How Huolala’s Wukong Platform Solves Large‑Model Deployment Challenges

Introduction

The generative AI wave, represented by ChatGPT, has swept the globe, but many enterprises face the “technology hype, implementation difficulty” dilemma. Huolala, leveraging its deep AI experience in logistics, built the Wukong platform – a one‑stop large‑model development environment – now deployed in over 14 business units and 50+ real scenarios.

Challenges of Large‑Model Deployment

Despite widespread adoption, Huolala encounters several obstacles:

Unifying structured and unstructured enterprise knowledge for model consumption.

Dynamic composition of multiple agents to satisfy complex workflows that a single robot cannot handle.

Fragmented multimodal data (text tickets, tables, images, voice records) that are hard to parse uniformly.

Semantic gaps between structured and unstructured data, leading to low knowledge‑graph construction efficiency.

Fragmented processing pipelines requiring separate systems for image recognition, text analysis, video annotation, etc.

Difficulty scaling to diverse business needs (report parsing, dialogue QA, map routing) and low reuse of components.

Technical Breakthroughs of Huolala’s Large‑Model Development System

1. Wukong Platform Overview

Wukong is an internal, all‑business, one‑stop large‑model application platform. It provides AI workflow, low‑code/no‑code development, a large‑model marketplace, multimodal knowledge engine, Multi‑Agent collaboration, dynamic plug‑in architecture, deployment, monitoring, and observability. The platform enables fast, stable, visualized development of AI applications.

2. Multimodal Knowledge Engine

The engine ingests documents, tables, images, videos, webpages, and cloud documents, supporting more than 15 modalities. It performs semantic processing, embedding selection, knowledge chunking, and integrates security checks (WAF, DLP). After parsing, knowledge is indexed and stored in multiple back‑ends, enabling incremental updates without full retraining.

Knowledge construction and management are illustrated below.

3. Agent Workflow Engine

Agent Workflow offers a visual canvas where users drag and connect nodes, each representing an independent AI component. It supports task‑flow, dialogue‑flow, and voice‑flow types, enabling complex business logic to be combined with large‑model capabilities.

Multi‑Agent collaboration allows a parent workflow to invoke multiple child workflows, reducing duplicated development and supporting diverse scenarios.

4. Stability, Observability, and Usability

Observability covers the full execution chain: model calls, prompt assembly, knowledge retrieval, tool invocation, memory extraction, planning, actions, and result generation. It records resource consumption and cost. Usability includes a unified release pipeline, one‑click deployment to browsers, mini‑programs, service desks, Feishu bots, and open APIs/SDKs.

Representative Use Cases

Examples include an Office Copilot that unifies dozens of internal service desks, a car‑insurance quoting tool that extracts information from image‑based quotes, and an “Agent Plaza” that aggregates reusable AI agents for various business units.

Future Outlook

Huolala plans to continue lowering technical barriers, expanding the platform’s capabilities, and fostering a vibrant ecosystem where business teams can quickly build and iterate large‑model applications.