NCAA Digital Transformation - Nasera AI: Digital Information and Knowledge System — Software Requirements Specification (SRS)
Table of Contents
- 1 Document Information
- 2 Project Overview
- 3 User Requirements
- 4 Technical Requirements
- 5 External Dependencies
- 6 Release Planning
- 7 Risks Assumptions
- 8 Market Specific Considerations
- 9 Sign Off
- 10 Detailed Feature Requirements
- 11 Additional Context
1 Document Information
| Field | Value |
|---|---|
| Project Name | NCAA Digital Transformation - Nasera AI: Digital Information and Knowledge System |
| Version | 1.0 |
| Date | 2025-11-12 |
| Project Manager | TBD |
| Tech Lead | TBD |
| Qa Lead | TBD |
| Platforms | ['Web', 'Mobile', 'API Services', 'AI/ML Infrastructure'] |
| Document Status | Draft |
| Module Code | NASERA_AI |
| Parent Project | NCAA Digital Transformation - Ngorongoro Gateway System |
2 Project Overview
2.1 What Are We Building
2.1.1 System Function
Nasera AI is NCAA's centralized, AI-driven knowledge and information management platform serving as both a digital information hub and an intelligent operational assistant. The system consolidates data from all NCAA departments (Tourism, Finance, Conservation, ICT, Human Resources, Operations) to train specialized AI and ML models that continuously improve accuracy, responsiveness, and relevance. By leveraging Natural Language Processing (NLP) and Large Language Models (LLMs) connected through secured APIs across all NCAA systems, Nasera AI delivers instant answers, predictive insights, and data-driven support to both internal staff and external stakeholders including tourists and tour operators.
2.1.2 Users
- Tourists: Information about NCAA, permits, facilities, wildlife, and regulations via multilingual interface
- Tour Operators: Booking information, permit requirements, operational guidelines, and real-time updates
- NCAA Staff: Operational assistance, data queries, document retrieval, and decision support across all departments
- Management: Predictive analytics, performance insights, and strategic recommendations
- Conservation Officers: Wildlife data analysis, threat intelligence, and research support
- Finance Team: Revenue forecasting, budget analysis, and financial insights
- ICT Team: System monitoring, performance analytics, and technical documentation
2.1.3 Problem Solved
Fragmented institutional knowledge scattered across departments, inconsistent information provided to tourists and operators, time-consuming manual information searches, lack of predictive capabilities for planning, inability to answer complex questions requiring cross-departmental data, language barriers limiting accessibility (English-only systems), and absence of intelligent assistance for data-driven decision-making across the organization.
2.1.4 Key Success Metric
95% accuracy in answering factual questions about NCAA operations, <2 second response time for standard queries, multilingual support (English, Swahili, French, German, Chinese), 80% reduction in manual information requests, predictive analytics with 80%+ forecast accuracy, complete integration with all 10+ NCAA digital systems, natural language interface requiring no technical training, and 24/7 availability for tourists and staff.
