LargitData — Enterprise Intelligence & Risk AI Platform

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Industry Solutions

Government agencies shoulder the mandate of public service excellence and governance quality. LargitData leverages InfoMiner sentiment intelligence and advanced AI document processing to help government tiers track public opinion, streamline policy communications, and empower data-informed policymaking for responsive smart governance.

Infographic for AI Solutions for Government Agencies, illustrating key concepts from Industry Solutions

Industry Challenges

Modern governments face the dual challenge of a rapidly evolving public opinion environment and growing civic engagement:

  • Difficulty Capturing Real-Time Public Opinion：Citizen feedback scatters across social platforms, news comment sections, forums, and official petition channels, making legacy polling and focus groups inadequate for real-time sentiment tracking.
  • Policy Communication Effectiveness Is Hard to Measure：After policy campaigns, the lack of effective tools to assess public acceptance and response makes it difficult to adjust communication strategies in real time.
  • Insufficient Timeliness in Crisis Sentiment Response：Negative sentiment related to public safety, environmental pollution, or policy controversies can spread within hours; without real-time detection, it can severely damage the government's public image.
  • Inadequate Cross-Department Knowledge Sharing：Regulatory documents, standard operating procedures, and historical cases are stored in silos across different bureaus and departments, making it time-consuming and error-prone for civil servants to locate the information they need.
  • Low Efficiency in Official Document and Report Processing：Large volumes of official documents, research reports, and meeting minutes still rely on manual reading and summarization, reducing administrative efficiency.

Industry Solutions

LargitData provides government agencies with a comprehensive AI solution suite:

InfoMiner Social Listening

  • Monitors 100,000+ channels across news media, social platforms, forums, and civic policy spaces (coverage varies by subscription tier and data licensing).
  • AI sentiment analysis tracks public polarity toward specific policies and civic issues, delivering quantifiable benchmarks for longitudinal tracking.
  • Thematic trend tracking and keyword cloud analytics assist policymakers in rapidly identifying core public concerns.
  • Automated alerts notify spokespersons and press liaisons when negative sentiment spikes abnormally; thresholds are tunable to agency requirements.

Learn MoreInfoMiner Social Listening

RAGi — Intelligent Official Document Query and Knowledge Management

  • Consolidate regulatory compilations, operational manuals, historical official documents, and meeting minutes into an AI knowledge base that civil servants can query directly in natural language.
  • Supports on-premise deployment, so sensitive data can stay within the agency's own server room, helping meet cybersecurity-related requirements; the actual controls still need to be verified against the deployment approach.
  • AI generates automated executive summaries of lengthy briefs and meeting minutes to reduce reading workloads, subject to human verification.

Learn MoreRAGi Enterprise AI Retrieval-Augmented Generation Engine

AI Governance Copilot — Executive Briefings and Decision Support

  • Built on sentiment monitoring, daily executive intelligence briefings are generated automatically: key issues, escalating sentiment events, recommended handling priorities.
  • For incidents, timelines, stakeholder positions and recommended messaging are compiled automatically; before council sessions, debate preparation materials are produced.
  • Every conclusion links to traceable source citations, preserving full human review and audit trails.

Learn MoreGovernment AI Copilot

Diverse application scenarios

Scenario 1: Public Sentiment Assessment Prior to Policy Rollout

Prior to launching an urban renewal initiative, a municipal government leveraged InfoMiner to analyze citizen discussions and sentiment polarity across social media and forums. Findings revealed public concerns clustered around relocation compensation and traffic disruptions during construction, prompting the communications team to tailor outreach and publish proactive FAQs. Note that online demographics differ from the general populace; cross-reference web intelligence with traditional surveys and official petitions to avoid conflating digital discourse with total public consensus.

Scenario 2: Real-Time Sentiment Monitoring During Disaster Response

During a typhoon emergency, an emergency operations center used InfoMiner to monitor citizen-reported damage, SOS distress messages, and stranded geolocations on social channels. The system aggregated and prioritized urgent distress calls, empowering the command center to gauge local damage and allocate rescue assets. Such capabilities serve auxiliary roles; official disaster dispatch and emergency command proceed via established statutory emergency protocols.

Scenario 3: Cross-Department Regulatory Knowledge Base Construction

A metropolitan municipality deployed RAGi to build an inter-agency statutory and SOP knowledge base. When handling cross-departmental matters, personnel query in natural language (e.g., 'What inter-agency approvals are required for a building permit application?'), and RAGi extracts relevant clauses and procedural workflows across municipal manuals, minimizing back-and-forth inquiries. Knowledge accuracy depends heavily on document freshness; establish strict version control and decommissioning cadences.

Scenario 4: Ongoing Tracking of Governance Satisfaction

The Mayor's Office deploys InfoMiner to longitudinally monitor online discussion volume and sentiment trajectories across municipal infrastructure projects (e.g., transit extensions, social housing, park revitalizations). Monthly automated public sentiment reports equip policy teams to track policy perceptions, providing data-driven input for policy prioritization.

Scenario 5: Automated Official Document Summarization and Classification

Large volumes of incoming official documents and petitions are summarized, classified, and given routing suggestions through RAGi's AI document processing feature. The system judges the likely responsible unit and urgency level based on content, for the document-routing staff to confirm before sending, moving repetitive reading and categorization work upstream. How much manual time this actually saves depends on how consistent the document formats are, the proportion of scanned documents, and how fine-grained the classification scheme is; it's recommended to run a one-month pilot on actual document volume before evaluating.

