AI agentsat work in real business operations

Investment AI agents consolidate project materials and research data to support due diligence, post-investment monitoring, and report preparation, with risk indicators delivered to the investment research workspace.

Task

Consolidate project materials and research data and support corporate due diligence, post-investment risk monitoring, and regulatory reporting preparation.

Connect

Internal investment research systems, project repositories, operating and financial data, public information, policies and regulations, and report templates.

Execution

Retrieve documents, cross-check data, flag risks, prepare monthly reports and drafts, and track changes across portfolio companies.

Improvements

Support recurring due diligence, reporting, and portfolio monitoring, with hourly risk updates and less repetitive document preparation.

Due diligence processing: 5–10 minutes per project. Draft regulatory submissions: 2–3 hours. Portfolio risk monitoring: continuous monitoring with hourly updates. Seven role-specific agents work together.

Bank-wide AI agents support office work, operational compliance, customer management, and multilingual services by connecting regulations, office automation data, and business workflows.

Task

Support presentations and reports, document review, compliance Q&A, prospect discovery, marketing assistance, and multilingual services.

Connect

Regulations and internal documents, office system data, Chinese- and English-language content with Cantonese support, enterprise workflows, retrieval-augmented generation (RAG), and Model Context Protocol (MCP) services.

Execution

Parse documents, retrieve authoritative knowledge, compare contracts and materials, identify prospects, and prepare business content.

Improvements

Deliver consistent document parsing, contract review, knowledge Q&A, and multilingual content across the bank.

Bank-wide monthly active usage above 40%. Document parsing accuracy: 99%. Contract review accuracy: 87%. Knowledge Q&A accuracy: above 90%. More than 20 role-specific agents support bank-wide use.

The due diligence AI agent reads licenses, financial statements, contracts, and project materials, classifies facts, flags information gaps, and prepares a sectioned draft report.

Task

Support merger, acquisition, and investment due diligence and organize company, financial, legal, and industry analysis materials.

Connect

Business licenses, financial statements, contracts, company information, historical report templates, and professional rules.

Execution

Classify materials, extract corporate, financial, and compliance findings, prepare draft sections, and flag missing information.

Improvements

Classify and extract multi-source materials consistently and present key facts, risks, and gaps in one place.

Continuously produce organized materials, extracted information, and draft reports, with key facts, risks, and decision recommendations presented together.

The claims service AI agent handles claim notifications, document preparation, and status inquiries, verifies identities and policies, and routes missing or unusual items to a review queue.

Task

Handle claim notifications, document checklists, submissions, progress tracking, and frequently asked questions.

Connect

Policy information, identity verification services, claims rules, document checklists, and case status.

Execution

Identify claim intent, verify identity and policy details, explain required materials, check omissions, and synchronize progress.

Improvements

Provide immediate feedback on document requirements, missing items, and case progress, and route anomalies to a specialist review queue.

Customers can view document requirements and case progress immediately. Straightforward cases move through the process, while exceptions enter a dedicated review queue.

The dispatch AI agent cross-checks procedures, historical faults, business intelligence, and real-time operating data to provide cited response evidence for complex conditions.

Task

Query operating procedures, historical faults, and current data to support fault response plans.

Connect

More than 4,000 static and dynamic documents, specialist terminology, business intelligence, and real-time operating data, deployed in the customer's private environment.

Execution

Interpret power terminology, retrieve policies and historical faults, cross-check business intelligence and real-time data, and return cited results.

Improvements

Return procedures, cases, and real-time data with citations through a clearer retrieval path for complex conditions.

Information retrieval time fell from tens of minutes to a few seconds; major fault plan retrieval and preparation time fell by at least 40%. The implementation covers 19 provinces.

The field inspection AI agent receives photos, voice notes, work requirements, and historical records, organizes anomaly information, and generates structured records and service briefs.

Task

Perform inspections of distribution rooms, shafts, hazards, and notice boards and produce standard records and customer service briefs.

Connect

WeCom, field photos, voice notes, historical community records, monthly records, and work requirements.

Execution

Identify equipment and work stages, organize voice notes and anomaly fields, and generate inspection records and customer service briefs.

Improvements

Archive photos, voice notes, and anomaly information on site and produce inspection records and customer briefs together, reducing omissions.

Field work time decreased by about 30%, document organization time by about 80%, and key information omissions by about 80%.

The equipment assessment AI agent consolidates operating signals, multidisciplinary diagnostics, and historical fault cases to produce converter transformer condition reports and maintenance recommendations.

Task

Assess converter transformer health, consolidate diagnostic findings, and prepare maintenance plans and response strategies.

Connect

SCADA and DCS systems, dissolved gas analysis, infrared data, video, 154 measurement points, and more than 2,000 real fault cases.

Execution

Eight specialist agents run diagnostics. A coordinating agent consolidates their findings into condition reports, similar fault cases, and maintenance recommendations.

Improvements

Analyze multi-source signals and fault cases consistently and convert diagnostic findings into standard reports and maintenance recommendations.

Analysis time was reduced from at least a week of expert review to minutes, with assessment accuracy above 90% in this project. Eight specialist agents and one coordinating agent analyze 154 measurement points and more than 2,000 real fault cases.

The overseas service AI agent uses repair cases, technical bulletins, and frontline feedback to provide bilingual fault support and initiate expert collaboration.

