We Speak Your Industry's Language
Starting from industry pain points, we share the real cases and lessons we've delivered.
Manufacturing & Industry
From blueprints to production scheduling, manufacturing data lives in documents and the minds of veteran engineers.
Production drawings and order parameters are entered manually — errors are hard to catch
Process know-how lives in veteran engineers' heads and is lost when they leave
Inconsistent data definitions across systems make quality tracing slow
From Blueprints to Data: Intelligent Extraction of Production Parameters for PCB Orders
A PCB manufacturer in the Yangtze River Delta automatically extracts electrical parameters and dimension annotations scattered across customer blueprints into structured data using OCR, NLP, and multimodal AI models — 'blueprints in, data out' — dramatically improving the speed and accuracy of drawing data processing, reducing manual dependence, and giving production and business decisions a precise data foundation.
Automotive & Mobility
Automotive business data spans sales, marketing and after-sales — long reporting chains and inconsistent definitions.
Business reviews depend on manual reports; one question needs multiple departments
Insurance, leads, test-drive and order metrics are scattered with different definitions
Queries break whenever data sources change, so nothing gets reused
Natural-Language Analytics: Turning a Million-Unit Automaker's Marketing War Room into a Conversation
- Ask for multi-brand, multi-region sales comparisons in natural language — no more 3-department, 5-day waits
- Stop manually aggregating insurance, leads, test-drive and order-tracking metrics
- No query rewrites when data sources change — AI adapts to new table structures automatically
From a 90%+ answer rate and 85%+ accuracy in the MVP stage, to covering 14 dashboards of the marketing war room and interactive BI — a national automaker selling over a million vehicles a year turned business analytics into a conversation anyone can start.
Finance & Legal
Knowledge and data across banks, brokers and law firms are scattered across documents, systems and expert brains.
Firm-wide knowledge is scattered across policies, contracts and systems — search relies on people
Data assets can only be read by engineers; business users can't ask questions
Contract review relies on manual line-by-line checks — slow and error-prone
A Regional Top-Tier City Commercial Bank Turns Institution-Wide Knowledge into One Question, One Answer
26 departments surveyed, 8 business-specific agents, dual-dimension tags and multi-channel permission routing — a regional top-tier city commercial bank with RMB 600 billion in assets consolidated scattered institutional knowledge into one entry point where employees get answers in seconds.
How a Bank's Data Assets Went from 'Databases Only Engineers Can Read' to 'Numbers Any Colleague Can Ask For'
Delivered on the OntiCards data semantic layer and agent platform: obscure schemas are translated into business-readable DataCards, and natural-language queries auto-generate SQL and charts. AI output achieved an 85% adoption rate in 2 hours — comparable to an expert's 8-hour output — a 4x efficiency gain.
Contract Review from 40 Minutes to 4 Minutes: A Real Estate Group's Intelligent Legal Turnaround
NLP + large language models + an expert rule base: automatically identifying key clauses, potential risks, and compliance points, with structured comparison, smart annotations, and risk grading. A real estate group spanning residential, commercial, and industrial parks moved its legal work from 'manual checking' to 'intelligent decision support.'
The Structuring Revolution in Vulnerability Intelligence: AI Semantic Matching Against Enterprise Assets
Real-time ingestion, NLP-based cleaning and structuring, AI semantic analysis to match enterprise assets, and automated risk alerts — a large enterprise group's security team automated the entire vulnerability intelligence pipeline, dramatically improving response speed and cutting manual monitoring burden.
Government & Public Services
Government and public-service scenarios have many heterogeneous endpoints; monitoring still depends on human attention.
Monitoring walls rely on human watching — anomalies are detected late
Policy documents come in messy formats that agents can't understand directly
Multi-source heterogeneous data is hard to integrate, slowing emergency coordination
Highway Network Monitoring: From 'Watching Screens' to 'AI Proactive Alerting'
A provincial transportation investment group in western China with over 6,000 km of operated highways turned thousands of surveillance feeds into perceivable, analyzable, traceable data with a multimodal vision approach — 7 camera fault categories, 8 anomaly event categories, recognition accuracy of no less than 85%, and first-response time under 5 seconds for report generation and Q&A agents.
Smart Integration of Flood-Control Data: A Copilot for Flood Detention Basins
The information management platform of a flood detention basin in southern China consolidated hydrology, meteorology, and engineering data from 'every system for itself' to 'one screen for all' — with real-time AI alerts, dynamic monitoring and simulation, and science-based management recommendations grounded in historical and live data, moving flood-control decisions from experience-driven to data-driven.