2.2 Scope
2.2.1 In Scope
- Centralized knowledge base covering all NCAA departments and operations
- Natural Language Processing (NLP) for question understanding and intent recognition
- Large Language Model (LLM) integration for conversational AI capabilities
- Multilingual support (English, Swahili, French, German, Chinese)
- Web-based conversational interface for tourists and operators
- Mobile app integration for on-the-go queries
- SMS-based query interface for low-connectivity users
- Staff portal for advanced queries and analytics
- API integration with all NCAA systems (Gateway, Mobile, Fleet, Surveillance, BI, Finance, HR)
- Predictive analytics for visitor trends, revenue forecasting, resource allocation
- Machine learning models trained on NCAA-specific data
- Automated content curation and knowledge base updates
- Performance monitoring and accuracy improvement systems
- Administrator interface for content management and model training
- Analytics dashboard for query patterns and system performance
- Document retrieval and summarization from NCAA document repositories
2.2.2 Out Of Scope
- Booking and payment processing (handled by Mobile App and Gateway)
- Real-time vehicle tracking (handled by Fleet Management)
- Live camera feeds (handled by Surveillance System)
- Transactional operations (Nasera AI provides information and insights only)
- Human resources management functions (HR system integration only)
- Financial transaction processing (Finance system integration only)
- Content creation (Nasera AI curates and presents existing content)
- Legal advice or official policy interpretation (provides information only)
3 User Requirements
3.1 Conversational Interface
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-NASERA-CHAT-NL | Ask questions in natural language (conversational style) | Get information without learning technical query syntax | Must | Supports colloquial language, typos, and incomplete questions. Context-aware multi-turn conversations. Remembers conversation history within session. |
| FT-NASERA-CHAT-MULTILANG | Interact with Nasera AI in my preferred language | Access information regardless of language barriers | Must | Priority languages: English, Swahili (primary). Secondary: French, German, Chinese. Automatic language detection. Translation quality 95%+ for primary languages. |
| FT-NASERA-CHAT-CONTEXT | Have multi-turn conversations where AI remembers context | Ask follow-up questions without repeating information | Must | Maintains conversation context for session duration. References previous questions and answers. Clarifies ambiguous queries based on context. |
| FT-NASERA-CHAT-VOICE | Use voice input for queries via mobile app | Ask questions hands-free while traveling or in the field | Should | Speech-to-text integration. Multilingual voice recognition. Works offline with on-device processing for basic queries. |
3.2 Knowledge Base
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-NASERA-KB-COMPREHENSIVE | Access comprehensive information about NCAA operations, policies, and services | Find accurate answers to any NCAA-related question | Must | Covers: tariffs, regulations, wildlife, conservation, tourism, facilities, history, geography, safety, permits, bookings, accommodation, transportation, accessibility. |
| FT-NASERA-KB-REALTIME | Receive real-time information synchronized from operational systems | Get current data on availability, capacity, incidents, and operations | Must | API integration with Gateway (capacity), Fleet (availability), Surveillance (incidents), BI (analytics). Data freshness <5 minutes for operational queries. |
| FT-NASERA-KB-STRUCTURED | Access structured data from NCAA databases alongside unstructured documents | Get precise answers from policies, reports, and operational data | Must | Integrates structured data (databases) and unstructured (documents, PDFs, websites). Hybrid retrieval combining keyword and semantic search. |
| FT-NASERA-KB-UPDATES | Ensure knowledge base is automatically updated as systems change | Trust that information is current and accurate | Must | Automated sync from source systems. Version control for policy documents. Change detection and notification. Manual review for critical updates. |
3.3 Predictive Analytics
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-NASERA-PREDICT-VISITORS | Forecast visitor numbers for upcoming periods | Plan staffing, resources, and capacity management proactively | Must | Daily, weekly, monthly forecasts. Seasonal pattern recognition. Event-based adjustments (holidays, festivals). 80%+ accuracy target for 7-day forecasts. |
