Authors: Geoffrey Duncan Opiyo, Hillary Arinda, Justine Okumu, Deo Mugabe

🚀GitHub Repository

1. Executive Summary

AskImmigrate 2.0 Pro is a multi-agent AI system designed to transform how immigration information is accessed and understood.
It combines retrieval-augmented generation, fee calculation, and user safeguards to provide fast, accurate, and reliable responses.
The platform is multilingual, user-centered, and built with performance optimizations that reduce average response times from 38 seconds to just 3–5 seconds.

By balancing technical robustness with practical usability, AskImmigrate 2.0 Pro addresses a critical need for accessible, accurate, and safe immigration technology.
Its architecture ensures transparency, resilience, and scalability, positioning it as a foundation for future innovation in the field.

For a live demonstration of AskImmigrate 2.0 Pro, visit:
🔗 https://ask-imi-ui-production.up.railway.app/

---

2. Problem Statement

Immigration procedures are complicated and time-consuming. Applicants face unclear requirements, shifting policies, and a lack of centralized guidance. Existing digital tools are either incomplete, outdated, or fail to address real-world scenarios.

This gap leaves individuals vulnerable to misinformation, delays, and costly errors. A reliable, intelligent, and adaptive system is needed to simplify the process and provide accurate support.

---

3. Objectives

The primary objective is to build a reliable, user-centered immigration assistance system. It must deliver accurate information, adapt to policy changes, and support multilingual users.

The system should reduce complexity, improve trust, and ensure safety in every interaction. By combining technical excellence with human-centered design, it will bridge the gap between immigration procedures and accessible digital guidance.

---

3.1 Code and Dependencies

• Languages: Python 3.10, TypeScript (frontend)
• Frameworks: FastAPI, React + Vite
• AI/ML: HuggingFace Transformers, SentenceTransformers, ChromaDB
• Runtime: Uvicorn + Gunicorn
• Containerization: Docker multi-stage builds
• Monitoring: Structured logging with correlation IDs, Prometheus hooks
• Deployment: Horizontal scaling with Docker + Load Balancer

---

3.2 Installation Guide

Prerequisites

To set up AskImmigrate 2.0 Pro locally, you will need the following:

• Python 3.10 or higher (tested with 3.11). Python is required for the backend services.
• Node.js version 18 or 20 (but not 21+). Node.js is needed only if you plan to run the optional React-based frontend.
• API Keys for LLM and search providers. At least one Large Language Model (LLM) provider key must be available (Gemini by default, or GROQ/OpenAI as alternatives). Additionally, a Tavily API key is required for web search functionality.

Having these tools installed ensures the backend can run smoothly and the frontend can render the user interface.

---

Step 1: Clone the Repository

git clone https://github.com/okumujustine/AskImmigrate2.0.git
cd AskImmigrate2.0

---

Step 2: Prepare Python Environment

python -m venv askimmigrate_env

Activate the environment:

• On Windows:

  askimmigrate_env\Scripts\activate

• On macOS/Linux:

  source askimmigrate_env/bin/activate

Upgrade pip:

python -m pip install --upgrade pip

---

Step 3: Install Dependencies

pip install -r requirements.txt

For development/testing:

pip install -r requirements-test.txt

---

Step 4: Configure Environment Variables

Create a .env file in the root directory:

# Required: Choose one LLM provider
GEMINI_API_KEY=your-gemini-api-key
# Alternative: GROQ_API_KEY=your-groq-api-key
# Alternative: OPENAI_API_KEY=your-openai-api-key

# Required: Web search provider
TAVILY_API_KEY=your-tavily-api-key

# Optional: Tracing with LangSmith
LANGSMITH_TRACING=true
LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
LANGSMITH_API_KEY=your-langsmith-key
LANGSMITH_PROJECT="AskImmigrate2.0"

---

Step 5: Initialize the Knowledge Base

python -m backend.code.embed_documents

---

Step 6: Install and Build the Frontend (Optional)

cd frontend
npm install
cd ..

---

Step 7: Run the Application

Start backend:

uvicorn backend.code.api:app --host 0.0.0.0 --port 8088

Start frontend:

cd frontend
npm run dev

Access via browser: http://localhost:5173

---

3.3 Testing and Validation

Reliability in a production-grade AI system cannot be assumed; it must be continuously validated. AskImmigrate 2.0 Pro includes a comprehensive testing suite designed to verify functionality at three levels: unit tests, integration tests, and end-to-end (E2E) tests. Together, these ensure that the system is robust, resilient, and ready for real-world use.

Unit Tests

Unit tests validate the smallest building blocks of the system in isolation. These tests focus on functions and utilities that, if incorrect, could compromise the entire workflow. Examples include:

• Session management and persistence – verifying that user sessions are correctly created and maintained.
• Input sanitization and validation – ensuring harmful or irrelevant queries are blocked before reaching agents.
• Caching utilities – confirming that embeddings are stored and reused efficiently, avoiding redundant computation.
• API response formatting – checking that structured responses are returned consistently.

By isolating these critical operations, unit tests prevent small defects from cascading into large system failures.