Three AI Agents, One Telecom Operator: Organizational Rollout
A provincial telecom operator deployed three focused AI agents — a public-opinion insight assistant, a sales agent, and a contract-review assistant — into the daily workflows of its marketing, sales, and legal departments, turning 'humans watch, humans chase, humans review' into 'machines watch, machines chase, machines review' and making operations, marketing, and risk control smarter.
How Policy Documents Become Data That LLMs Can Understand
A municipal talent service group's regional policy consulting platform structures and digitizes vast policy documents — precisely extracting issuing agencies, eligibility criteria, subsidy standards, and more — then uses hybrid retrieval and vector embedding so large language models can read and answer accurately, delivered through a WeChat mini-program and an offline exhibition hall.
Education
Universities and education institutions hold rich academic, student and video data, yet struggle to turn it into usable insight.
Academic data is fragmented across systems, making statistics and queries hard
Student reports are written manually by teachers — slow and inconsistent
Years of classroom video sit unused, impossible to search or analyze
The Academic Data Intelligence Cube: Making University Data Computable, Queryable, and Actionable
After integrating data from academic affairs, student services, and research systems across multiple campuses, a university compressed multi-day reporting cycles into minutes with natural-language querying and automated report generation—giving faculty and administrators conversational data access for the first time.
From Teachers Burning Midnight Oil to AI-Generated Student Reports
A school academic-affairs department serving several thousand students transformed its end-of-term comprehensive reporting from manual teacher drafting to automated generation using an LLM and multi-Agent architecture—boosting output efficiency by over 80%.
HK EDB EMM Platform: Classroom Video Auto-Analysis and Faster Teaching Feedback
By combining AI video analysis with a data-middle-platform backbone, the Hong Kong Education Bureau's EMM e-learning platform lets teachers pinpoint instructional clips without watching entire recordings—boosting lesson-search and prep efficiency by an order of magnitude.
Special-Needs Rehabilitation Reports: From 3 Days to 30 Minutes
An organization focused on special-needs children's rehabilitation and inclusive education cut its single SEN student rehabilitation-assessment report cycle from 3 days to 30 minutes using a large-model intelligent-analysis module and OntiCards automated data retrieval—while significantly improving terminology accuracy and data-citation precision.
Retail & FMCG
Retail and e-commerce data spans marketing, storefront and transaction chains, demanding near-real-time responsiveness.
Marketing and storefront data are disconnected; the full chain is hard to unify
Market intelligence is gathered manually, so reports come out slowly
Store and channel data lack consistent definitions, making daily reconciliation hard
AI Across the FMCG Value Chain: Marketing, Retail, and Enterprise in Sync
A nationwide building-materials and fast-moving-consumer-goods company built an AI application system covering marketing, retail operations, and enterprise functions—linking data-driven decision-making, store-floor intelligence, and organizational content production into a closed loop that significantly improved collaboration and market responsiveness.
Multi-Agent E-commerce Market Analysis: Automating Market Reports
An intelligent multi-agent system (MAS) delivers big-data market insights: multiple AI agents work together to automate cross-platform data collection, analysis and market report generation, then link those insights into product description and listing strategy adjustments — making data-driven decisions routine.
Knowledge-Intensive Organizations
For airlines, media groups and archives, knowledge is the core asset — yet much of it is locked in individuals and legacy media.
Expertise depends on individual memory and leaves when people do
Historical archives and media assets are paper-based or unstructured — hard to search
Senior experience is rarely systematized for new hires
Managing Dispatch Knowledge Across Its Full Lifecycle: A Smarter Knowledge Platform for Airlines
Built around dispatch operations, the platform integrates existing structured knowledge with natural-language interaction and AI, covering the full lifecycle of knowledge — capture, management, application and evolution — across three business scenarios that turn manual handbook flipping into instant answers.
Keeping Retiring Executives' Expertise in the Building with Decision Trees
Building a 'virtual executive' for senior managers approaching retirement, the platform uses a question-thought-chain-answer model and multi-path decision trees to turn each executive's decision process into an enterprise knowledge base that keeps serving management long after they leave.
A Century of Archives, Reborn Digitally: From Paper Piles to Cross-Modal Search
An AI-driven archive management platform automates the processing of vast historical materials, turning scattered multimedia archives into structured, searchable digital assets — text, images and video now interlink across modalities, keeping a century of organizational history alive and usable.
Unlocking Decades of News Media Assets: Intelligent Media Asset Management for a Media Group
An LLM-powered media asset management platform unifies decades of news content and diverse media assets in one storage and processing foundation. AI and semantic search answer asset queries in seconds, while NLP and multimodal models auto-generate news drafts and match images — freeing professionals from repetitive busywork.
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