| FT-NASERA-PREDICT-REVENUE | Forecast revenue by category (permits, fees, services) | Support budget planning and financial decision-making | Must | Revenue forecasting by category and gate. Monthly, quarterly, annual projections. Confidence intervals provided. Integration with Finance systems. |
| FT-NASERA-PREDICT-MAINTENANCE | Predict equipment maintenance needs before failures occur | Schedule preventive maintenance and reduce downtime | Must | Fleet Management integration for vehicle predictions. Infrastructure monitoring for facilities. Risk scoring and prioritization. 30-day advance predictions. |
| FT-NASERA-PREDICT-THREATS | Predict security threat hotspots and high-risk periods | Deploy security resources proactively | Must | Surveillance System integration. Historical incident analysis. Environmental factor correlation. Geographic and temporal predictions. Daily updates. |
| FT-NASERA-PREDICT-OPTIMIZATION | Receive optimization recommendations for operations | Improve efficiency and reduce costs based on data-driven insights | Should | Resource allocation recommendations. Route optimization suggestions. Staffing optimization. Cost reduction opportunities. Prioritized by impact. |
3.4 Departmental Integration
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-NASERA-INT-GATEWAY | Query real-time gate operations data (capacity, permits, vehicles) | Provide current operational status to stakeholders | Must | API integration with Gateway System. Current capacity by gate. Permit status queries. Vehicle entry/exit data. <5 minute data freshness. |
| FT-NASERA-INT-MOBILE | Access booking and user data from Mobile App | Answer questions about reservations and user accounts | Must | Booking status queries. Permit retrieval. User account information (privacy-protected). Upcoming reservations. Payment status (not processing). |
| FT-NASERA-INT-FLEET | Query fleet data for vehicle availability and performance | Provide transportation information and support fleet optimization | Must | Vehicle availability queries. Maintenance schedules. Fuel efficiency data. Route optimization input. Driver performance (privacy-protected). |
| FT-NASERA-INT-SURVEILLANCE | Access surveillance data for security insights and threat intelligence | Support security decision-making with AI-powered analysis | Must | Incident data access (security-cleared users only). Threat pattern analysis. Hotspot predictions. Security analytics. Alert correlation. |
| FT-NASERA-INT-BI | Query BI System data for analytics and reporting | Answer complex questions requiring cross-departmental data analysis | Must | Bidirectional integration - queries BI and feeds AI insights back. Complex analytics queries. Historical trend analysis. Executive reporting data. |
| FT-NASERA-INT-FINANCE | Access financial data for budget and revenue queries | Support financial planning and reporting with accurate data | Should | Revenue by category and period. Budget utilization. Cost analysis. Financial forecasts. Role-based access control for sensitive data. |
| FT-NASERA-INT-HR | Query HR data for staffing and performance information | Support HR planning and employee information requests | Should | Staff directory (public information only). Staffing levels by department. Leave schedules (privacy-protected). Performance metrics (aggregated). Training records. |
3.5 Staff Portal
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-NASERA-STAFF-ADVANCED | Access advanced analytics and complex queries beyond tourist information | Perform my job more effectively with AI-powered insights | Must | Staff-only portal with authentication. Advanced query capabilities. Data visualization. Report generation. Export functionality. Role-based access. |
| FT-NASERA-STAFF-DOCS | Search and retrieve documents from NCAA repositories | Find policies, procedures, and reports quickly without manual searching | Must | Document search across file servers and SharePoint. Semantic search (meaning-based, not just keywords). Document summarization. Version awareness. |
| FT-NASERA-STAFF-INSIGHTS | Receive proactive insights and recommendations relevant to my role | Stay informed of important trends and optimization opportunities | Should | Role-based insight generation. Daily briefings. Anomaly alerts. Recommendation engine. Configurable preferences. Email or app notifications. |
| FT-NASERA-STAFF-ANALYSIS | Perform ad-hoc data analysis using natural language queries | Answer business questions without technical SQL or data science skills | Should | Natural language to SQL translation. Data visualization generation. Statistical analysis. Trend identification. Export results to Excel/PDF. |
3.6 Tourist Portal
