checks. Hand over the system to the client and provide training. This timeline assumes the client has clean, accessible data and dedicated staff available for interviews and testing. If data quality is poor, additional time may be needed for cleaning and preprocessing.
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