AI-Driven Onboarding Analytics System

The Retrieval-Augmented Generation (RAG) system is the engine that ensures AI responses are grounded in real, verifiable company data, including dynamic market conditions and evolving regulatory landscapes.

• Vector Database: Pinecone for financial document embeddings, regulatory texts, internal knowledge bases, and real-time market data, ensuring lightning-fast semantic search and contextual relevance.
• Embedding Model: text-embedding-3 fine-tuned on financial, regulatory, and user interaction data to improve domain-specific context retrieval and understand user intent.
• Context Window Management: A dynamic context window (e.g., 16k-token with sliding window) for processing and understanding long regulatory documents, comprehensive user session logs, and live market news, adapting based on query complexity and available information.
• Real-time Data Ingestion: MCPs feed real-time market data, news, and regulatory updates into the vector database, ensuring the RAG system always has the freshest context.

Example Query:

- Input: "Show German users who dropped out during ID verification between 2-5 PM yesterday, and what common regulatory hurdles they faced."

- Processing: The RAG engine translates this into a series of retrieval and filtering steps:

1. Time-range filtering (2-5 PM)
2. Geographic filter (German IPs/documents)
3. Stage identification (ID verification)
4. Dropout detection (incomplete flows)
5. Retrieval of relevant German regulatory documents (e.g., BaFin, GDPR) and common error patterns associated with those regulations.

The system uses a pool of specialized AI agents, each with a distinct set of tools and responsibilities, that work together under the Orchestration Layer to solve complex queries and proactively intervene.

This flow shows how the system deconstructs a natural language query into a series of actionable steps, leveraging real-time data and agent capabilities.

Query: "Why did German users drop out yesterday during the deposit stage, and how can we improve it?"

1. Dynamic Document Verification Flow: The Compliance Agent, informed by real-time regulatory updates via MCPs, automatically checks an uploaded utility bill against jurisdiction-specific rules (e.g., image quality, address format, issuer legitimacy, date validity). The system provides an accept/reject decision with specific, actionable feedback, and if needed, the Onboarding Flow Agent offers alternative document types or simplified upload instructions.
2. Adaptive Risk Assessment Process: The system evaluates a user's trading experience, financial knowledge, and risk tolerance in real-time, leveraging RAG for the latest market volatility data and regulatory suitability rules. It generates a personalized risk profile and applies appropriate product restrictions or educational nudges, dynamically adjusting as the user interacts with the platform or market conditions change.
3. Personalized Onboarding Journey: Based on initial user data (e.g., country, stated experience), the UX Agent dynamically adjusts the onboarding flow. For a beginner in Germany, it might emphasize demo account usage, provide simplified language, and offer a direct link to educational resources, while for an experienced trader, it might fast-track to funding options.

This process is used for complex, multi-step decisions, such as comprehensive document verification or complex risk assessments, by exploring multiple reasoning paths in parallel and evaluating their outcomes.

Use Case: Complex Verification Decisions (e.g., for high-risk jurisdictions)

This framework allows the system to generate an initial response or action, critique its own accuracy and effectiveness, and then perform additional retrieval and refinement cycles before presenting the final answer or executing a final action. This is crucial for maintaining high accuracy in a dynamic Forex environment.

Example Implementation in Document Processing:

Document: Proof of Address

Self-Reflection Steps:

1. Initial Assessment: "Is document type correct? Is the date visible? Is the name matching?" (Initial RAG query for basic validation rules).
2. Quality Check: "Can I read all text? Are all corners visible? Is there glare or blur?" (Agent uses image processing tools, RAG for image quality standards).
3. Verification & Cross-Referencing: "Does the address match the application and CRM? Is the issuer legitimate? Is the document recent enough per regulation?" (Agent queries multiple MCP-fed data sources and RAG for regulatory recency rules).
4. Critique & Refinement: "Are there any conflicting data points? Is the confidence score high enough? Should I retrieve more context on this specific issuer or regulation?" (LLM critiques its own assessment, triggers further RAG queries or agent actions).
5. Final Decision: Confidence score calculation and Accept/Reject decision with detailed reasoning and audit trail.

1. Tree of Thoughts Process Flow:
   flowchart LR UA[User Action] --> MAP[Multiple Paths] --> BPS[Select Best Path] --> IMP[Execute/Respond]
2. Self-Reflective RAG System:
   flowchart LR Q[Query/Action] --> IR[Initial Results] --> SV[Self-Validation] --> R[Refinement] --> FR[Final Response]
3. Constitutional AI Framework:
   flowchart LR I[Input] --> RC[Regulatory Check] --> CV[Policy Validation] --> SA[Safe Action]
4. Multi-Agent Collaboration:
   flowchart LR CT[Complex Task] --> OD[Orchestrator] --> AC[Agents Collaborate] --> SR[Synthesize Results] --> UO[Unified Output]

Overall Project Duration: 36-45 Weeks (Approx. 9-11 Months) - Adjusted for new features

• Real-time performance tracking of all system components (RAG, agents, MCPs).
• User experience assessment through analytics, feedback, and session replays.
• Compliance verification through automated audits and rule checks.
• System health monitoring with proactive alerts for anomalies.
• Agent performance metrics (e.g., accuracy of interventions, success rate of recommendations).

• Automated testing (unit, integration, end-to-end) for all code changes.
• User acceptance verification with pilot groups and A/B testing.
• Compliance confirmation through simulated regulatory scenarios.
• Performance validation under various load conditions.
• Adversarial testing for LLM and agent robustness.
• Human-in-the-loop validation for critical agent decisions.