Industries & Cases

CoT Network has accumulated AI project experience across multiple industries. In keeping with client confidentiality, the following cases are displayed by industry and organizational type.

Before → After · outcomes

Real deliveries across government and multiple industries, shown as before/after comparisons; all clients are described anonymously.

Government hotline

City 12345 agent-assist platform

Before: Ticket triage, urgency, and routing all judged manually by agents — backlogs at peak, inconsistent
With AI: AI triage + policy-grounded routing + 24h four-level risk alerts + one-click daily/weekly reports

Key outcome

Triage load ↓ ~70%; reports half a day → minutes

Fire & rescue

Metro fire-service AI workbench

Before: Enforcement-document review done line-by-line by hand (hours); sensitive files can't use external AI
With AI: 9 on-prem specialist agents; smart document review; sensitive data de-identified locally before any external model

Key outcome

Document review hours → minutes; sensitive data never leaves the premises

Discipline inspection

Public-complaint AI processing platform

Before: Complaints entered by hand (~30 min each); elements transcribed manually, inconsistent categorization
With AI: Multi-channel intake + AI element extraction + routing suggestions + full audit trail

Key outcome

Intake per case 30 → 1 min; categorization consistency ↑

Water utility

Provincial water group media-content AI platform

Before: 8 document types drafted from scratch by hand (half a day); video & posters outsourced (weeks)
With AI: AI drafting + official-writing compliance checks + templated posters + text-to-video pipeline + three-role review

Key outcome

Drafting half a day → minutes; video weeks → 1–2 days

Legal services

Full-service law-firm AI assistant

Before: Case files in mixed formats, scans unsearchable; evidence organized page by page
With AI: Unified ingestion + OCR + natural-language retrieval + AI evidence extraction and memo drafts

Key outcome

Evidence prep ↓ ~60%; retrieval page-flipping → seconds

Enterprise IT ops

AIOps intelligent operations platform

Before: Alerts handled ad hoc; daily/weekly reports compiled by hand, consuming large effort
With AI: Unified alert loop + AI-assisted handling + health inspection + auto-generated reports

Key outcome

Reporting ↓ ~70%; alert-handling time ↓ ~50%

Energy (central SOE)

Enterprise knowledge base & Q&A

Before: Rules looked up by flipping files or asking experts; experts tied up by repeat questions, slow onboarding
With AI: Knowledge-base Q&A returning answers with sources in seconds, capturing expert experience

Key outcome

Consultations ↓ ~half; lookup file-flipping → seconds with sources

Telecom operator

Multi-tenant agent platform

Before: Each client delivered as a bespoke build; onboarding measured in project-weeks; duplicated effort
With AI: Standardized multi-tenant platform, provisioned by configuration

Key outcome

Onboarding ↓ ~80%; project-level → configuration-level

Deep-dive case studies

Challenge, approach, and value — the full implementation path of representative projects.

Government hotline

01/04

City 12345 hotline agent-assist platform

Government hotline · agent assist

Challenge

At peak, tickets backed up; classification, urgency, and owning department were all judged manually by agents with inconsistent results, and mass-complaint trends could only be spotted after the fact.

Approach

Without replacing the existing call and ticketing systems, we added an intelligence layer: AI ticket assessment, policy-grounded routing suggestions, 24-hour risk scanning with four-level alerts, a leadership situation cockpit, and one-click daily/weekly reports.

Value

Agent triage load down ~70%
Risk shifts from reactive handling to early warning
Routing carries policy grounding and is traceable
Report writing goes from half a day to minutes

Before → after

Manual, inconsistent triage → AI assessment cuts triage load ~70%; daily reports half a day → minutes

Fire & rescue

02/04

Metropolitan fire-service AI agent platform

Fire & rescue · on-prem agents

Challenge

Enforcement-document scoring was reviewed line by line by hand; submitted files were too sensitive for external AI; dispatch positioning and multilingual communication were hard; and equipment-maintenance status was scattered.

Approach

Nine specialist agents deployed independently on-premises — covering call positioning, fire-condition extraction, dispatch decision support, smart document review, and full equipment lifecycle management — with sensitive data de-identified locally before any external model is called.

Value

Document review from hours to minutes
Automatic alerts for overdue equipment maintenance
Sensitive content never leaves the premises
Decisions are evidence-based and records are reviewable

Before → after

Document review hours → minutes; sensitive content external-unusable → fully local de-identification

Water utility

03/04

Provincial water group media-content AI platform

Water utility · media content

Challenge

Government notices and emergency bulletins were drafted from scratch by hand with slow turnaround; video and posters were outsourced; content review happened offline with no record.

Approach

AI drafting for 8 document types plus official-writing compliance checks, templated image/poster generation, a text-to-finished-video pipeline, and a three-role review flow with up-front sensitive-word filtering.

Value

Drafting from half a day to minutes
Compliance risk intercepted up front
Routine video no longer outsourced — cycle from weeks to 1–2 days
Full audit trail, reviewable end to end

Before → after

Drafting half a day → minutes; routine video weeks (outsourced) → 1–2 days (in-house)

Legal services

04/04

Full-service law firm AI assistant

Law firm · case & evidence

Challenge

Historical case files were in inconsistent formats with unsearchable scans; evidence was organized page by page; and new hires took a long time to get up to speed.

Approach

Unified document ingestion + OCR, natural-language retrieval and Q&A with answers citing sources, and AI extraction of evidence elements to generate checklists and first-draft dispute memos, collaborating case by case.

Value

Retrieval from file-by-file flipping to second-level pinpointing
Evidence-prep time reduced ~60%
Memo drafting cut by more than half
Dormant archives become searchable assets

Before → after

Retrieval page-flipping → second-level pinpointing; evidence prep ↓ ~60%

ENER

Energy

Built knowledge management and internal intelligent Q&A systems, enabling efficient accumulation, retrieval, and reuse of specialized knowledge.

Large Central SOE

CONS

Construction & Engineering

Combined with project-based management scenarios, organized business processes and documentation systems, exploring AI-assisted knowledge extraction, project support, and management efficiency.

Leading State-Owned Enterprise

MANU

Manufacturing

Designed enterprise-level AI application roadmaps around production, management, and internal collaboration scenarios, driving pilot deployment of core scenarios.

Large State-Owned Group

TRAN

Transportation & Logistics

Focused on business knowledge distribution, process assistance, and operational support to drive AI adoption for internal service efficiency improvement.

Central Enterprise System Unit

FINA

Financial Services

Built intelligent assistance systems for professional roles around compliance, knowledge services, and internal operational support.

State Capital-Backed Institution

CONS

Consumer Goods

Combined with market, operations, and sales management needs, exploring comprehensive AI applications in business analysis, content assistance, and management support.

Large Enterprise Group

PHAR

Pharmaceutical & Healthcare

Advanced integration of AI capabilities with existing business systems around knowledge systems, process optimization, and professional service support.

State-Backed Platform

EDUC

Education & Training

Built AI-assisted applications and content production support systems with course content, knowledge services, and internal operational efficiency as the core.

Large Institution

Different Industries, Same Underlying Logic

While AI applications differ across industries, successful projects share similar underlying logic: starting from real business problems, under organizationally feasible conditions, progressively building deployable, measurable, and replicable capabilities.