Integration Tests

Integration tests verify that individual modules work together as intended. In AskImmigrate 2.0 Pro, these tests simulate the cooperation of multiple agents, tools, and data sources. They validate scenarios such as:

• Queries flowing correctly through the Manager Node → RAG Retrieval Agent → Synthesis Node pipeline.
• Successful retrieval and embedding of documents within ChromaDB.
• Proper functioning of multilingual translation pipelines when paired with LLMs.
• Activation of fallback mechanisms when a primary LLM provider is unavailable.

These tests confirm that the system’s components remain interoperable as it evolves and scales.

End-to-End (E2E) Tests

E2E tests replicate real user interactions, validating the entire workflow from query submission to final response. Typical scenarios include:

• A user submitting a multilingual immigration-related query through the web interface and receiving a coherent answer in the same language.
• The backend streaming live progress updates via Server-Sent Events (SSE).
• Full query execution across safety checks, retrieval, synthesis, and review, ensuring consistency with production expectations.

E2E testing provides confidence that AskImmigrate 2.0 Pro behaves reliably under real-world conditions, not just in isolated modules.

Continuous Validation

Testing is not a one-time activity. The project integrates testing into its development and deployment lifecycle:

• During development – unit and integration tests are run locally as new features are introduced.
• Continuous Integration (CI) – automated pipelines execute the full suite on every pull request and merge.
• Coverage thresholds – critical components such as session management and RAG workflows must meet 85–90% code coverage, with system utilities meeting 75–80%.
• Monitoring in production – operational metrics like response times, error rates, and cache hit ratios are continuously checked against expectations, complementing formal tests with real-time validation.

---

Together, these layers of testing ensure that AskImmigrate 2.0 Pro is not only functional, but also resilient, secure, and performant. By validating individual components, their integrations, and full user scenarios, the system demonstrates the robustness necessary for production deployment in high-stakes domains like immigration guidance.

---

4. System Architecture

AskImmigrate 2.0 Pro is designed as a modular, multi-agent platform. Its architecture balances flexibility, scalability, and reliability.

Agents and Orchestration: Specialized agents handle retrieval, fee calculation, and task management. A manager node coordinates their collaboration to ensure consistency and accuracy.

Backend Services: A Python-based backend powers workflows, exposing system functionality through APIs for both internal and external use.

Frontend Interface: A React application provides an accessible user experience, offering multilingual support, responsive design, and seamless integration with backend services.

Data and Knowledge Layer: Documents are embedded and stored for fast retrieval. External APIs, such as Gemini, OpenAI, and Tavily, extend reasoning and domain knowledge.

Monitoring and Evaluation: Reliability and transparency are strengthened through structured logging, retry mechanisms, and integration with LangSmith. LangSmith enables tracing, evaluation, and debugging of agent interactions, ensuring observability throughout the system lifecycle.

This layered approach ensures the system can scale effectively while remaining transparent, resilient, and user-centered.

---

5. Key Features

---

6. Multilingual Implementation

AskImmigrate 2.0 Pro is built for a global audience, ensuring accessibility across diverse linguistic backgrounds.

The interface includes a language selector that supports multiple languages, with preferences saved for returning users. On first use, the system automatically detects the browser language to offer a seamless experience. All interface elements—from navigation labels to help content—are fully localized and accessible, with support for screen readers and high-contrast modes.

Beyond the interface, the system allows users to interact in any supported language. Queries in Spanish, French, or Portuguese are answered in the same language, while English remains available as a reliable fallback.

To maintain performance, translation packs are loaded only when needed, frequently used strings are cached, and unused packs are cleared to reduce memory usage.

Fallback Handling

If a translation is unavailable, the system defaults to English while logging the error for correction. When a user submits a query in an unsupported language, the system provides a clear message:

---

7. Safety and Security Implementation

Input Validation

• Multi-level validation to prevent SQL injection, XSS, and memory abuse.
• Rate limiting: max 10 requests/min/session.
• Sanitization before processing to strip harmful content.

def validate_immigration_query(query, session_id):
    if len(query) > 5000:
        return ValidationResult(False, ["Query too long"])

    if contains_malicious_patterns(query):
        return ValidationResult(False, ["Invalid content detected"])

    if not is_immigration_related(query):
        return ValidationResult(False, ["Please ask immigration-related questions"])

    return ValidationResult(True, sanitize_text(query))

Error Handling

• Structured error handling with retries for transient issues.
• LLM fallback: Gemini → OpenAI → Groq.
• Cache fallback for DB failures.
• Exponential backoff for network timeouts.

Isolation & Logging

• Error boundaries per agent to prevent cascading failures.
• Logs with correlation IDs for traceability without exposing sensitive data.

---

8. Production Deployment Strategy

Containerization

• Docker with Python 3.10 and CPU-only PyTorch for reduced RAM usage.
• Multi-stage builds to shrink image size.
• AI models pre-warmed at build time.

Uvicorn Configuration

• 4 worker processes for 8 vCPU setup.
• Optimized timeouts & connection limits.
• Health checks enabled.