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-NASERA-TOURIST-INFO | Ask questions about visiting NCAA in multiple languages | Plan my visit with accurate, easy-to-understand information | Must | Conversational interface on NCAA website and mobile app. Common topics: permits, fees, rules, wildlife, safety, best times to visit, what to bring, accommodation. |
| FT-NASERA-TOURIST-REALTIME | Get real-time information on capacity and conditions | Make informed decisions about when and where to visit | Must | Current capacity levels. Weather conditions. Road status. Facility availability. Wildlife sighting reports (recent). Gate wait times. |
| FT-NASERA-TOURIST-RECOMMENDATIONS | Receive personalized recommendations based on my interests and constraints | Have a better experience tailored to my preferences | Should | Recommendation engine based on: interests (wildlife, culture, photography), time available, season, fitness level, budget. Suggests routes, activities, timing. |
| FT-NASERA-TOURIST-SMS | Query Nasera AI via SMS when I have no internet access | Get essential information even in low-connectivity areas | Should | SMS gateway integration. Keyword-based queries. Simple responses (character-limited). Common queries: capacity, rules, emergency contacts. English and Swahili. |
3.7 Administration
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-NASERA-ADMIN-CONTENT | Manage knowledge base content and approve updates | Ensure information accuracy and quality control | Must | Content management interface. Approval workflows for critical updates. Version control. Content categorization and tagging. Search and filter functionality. |
| FT-NASERA-ADMIN-TRAINING | Train and fine-tune AI models using NCAA-specific data | Improve accuracy and relevance for NCAA use cases | Must | Model training interface. Training data curation. Evaluation metrics. A/B testing capability. Model version management. Rollback functionality. |
| FT-NASERA-ADMIN-ANALYTICS | Monitor system performance, query patterns, and user satisfaction | Identify issues and improvement opportunities | Must | Analytics dashboard: query volume, response times, accuracy metrics, user feedback, popular topics, failed queries, system health. Daily/weekly reports. |
| FT-NASERA-ADMIN-FEEDBACK | Review user feedback and incorrect responses | Continuously improve system accuracy | Must | User feedback collection (thumbs up/down, comments). Incorrect response flagging. Review queue for administrators. Feedback-driven training. |
3.8 Performance Reliability
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-NASERA-PERF-RESPONSE | Receive responses to standard queries in under 2 seconds | Have a smooth, real-time conversational experience | Must | Response time <2 seconds for 95% of queries. Complex analytics queries <10 seconds. Caching for common queries. Progressive response for long answers. |
| FT-NASERA-PERF-AVAILABILITY | Access Nasera AI 24/7 with minimal downtime | Get information whenever needed, regardless of time zone | Must | 99.5% uptime target. Redundant infrastructure. Graceful degradation (basic functionality if advanced features unavailable). Scheduled maintenance windows. |
| FT-NASERA-PERF-SCALE | Use the system even during high-traffic periods (peak tourist season) | Rely on Nasera AI regardless of concurrent users | Must | Auto-scaling infrastructure. Load testing for 500+ concurrent users. Queue management for complex queries. Rate limiting to prevent abuse. |
| FT-NASERA-PERF-ACCURACY | Trust that responses are accurate and cite sources when applicable | Rely on information for decision-making | Must | 95%+ accuracy for factual queries. Source citation for policies and official information. Confidence scoring. 'I don't know' responses when uncertain. |
4 Technical Requirements
4.1 Performance Standards
| Requirement | Target | How To Test |
|---|---|---|
| Query response time | < 2 seconds for 95% of standard queries, < 10 seconds for complex analytics | Performance testing with representative query mix, load testing with concurrent users |
| Prediction accuracy | ≥ 80% accuracy for visitor forecasts (7-day), ≥ 85% for revenue forecasts (monthly) | Backtesting with historical data, ongoing validation against actuals |
| Question answering accuracy | ≥ 95% accuracy for factual queries about NCAA operations | Human evaluation on curated test set, user feedback tracking |
| System availability | 99.5% uptime | Uptime monitoring over 90-day periods |
| Translation quality | ≥ 95% for English-Swahili, ≥ 90% for secondary languages | Human evaluation by native speakers, BLEU score benchmarking |
| Concurrent user support | 500+ concurrent users without performance degradation | Load testing with simulated concurrent users |
| API integration latency | < 500ms for API calls to integrated systems | Integration testing with latency monitoring |
