Aurora AI by 極光有序有限公司
Enterprise AI workflows Start with a real task
Need to find engineering drawings, organize RFQ attachments, or choose a workflow to automate? Explore practical guides, rollout requirements, and human review before assessing Aurora for your team.
Aurora product and rollout articles
Enterprises do not need another chat window
The hard part of adopting AI agents is carrying a task through tool execution, human decisions, and a verifiable result. This article explains why Aurora starts with workflow, not chat.
Not every step needs approval — only consequential actions do
Routine work can continue. When an agent is about to invoke a consequential tool, Aurora brings the tool, primary parameters, and decision back to the user.
Tools and working methods cannot live only in prompts
MCP, skills, model providers, and plan-tool scope all determine what an agent can do. This article explains why each capability needs an explicit, reviewable administrative surface.
Start manufacturing AI with work the team can verify
Choose a pilot by work frequency, data availability, human review and verifiable results. Engineering documents are the Aurora workflow example used in this article.
How do you organize requirements and attachments after receiving an RFQ?
Start an RFQ review with known requirements, missing information and attachment sources. This article uses Aurora’s demonstrated email workflow to explain search and human review.
How to find the right engineering drawing by number, part, revision, and candidate search
Engineering drawing search cannot rely on filename keywords alone. Learn how drawing numbers, part numbers, revisions, title blocks, and historical records become explainable search conditions and reviewable candidates.
How to measure manufacturing AI ROI with labor, waiting, rework, and wrong-revision baselines
Manufacturing AI ROI cannot be measured by model response time alone. Use labor, waiting, rework, and wrong-revision baselines to compare one real workflow before and after implementation.
An order arrives: how do you find its drawings and verify revisions?
Which drawings does an order need, and are any missing or the wrong revision? Aurora’s DWG and order-spreadsheet example explains prerequisites, number checks and delivery review.
Answer six questions before choosing the first enterprise AI workflow
Before adopting an AI agent, define the successful result, input sources, decision boundary, tool access, failure handling, and accountable owner. This checklist helps a team choose a verifiable first workflow.
How AI agents, RPA, and workflow automation should divide the work
RPA fits repeatable predefined operations, workflows manage sequence and state, and AI agents handle steps that require interpretation and judgment. This article explains how the three approaches work together.
What to inventory before an enterprise AI agent can use data and tools
Safe enterprise-agent execution depends on explicit data sources, tool permissions, human confirmation, and recovery ownership. This article provides a practical first-phase inventory.
Background work should not disappear when a page closes
Enterprise file processing and tool execution can outlast a browser request. Learn how to accept queued, processing, completed, failed, return, and rerun behavior.
Normalize drawing numbers before matching and expose what remains unmatched
DWG filenames, title blocks, and order columns often format the same identifier differently. Learn how to define normalization, separate exact and rules-based matches, and route uncertain items for review.
Enterprise AI workflow acceptance must test more than the happy path
Invalid files, missing tool permissions, external timeouts, and human rejection can interrupt a workflow. Use this framework to accept failure, retry, handoff, and delivery behavior.