ENV WORKERS=4 \
    MAX_WORKERS=8 \
    TIMEOUT=60 \
    TRANSFORMERS_CACHE=/app/cache

CMD ["uvicorn", "backend.code.api:app", "--host", "0.0.0.0", "--port", "8088", "--workers", "4"]

Scalability

• Stateless design for horizontal scaling.
• Persistent session storage (SQLite).
• Load balancer + auto-scaling rules.

---

9. Monitoring and Operational Excellence

Monitoring Metrics

• Response times, cache hit rates, memory usage, error rates.
• SSE connection performance metrics.
• Slow DB query detection.

Logging

• Structured logs with correlation IDs.
• No personal data stored in logs.

Alerts

• Triggers for high error rates or slow responses.
• Continuous performance audits.

---

10. Risk Assessment and Limitations

AskImmigrate 2.0 Pro, like any production system, faces risks that must be acknowledged. Its functionality depends on external large language model providers, meaning downtime or service interruptions can reduce reliability.

The knowledge base is updated manually, which may cause delays in reflecting new immigration policies.

Performance also has limits. Memory use increases with concurrent sessions, and SQLite—though efficient for moderate workloads—may slow as session histories expand. Long-lived Server-Sent Event (SSE) connections improve interactivity but add server strain under heavy load.

The AI’s responses remain advisory and do not replace professional legal advice. Multilingual support is strongest in English, Spanish, French, and Portuguese, but performance in other languages varies until further fine-tuning is applied.

Acknowledging these risks provides transparency while highlighting areas for future improvement.

---

11. Recommendation

AskImmigrate 2.0 Pro shows strong technical stability, real-world value, and user safety. It is ready to move beyond the development phase.

The system should enter a wider pilot within controlled immigration technology settings. This will test scalability and reliability with a broader set of users and conditions.

Future development should expand coverage to more visa categories and strengthen human-in-the-loop safeguards. These steps will align the system with regulatory requirements while maintaining accuracy and accountability.

---

12. Future Enhancements

Planned upgrades for AskImmigrate 2.0 Pro focus on automation, multilingual support, analytics, fine-tuning, and stronger human oversight.

Automated Knowledge Updates will streamline content management by introducing a crawler and parser for USCIS publications, combined with change detection to automatically refresh the vector database. This will reduce the lag between policy changes and system updates.

Multi-Language Expansion is planned to extend beyond the current support for English, Spanish, French, and Portuguese. The pipeline will include back-translation verification for accuracy and exploration of regional dialect handling to improve inclusivity.

Advanced Analytics will allow stakeholders to monitor usage trends, identify the most common immigration topics, and prioritize system improvements based on real user behavior.

AI Fine-Tuning will focus on adapting embedding and synthesis models to domain-specific Q&A pairs, increasing accuracy for niche immigration categories where general-purpose models may fall short.

Finally, Human-in-the-Loop Safeguards will be expanded to ensure accountability for sensitive cases. High-risk queries such as asylum, deportation, or appeals will be flagged for human review before a response is finalized. Confidence-based thresholds will also trigger human validation when the system is uncertain, and periodic expert audits will maintain long-term quality. These measures will provide an additional layer of trust while keeping automation efficient for routine cases.

---

13. Lessons Learned

The development of AskImmigrate 2.0 Pro revealed several critical insights:

• Smart routing cut response times from 38 seconds to 3–5 seconds by sending simple queries through faster paths while reserving the full workflow for complex cases.
• Server-Sent Events (SSE) delivered real-time updates efficiently, avoiding the added complexity of WebSockets.
• Embedding caching sped up repeated queries, while isolating errors at the agent level prevented system-wide failures.
• Pre-warming language models eliminated cold start delays, and multilingual UI support with persistent user preferences improved accessibility.

Together, these lessons strengthened responsiveness, reliability, and user experience, setting best practices for future iterations.

---

14. Glossary

---

15. References

1. USCIS Official Publications – https://www.uscis.gov
2. ChromaDB Documentation – https://docs.trychroma.com
3. Sentence Transformers – https://www.sbert.net
4. FastAPI Documentation – https://fastapi.tiangolo.com
5. LangChain Framework – https://www.langchain.com

---

16. Performance Characteristics

• Average Response Time:

  • Fast-path queries: 3–5s
  • Complex-path queries: 12–22s

• Throughput:

  • Handles 200+ concurrent users with Docker scaling.

• Cache Hit Rate:

  • ~60% for embeddings, ~40% for full answers.

• Resource Usage:

  • Python backend: ~400MB RAM baseline, scales linearly with sessions.
  • SQLite persists session state, but can be swapped for Postgres in high-load environments.

---

17. License

🔐 License: This project is licensed under the MIT License

---

18. Conclusion

AskImmigrate 2.0 Pro marks a major step forward in applying artificial intelligence to immigration technology. Through multi-agent orchestration, retrieval-augmented generation, and user-centered safeguards, it delivers both robustness and usability.

The evaluation shows that the system not only addresses current needs but can also adapt to future regulatory and technological changes. With expanded visa coverage and stronger safeguards, it is well-positioned to support the digital transformation of immigration services.

In summary, AskImmigrate 2.0 Pro offers a reliable, scalable, and innovative framework that enhances user experience and operational efficiency—representing a meaningful advancement in the field.