4.2 Platform Requirements
| Platform | Minimum Version | Target Version | Notes |
|---|---|---|---|
| LLM Foundation | GPT-3.5 equivalent or open-source alternative (LLaMA 2 70B) | GPT-4 equivalent or state-of-the-art open-source model | Self-hosted option preferred for data privacy, cloud API fallback acceptable |
| NLP Pipeline | spaCy 3.0+ or NLTK 3.6+ | Latest stable versions with custom NCAA models | Multilingual support essential, domain adaptation for conservation/tourism |
| ML Framework | TensorFlow 2.8+ or PyTorch 1.12+ | Latest stable versions | GPU support for training, CPU inference acceptable for deployment |
| Vector Database | Pinecone, Weaviate, or Milvus | Latest stable with semantic search capabilities | For knowledge base embedding storage and retrieval |
| Backend Infrastructure | Python 3.9+, FastAPI or Flask | Python 3.11+, FastAPI with async support | Containerized deployment (Docker), Kubernetes for orchestration |
| Database | PostgreSQL 13+ for structured data | PostgreSQL 15+ with pgvector extension | Conversation history, user profiles, analytics storage |
4.3 Security Privacy
| Requirement | Must Have | Implementation |
|---|---|---|
| Data privacy | True | No storage of personally identifiable information without consent, anonymized analytics, conversation history opt-in, GDPR-style data subject rights |
| Authentication & authorization | True | OAuth 2.0 for staff portal, role-based access control (RBAC), anonymous access for tourist queries, API key authentication for system integrations |
| Data encryption | True | TLS 1.3 for all communications, AES-256 for data at rest, encrypted API keys, secure credential storage |
| Audit logging | True | Comprehensive logging of queries (anonymized), system access, administrative actions, API calls, compliance with NCAA audit requirements |
| Model security | True | Protection against prompt injection attacks, output filtering for inappropriate content, rate limiting, abuse detection |
5 External Dependencies
5.1 Third Party Services
| Service | What It Does | Criticality | Backup Plan |
|---|---|---|---|
| LLM API Service | Provide large language model capabilities if not self-hosted | High | Self-hosted open-source LLM (LLaMA, Falcon) for data privacy and cost control |
| Translation Service | Provide machine translation for secondary languages | Medium | Open-source translation models (NLLB, Helsinki NLP) |
| Speech-to-Text API | Convert voice queries to text for voice interface | Low | On-device speech recognition for basic functionality |
| SMS Gateway | Enable SMS-based queries and responses | Medium | Web and app interfaces only (SMS nice-to-have) |
5.2 Internal Dependencies
| System | Integration Type | Data Exchanged | Criticality |
|---|---|---|---|
| Gateway System | RESTful API | Capacity, permits, vehicle entries, operational status | High |
| Mobile Application | RESTful API + embedded chat widget | Bookings, user data, permit status | High |
| Fleet Management | RESTful API | Vehicle availability, performance, maintenance data | High |
| Surveillance System | RESTful API | Incident data, threat intelligence (security-cleared only) | High |
| BI System | Bidirectional RESTful API + direct database access | All analytics data, historical trends, predictions | High |
| Finance System | RESTful API | Revenue, costs, budgets (role-based access) | Medium |
| HR System | RESTful API | Staff directory, staffing levels (privacy-protected) | Medium |
6 Release Planning
6.1 Development Phases
| Phase | Features Included | Timeline | Success Criteria |
|---|---|---|---|
| Phase 1 (Core Knowledge Base & Tourist Interface - MVP) | ['Knowledge base for tourist information (permits, fees, rules, wildlife)', 'Web-based chat interface (English and Swahili)', 'Basic NLP for question understanding', 'Integration with Gateway for real-time capacity', 'Simple admin interface for content management', 'Tourist-facing website and mobile app integration'] | 16 weeks | Tourist queries answered with 90%+ accuracy, <3 second response time, English and Swahili fully functional, real-time capacity data available |
| Phase 2 (Staff Portal & System Integration) | ['Staff-only portal with authentication', 'Advanced queries and analytics', 'Integration with Fleet, Surveillance, BI systems', 'Document search and retrieval', 'Multilingual expansion (French, German, Chinese)', 'Voice interface (mobile app)', 'Admin analytics dashboard'] | 16 weeks | All system integrations operational, staff portal adoption by 80%+ of users, multilingual support tested and functional, document retrieval working |