Task

Support fault knowledge Q&A, expert appointments, cross-region coordination, meeting summaries, and customer feedback organization.

Connect

Historical repair cases, technical bulletins, customer questions, frontline feedback, and expert collaboration records.

Execution

Retrieve fault knowledge in Chinese and English, arrange expert appointments, organize meeting summaries and frontline feedback, and produce service records.

Improvements

Continuously archive fault knowledge, expert collaboration, and feedback records so overseas dealerships can reuse repair experience.

Deployed within 2 weeks. Faster technical fault queries and expert coordination, with repair knowledge shared across overseas dealerships.

Operations agents connect solution libraries, HR policies, equipment data, and business systems to support bid review, employee questions, and energy monitoring.

Task

Support technical and commercial bids, human resources policy inquiries, energy anomaly monitoring, and performance assessment.

Connect

Enterprise solution repositories, bid materials, human resources policies, equipment operations and performance data, and business systems.

Execution

Generate draft bids and flag risks, answer employee questions, continuously monitor energy changes, and consolidate performance information.

Improvements

Draft bids, HR answers, and energy alerts are delivered within their respective timeframes, with management issues presented in one place.

Bid drafts prepared in hours rather than days. HR questions answered immediately and energy anomalies detected within minutes.

The scheduling AI agent reads role requirements, skill matrices, and attendance status to recommend shifts and replacements for leave or conflicts.

Task

Assign personnel according to production line roles, handle leave and assignment conflicts, and recommend replacements.

Connect

Role staffing levels, employee skills, proficiency, attendance, historical performance, and production demand.

Execution

Match role conditions with employee skills, identify scheduling conflicts, and recommend prioritized replacement and rescheduling plans.

Improvements

Generate shift plans within minutes and provide replacement recommendations for leave and role conflicts at the same time.

Shift planning fell from hours to minutes; leave and role conflicts trigger replacement and rescheduling options aligned with role requirements.

The material data agent handles vague or incomplete descriptions, finds similar materials, interprets model numbers, and checks naming and coding rules.

Task

Support similar-material searches, new material naming and model standards, and pre-entry review.

Connect

Material group rules, material master data, model rules, historical descriptions, and new-item review workflows.

Execution

Retrieve similar records, parse key model fields, validate names and codes, and identify required revisions.

Improvements

Validate similar materials and naming rules during entry to reduce duplicate records, errors, and inconsistent definitions.

Fuzzy search time fell from 11–19 minutes to seconds or minutes; new materials are entered and reviewed under defined rules.

The sales operations AI agent consolidates sales, expense, order, and promotion data, generates operating reports, verifies anomalies, and tracks operational actions.

Task

Support weekly and monthly reports, sales data Q&A, expense verification, promotion tracking, order entry, and duplicate photo detection.

Connect

Sales data, expense records, orders, promotion policies, distributor information, and reimbursement photos.

Execution

Calculate year-over-year and period-over-period changes, generate reports, verify expense and order rules, flag anomalies, and track promotion progress.

Improvements

Produce weekly and monthly reports, routine data answers, and expense and order verification on demand, with anomalies presented together.

Weekly and monthly reporting fell from 2–4 hours to 10–30 minutes; routine data questions return in seconds, and verification supports batch processing.

The office service AI agent connects policy knowledge with meeting, travel, and finance systems to handle employee inquiries, reservations, and service requests.

Task

Handle meeting room bookings, calendars, travel and finance requests, and IT and HR policy questions.

Connect

Company policies, meeting and calendar systems, travel and finance systems, and IT and HR help desks.

Execution

Interpret employee needs, retrieve applicable policies, connect to business systems to initiate reservations, inquiries, or services, and write back status.

Improvements

Provide one response channel for meetings, travel, finance, and employee services and reduce policy searches and system switching.

Five categories of employee services supporting thousands of employees. Meeting and travel workflows respond in seconds.

The audit AI agent reads tender and bid documents, performs rule checks and semantic similarity analysis, flags risk indicators, and generates working papers and draft reports.

Task

Support procurement material pre-reading, field extraction, audit issue checks, anomaly identification, and working paper preparation.

Connect

Tender documents, bid documents, scanned files, optical character recognition results, 50 audit rules, and historical working papers.

Execution

Extract key fields, perform semantic similarity and rule checks, flag anomalies, and generate audit working papers and draft reports.

Improvements

Batch pre-review, similarity analysis, and rule checks for documents of 3,000–4,000 pages, with risk indicators and working papers produced together.

Process 3,000–4,000 pages in a single file across pre-audit, fieldwork, and post-audit stages, reducing repetitive reading and organization. The checks apply 50 audit rules.

The customs agent reads invoices, packing lists, and product documents, validates fields, suggests Harmonized System (HS) codes, and prepares draft declarations for review.

Task

Support import and export document verification, customs declaration field preparation, and classification evidence organization for customs declarant review.

Connect

Invoices, packing lists, product materials, classification rules, and existing business systems.

Execution

Cross-check product names, quantities, and amounts, suggest HS codes with supporting evidence, and flag missing information and discrepancies.

Improvements

Process document verification and field entry together, route items requiring confirmation to customs declarants, and reduce repetitive entry.

Deliver draft customs declarations, classification evidence, and verification checklists together; customs declarants confirm classification and formal submission.