How to measure impact: define a baseline before adoption

When an agency adopts a sentiment monitoring or AI document system, the most common problem is not having a comparable baseline at the acceptance stage. It's recommended to write the following measurement definitions into the requirements specification before project kickoff:

Measurement dimension Recommended metric Common pitfall
Crisis Sentiment Response Time Select all negative events within a fixed period and compare the time of the system's first alert against the time the agency would otherwise have found out Selecting only success cases overestimates the effect; the number of missed detections and false positives should be counted as well
Official Document Processing Efficiency Separate the time into three stages — intake logging, interpretation and classification, and case-officer handling — since only the first two stages are where AI can plausibly have an effect Including the case officer's substantive processing time in the savings figure makes the number impossible to reproduce
Cross-department queries Time a fixed set of test questions, while also recording answer accuracy and whether the correct regulatory source is cited Comparing speed alone without accuracy risks mistaking a faster wrong answer for an improvement
Public Sentiment Coverage Breadth Record the types and counts of sources covered, and flag any gap periods caused by platform redesigns Using the total count alone as a proxy for representativeness ignores differences in the online population structure and the underlying population
Sentiment interpretation quality Have agency staff sample-annotate a batch of posts as a reference set, and calculate the agreement rate with the system's interpretation Adopting the vendor-provided accuracy figure as-is, without confirming the test dataset or the labeling criteria

Expected benefit

  • Public sentiment monitoring transitions from periodic sampling to persistent observation：Traditional polling is a sample taken at a specific point in time, while online sentiment monitoring provides a continuous trend of discussion; the two complement each other rather than substitute for one another.
  • Crisis response workflows shifted upstream：Early alerts empower spokesperson teams to activate communications during initial negative sentiment diffusion; exact lead-time gains should be benchmarked against historical agency incidents.
  • Reduced document-interpretation workload：AI-generated summaries and classification suggestions reduce repetitive reading; the actual benefit depends on how consistent the document formats are and the proportion of scanned files.
  • Cross-department queries become traceable：A unified knowledge repository establishes a shared single source of truth for statutory queries and SOP checks, logging query audits for post-incident review.
  • Data stays within the agency's server room：On-premise deployment can keep sensitive data from being sent to an external cloud, but this still needs to be paired with controls such as access control, encryption, and audit trails, and verified by the security team.

Regulatory and procurement considerations

When an agency adopts an AI system, it typically needs to address three areas together: cybersecurity, personal data protection, and outsourcing management. Common items that need to be confirmed include: the cybersecurity responsibility level (A through E) the agency has been rated at under the Cyber Security Management Act, and the corresponding required controls,Personal Data Protection Act on the provisions governing the collection, processing, and use of personal data, and the reminder in the Executive Yuan's 2023 Reference Guidelines for the Use of Generative AI by the Executive Yuan and Subordinate Agencies regarding the scope of generative AI use. It's recommended that, at the outset of the project, the IT, ethics/integrity, and legal affairs units be brought in together to confirm data classification and grading, retention periods, and where responsibility lies for managing outsourced personnel.

The actual scope of application and operational requirements are still subject to the competent authority's latest announcements and the determination of your agency's (or company's) legal counsel.

FAQ

InfoMiner strictly ingests and analyzes publicly published web data, never accessing private communications or non-public data. The system analyzes macro public discourse trends and thematic trajectories rather than profiling individual citizens. Nonetheless, we recommend agencies confirm collection purposes, data retention schedules, and de-identification parameters prior to deployment to support privacy compliance. Statutory compliance scopes remain subject to official regulatory announcements and institutional legal determinations.
Yes. We recommend setting keywords based on the issues your agency actually cares about and running a trial observation lasting several weeks, recording the number of alerts, false positives, and missed detections during the trial period as a basis for procurement evaluation. Most public-sector projects are subject to confidentiality agreements, so case-specific details cannot be disclosed, but we welcome a consultation where you describe your agency's situation so our team can provide a corresponding demonstration and pilot plan.
We can accommodate the common procurement workflows in government purchasing, including open tender, price comparison, negotiated procurement, and procurement through a Joint Supply Contract. We can provide a draft technical specification and service proposal to help the agency draft its requirements and evaluation criteria. Please contact us to confirm which procurement methods, qualifications, and items actually apply to your case, and refer to the Government e-Procurement Platform and the host agency's announcements as the authoritative source.
For RAGi on-premise deployment paired with the QubicX platform, hardware requirements depend on the scale of use, mainly the model size, the number of concurrent users, the volume of knowledge base documents, and the acceptable response latency. Small-scale deployments can start with a single GPU-equipped server, while large-scale deployments can be expanded to multiple nodes. We recommend running a small-scale performance validation first, using the agency's actual documents and typical questions, before finalizing the hardware configuration.
No programming background is required. InfoMiner provides a web interface, so government staff can start using it simply by setting monitoring keywords; RAGi is operated through natural-language conversation. In practice, we still recommend arranging training on keyword strategy and questioning technique, since how thorough the keyword setup is directly affects the monitoring coverage and noise level. LargitData provides training courses and an operating manual.
We recommend requiring written answers directly in the requirements specification on: the list of data sources and their update frequency, the deduplication rules for reposted news, how far back historical data can be traced, the notification and remediation process when a platform redesign or block causes a data gap, the evaluation method for sentiment analysis and whether error cases can be reviewed, whether alert thresholds can be adjusted per topic, and which fields and retention period the audit trail preserves. These items reflect the actual quality of real-world implementation far better than a feature checklist.

Want to learn more about AI solutions for government agencies?

Contact our expert team to design the most suitable public opinion analysis and smart government solution tailored to your needs.

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