| Phase 3 (Predictive Analytics & AI Enhancement) | ['Visitor forecasting models', 'Revenue prediction', 'Threat hotspot prediction (Surveillance integration)', 'Maintenance prediction (Fleet integration)', 'Optimization recommendations', 'Proactive insights for staff', 'SMS interface', 'Performance optimization'] | 16 weeks | Predictive models achieving 80%+ accuracy, optimization recommendations generating measurable improvements, SMS interface functional, system optimized for scale |
6.2 Release Checklist
- Knowledge base populated with comprehensive NCAA information
- All Must-Have features implemented and tested
- LLM foundation deployed (self-hosted or API)
- Vector database operational with embedded knowledge
- NLP pipeline trained on NCAA-specific data
- Web chat interface deployed on NCAA website
- Mobile app integration complete
- Staff portal operational with RBAC
- All system integrations tested and functional
- Multilingual support validated by native speakers
- Predictive models trained and validated
- Admin interface complete with analytics
- Performance testing passed (500+ concurrent users)
- Security audit completed
- Privacy compliance verified (data protection policies)
- User acceptance testing completed (staff and tourist pilot groups)
- Documentation complete (user guides, API docs, admin manuals)
- Training materials prepared for administrators
- Monitoring and alerting configured
- Backup and disaster recovery tested
7 Risks Assumptions
7.1 Risks
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| LLM hallucinations providing incorrect information to users | Medium | High | Retrieval-augmented generation (RAG) architecture grounding responses in knowledge base, confidence scoring, human review of high-stakes responses, user feedback mechanism, regular accuracy audits |
| Insufficient training data for NCAA-specific domain | Medium | Medium | Data curation from all NCAA systems and documents, synthetic data generation, continuous learning from user interactions, partnership with NCAA experts for validation |
| API integration failures affecting real-time data availability | Low | Medium | Graceful degradation to cached data, clear messaging when real-time data unavailable, redundant API endpoints, comprehensive error handling, fallback data sources |
| Translation quality issues for secondary languages | Medium | Low | Professional translation review for common queries, user feedback on translation quality, progressive rollout of secondary languages, fallback to English with notification |
| Performance issues during peak tourist season | Low | Medium | Auto-scaling infrastructure, load testing before peak season, caching strategies, query queue management, performance monitoring with automated scaling triggers |
| User adoption lower than expected (staff resistance) | Medium | Medium | Change management program, early staff involvement in design, champion identification, training and support, demonstrating time savings, iterative improvements based on feedback |
| Data privacy concerns limiting data access for training | Low | Medium | Clear data governance policies, anonymization techniques, role-based data access, compliance with Tanzania Data Protection Act, privacy-preserving ML techniques |
7.2 Assumptions
- All NCAA systems will provide APIs for data integration
- Sufficient historical data available for training predictive models
- Internet connectivity adequate for cloud-based LLM (if not self-hosted)
- Staff willing to adopt AI-powered tools with appropriate training
- Tourists will find conversational interface intuitive and useful
- Translation services adequate for conservation/tourism domain
- NCAA management committed to data-driven decision-making
- Budget available for LLM API costs or self-hosting infrastructure
- Multilingual content can be sourced or translated accurately
- User feedback will be provided to improve system accuracy
8 Market Specific Considerations
8.1 Primary Market
- Ngorongoro Conservation Area, Tanzania - tourists, tour operators, NCAA staff
8.2 Target Demographics
- International tourists (varying technical literacy and languages)
- Tour operators (need efficient access to operational information)
- NCAA staff (diverse roles from rangers to executives)
8.3 Local Considerations
- Multilingual support essential (English, Swahili mandatory; French, German, Chinese important for tourists)
- Low connectivity in conservation area requiring offline capabilities for mobile app
- Cultural sensitivity in language and content (conservation values, Maasai heritage)
- Tanzania Data Protection Act compliance for handling user data
- Local hosting preferred for data sovereignty and latency
- SMS interface important for low-connectivity scenarios
- Integration with national tourism databases for holistic information
