Case Study

Tiger Agent: Enterprise RAG Knowledge Assistant for Telecom

Client

global markets’s Leading Telecom Company

Industry

Region

global markets

Overview

global markets's largest telecommunications operator manages a vast and constantly evolving library of internal documentation, from product policies and technical manuals to operational procedures; spread across formats and silos with no unified access layer. Support and operations teams had no efficient way to retrieve precise answers from this documentation, leading to slow resolution times and inconsistent responses.

Prime Horizon designed and built Tiger Agent, a production-grade Retrieval-Augmented Generation (RAG) platform that transforms unstructured enterprise documents into an AI-powered knowledge assistant. The system ingests PDFs and diverse document formats, converts them into vector embeddings stored in a dedicated Weaviate vector database, and answers natural language queries by combining semantic understanding with keyword precision through hybrid search.

The result is an enterprise assistant capable of delivering context-grounded answers with 92–96% retrieval precision and an average response time of 6–10 seconds — with complete data sovereignty, no per-seat licensing costs, and a security posture built for a regulated telecommunications environment.

Technologies Used

Python

FastAPI

OpenAI

MongoDB

Redis

React

TypeScript

Ant Design

Tailwind CSS

Challenges

Unstructured, Heterogeneous Documentation: Internal knowledge was spread across PDFs, reports, and documents in inconsistent formats with no standardized structure, making automated extraction and reliable chunking difficult.
Retrieval Consistency and Precision: Pure semantic search produced inconsistent results on domain-specific telecom terminology, where exact keyword matches matter as much as conceptual relevance.
LLM Calibration for Grounded Responses: Out-of-the-box LLM behavior required careful prompt engineering and temperature tuning to prevent hallucinations and ensure responses stayed grounded in source documentation.
Query Classification and Fallback Handling: The system needed to correctly distinguish between queries it could answer from the knowledge base, queries answerable from conversation context, and queries that genuinely had no matching information.
Data Sovereignty and Security: As a telecom operator, the client required all data to remain within a controlled infrastructure with no dependency on third-party hosted vector services, and robust access control throughout.
Enterprise Operability: Beyond the chatbot itself, the client needed tools to manage documents, monitor usage, review conversation history, cache frequent responses, and track system performance, all without developer intervention.

Solutions

Hybrid Search with Weaviate: Implemented a hybrid retrieval pipeline combining vector-based semantic search with keyword (BM25) scoring, tuned via configurable alpha weighting, to handle both conceptual queries and precise term lookups across telecom-specific content.
Pattern-Based Document Processing Pipeline: Built an automated ingestion pipeline which initially dependent on LangChain but eventually shifted to a customized approach and also PyPDF2 that parses, cleans, and chunks documents into semantically coherent units, generates OpenAI embeddings, and loads them into Weaviate with deduplication logic to prevent redundant entries.
Query Rewriting and Contextual Rephrasing: Before retrieval, user queries are rephrased using conversation history to resolve pronoun references and implicit context, improving retrieval accuracy on multi-turn conversations.
Layered Fallback Architecture: A three-tier fallback system routes queries: first to the vector database, then to conversation history (using embedding-based relevance scoring), and finally to a defined “no result” response, preventing the model from generating unsupported answers.
Advanced Prompt Engineering with LLM Judge: A self-evaluating prompt design embeds an LLM feedback layer (using a §-delimited format) that silently scores its own responses for quality, powering continuous improvement without user-visible overhead.
Self-Hosted Infrastructure: Weaviate, MongoDB, and Redis are all properly containeraized and self hosted available in both Docker Container and an Independent Service system. It gives the client full control over data residency and eliminates external vector database licensing costs.
Embeddable Chat Widget: A separate React-based chat widget (orange-agent-chat) built as a standalone embeddable component allows integration into existing internal portals via iframe without requiring any frontend rebuild.
Admin Dashboard: A full React + TypeScript admin panel provides document management (Data Bank), live conversation monitoring, query log analytics, typeahead/suggestion management, response caching controls, and performance dashboards, enabling non-technical administrators to operate the platform independently.

Measurable Results

92–96% retrieval precision across production queries

4-8 seconds average end-to-end response time (without caching)

100% data sovereignty- all embeddings, documents, and conversation logs self-hosted with zero third-party cloud vector dependency

Eliminated per-seat licensing costs compared to commercial knowledge management alternatives

Multi-format document support PDFs and diverse file types processed through a single automated ingestion pipeline

Zero-touch document management operations team can upload, update, and retire documents via admin dashboard without engineering involvement