- Seasonal patterns (tourist seasons) requiring scalable infrastructure
- Currency and measurement unit localization (TSh vs USD, metric system)
- Time zone considerations (EAT - East Africa Time)
8.4 Conservation Context
8.4.1 Unesco Heritage
Information accuracy critical for World Heritage Site status
8.4.2 Wildlife Protection
Support for anti-poaching through threat intelligence
8.4.3 Community Engagement
Accessible information supporting community relations
8.4.4 Research Support
Data analysis capabilities supporting conservation research
9 Sign Off
9.1 Approval
| Role | Name | Signature | Date |
|---|---|---|---|
9.2 Document History
| Version | Date | Changes Made | Changed By |
|---|---|---|---|
| 1.0 | 2025-11-12 | Initial draft based on NCAA Digital Transformation roadmap Section 2.5 | SRS Development Team |
10 Detailed Feature Requirements
10.1 Ft Nasera Chat Nl
10.1.1 Priority
Must Have
10.1.2 User Story
As a user (tourist or staff), I want to ask questions in natural language so that I can get information without learning technical query syntax
10.1.3 Preconditions
NLP pipeline operational; LLM deployed; Knowledge base populated
10.1.4 Postconditions
Query understood; Relevant response generated; Conversation context maintained
10.1.5 Test Cases
| Id | Description | Weight |
|---|---|---|
| NASERA-CHAT-TC-001 | Answer simple factual question (e.g., 'What are the entry fees?') | High |
| NASERA-CHAT-TC-002 | Handle colloquial language (e.g., 'How much to get in?') | High |
| NASERA-CHAT-TC-003 | Understand questions with typos and grammatical errors | Medium |
| NASERA-CHAT-TC-004 | Maintain context across multi-turn conversation (3-5 exchanges) | High |
| NASERA-CHAT-TC-005 | Clarify ambiguous questions before answering | Medium |
10.2 Ft Nasera Chat Multilang
10.2.1 Priority
Must Have
10.2.2 User Story
As an international tourist, I want to interact with Nasera AI in my preferred language so that I can access information without language barriers
10.2.3 Preconditions
Translation models deployed; Multilingual knowledge base; Language detection operational
10.2.4 Postconditions
Query answered in user's language; Translation quality high; Language maintained throughout conversation
10.2.5 Test Cases
| Id | Description | Weight |
|---|---|---|
| NASERA-LANG-TC-001 | Automatically detect language from query (English, Swahili, French, German, Chinese) | High |
| NASERA-LANG-TC-002 | Answer in same language as query with 95%+ translation quality (English/Swahili) | High |
| NASERA-LANG-TC-003 | Answer in same language as query with 90%+ translation quality (secondary languages) | High |
| NASERA-LANG-TC-004 | Allow user to manually switch language mid-conversation | Medium |
| NASERA-LANG-TC-005 | Handle domain-specific terms (conservation, wildlife) accurately across languages | High |
10.3 Ft Nasera Kb Realtime
10.3.1 Priority
Must Have
10.3.2 User Story
As a user, I want to receive real-time information synchronized from operational systems so that I get current data on availability, capacity, and operations
10.3.3 Preconditions
API integrations operational; Data sync pipeline running; Knowledge base updated
10.3.4 Postconditions
Real-time data retrieved; Response includes current information; Data freshness indicated
10.3.5 Test Cases
| Id | Description | Weight |
|---|---|---|
| NASERA-KB-TC-001 | Query Gateway for current capacity and receive data <5 minutes old | High |
| NASERA-KB-TC-002 | Query Fleet for vehicle availability and receive real-time status | High |
| NASERA-KB-TC-003 | Query Surveillance for recent incidents (security-cleared users) | Medium |
| NASERA-KB-TC-004 | Indicate data freshness in response (e.g., 'as of 2 minutes ago') | Medium |
| NASERA-KB-TC-005 | Gracefully handle API failures with cached data and clear notification | High |
10.4 Ft Nasera Predict Visitors
10.4.1 Priority
Must Have
10.4.2 User Story
As an operations manager, I want to forecast visitor numbers for upcoming periods so that I can plan staffing and resources proactively
10.4.3 Preconditions
Historical visitor data available (2+ years); ML models trained; Seasonal patterns identified
10.4.4 Postconditions
Forecast generated; Confidence intervals provided; Predictions updated daily
10.4.5 Test Cases
| Id | Description | Weight |
|---|---|---|
| NASERA-PRED-TC-001 | Generate 7-day visitor forecast with 80%+ accuracy | High |
| NASERA-PRED-TC-002 | Generate monthly visitor forecast with 75%+ accuracy | High |
| NASERA-PRED-TC-003 | Include confidence intervals for all predictions | Medium |
| NASERA-PRED-TC-004 | Adjust for seasonal patterns (high/low tourist seasons) | High |
| NASERA-PRED-TC-005 | Account for holidays and special events | Medium |
| NASERA-PRED-TC-006 | Update forecasts daily with latest actual data | High |
10.5 Ft Nasera Int Gateway
10.5.1 Priority
Must Have
10.5.2 User Story
As Nasera AI, I want to query real-time gate operations data so that I can provide current operational status to stakeholders
10.5.3 Preconditions
Gateway API accessible; Authentication configured; Data mapping established
10.5.4 Postconditions
Gateway data retrieved; Response includes current capacity/permits/vehicles; API latency <500ms
10.5.5 Test Cases
| Id | Description | Weight |
|---|---|---|
| NASERA-INT-TC-001 | Query current capacity by gate via Gateway API | High |
| NASERA-INT-TC-002 | Retrieve permit status for specific permit ID | High |
| NASERA-INT-TC-003 | Get vehicle entry/exit data for today | Medium |
| NASERA-INT-TC-004 | API response time <500ms | High |
| NASERA-INT-TC-005 | Handle API errors gracefully with user-friendly messages | High |
10.6 Ft Nasera Staff Advanced
10.6.1 Priority
Must Have
10.6.2 User Story
As a staff member, I want to access advanced analytics and complex queries so that I can perform my job more effectively with AI-powered insights
10.6.3 Preconditions
Staff portal deployed; Authentication and RBAC operational; Advanced query engine ready
10.6.4 Postconditions
Staff authenticated; Advanced queries answered; Visualizations generated; Data exportable
10.6.5 Test Cases
| Id | Description | Weight |
|---|---|---|
| NASERA-STAFF-TC-001 | Authenticate staff user and enforce role-based access | High |
| NASERA-STAFF-TC-002 | Answer complex cross-departmental query (e.g., 'visitor trends vs revenue by month') | High |
| NASERA-STAFF-TC-003 | Generate data visualization (chart/graph) for query result | Medium |
| NASERA-STAFF-TC-004 | Export query results to Excel or PDF | Medium |
| NASERA-STAFF-TC-005 | Restrict sensitive data based on user role | High |
10.7 Ft Nasera Admin Training
10.7.1 Priority
Must Have
10.7.2 User Story
As an administrator, I want to train and fine-tune AI models using NCAA-specific data so that I can improve accuracy and relevance
10.7.3 Preconditions
Training data curated; ML infrastructure available; Admin interface operational
10.7.4 Postconditions
Model trained; Performance metrics evaluated; New model deployed or rolled back
10.7.5 Test Cases
| Id | Description | Weight |
|---|---|---|
| NASERA-ADMIN-TC-001 | Upload training data and initiate model training | High |
| NASERA-ADMIN-TC-002 | Monitor training progress and view metrics | Medium |
| NASERA-ADMIN-TC-003 | Evaluate model performance on test set | High |
| NASERA-ADMIN-TC-004 | Deploy new model version to production | High |
| NASERA-ADMIN-TC-005 | Rollback to previous model version if performance degrades | High |
11 Additional Context
11.1 Success Metrics
11.1.1 Query Accuracy
95%+ for factual queries (measured by user feedback and expert evaluation)
11.1.2 Response Time
<2 seconds for 95% of queries, <10 seconds for complex analytics
11.1.3 User Satisfaction
80%+ positive feedback ratings from users
11.1.4 Adoption Rate
80%+ of staff actively using staff portal within 6 months
11.1.5 Tourist Usage
50%+ of website visitors engaging with chat interface
11.1.6 Prediction Accuracy
80%+ for visitor forecasts (7-day), 85%+ for revenue (monthly)
11.1.7 System Availability
99.5% uptime
11.1.8 Multilingual Quality
95%+ translation quality for English/Swahili, 90%+ for secondary languages
11.1.9 Time Savings
80% reduction in manual information requests to staff
11.2 Architecture Overview
11.2.1 Knowledge Base Layer
Vector database storing embedded NCAA knowledge from documents, databases, and systems
11.2.2 Llm Layer
Large language model (self-hosted or API) for natural language understanding and generation
11.2.3 Npl Pipeline
Intent recognition, entity extraction, context management, multilingual processing
11.2.4 Integration Layer
API connectors to all NCAA systems for real-time data retrieval
11.2.5 Ml Models
Predictive models for forecasting (visitors, revenue, maintenance, threats)
11.2.6 Presentation Layer
Web chat interface, mobile app integration, SMS gateway, staff portal
11.2.7 Admin Layer
Content management, model training, analytics dashboard, user feedback review
11.3 Ai Capabilities
11.3.1 Understanding
Natural language understanding, intent classification, entity extraction, context tracking
11.3.2 Generation
Natural language response generation, multilingual translation, text summarization
11.3.3 Retrieval
Semantic search, hybrid retrieval (keyword + embedding), source citation
11.3.4 Prediction
Time series forecasting, anomaly detection, pattern recognition, optimization
11.3.5 Reasoning
Multi-step reasoning for complex queries, cross-referencing multiple data sources