AI Business Analyst Agents for BFSI Software Firm

Project Overview

A global financial technology provider approached us to augment their business analysis function with AI. Their analysts were overwhelmed with repeatable, high-stakes tasks like regulatory mappings, compliance diff checks, and vendor migration documentation. These tasks consumed up to 40% of their time, were prone to human error, and were spread across siloed tools such as Excel, JIRA, and Confluence. Our goal was to design an AI-powered agents that could streamline and scale analytical workflows while preserving traceability and compliance.

Key Challenges

• High analyst workload due to repetitive compliance and documentation tasks.
• Fragmented toolchain across JIRA, Confluence, and internal systems.
• Lack of consistent workflows and reasoning traceability.
• Need for secure and explainable AI integrations in a regulated environment.

Our Solution

• Agentic AI Crew: Developed specialized AI agents for payment workflows, card schemes, ISO 20022 migrations, and regulatory compliance. Each agent was tuned to its sub-domain and could work independently or in collaboration.
• LangGraph Workflow Orchestration: Orchestrated agent interactions and memory using LangGraph, enabling structured collaboration across projects with built-in decision checkpoints.
• Enterprise LLM Stack: Implemented intelligent model routing across GPT-4, Claude, and fine-tuned open-source models - balancing cost, latency, and context relevance. Applied triangulation strategies for output validation.
• Domain-Fine-Tuning: Fine-tuned LLMs using internal documentation, annotated project logs, and expert-labeled datasets. This increased domain specificity without sacrificing generality.
• Native Tooling Integration: Embedded agents directly into analysts' existing tools (JIRA, Refertest, internal IDE, Slack), enabling contextual co-piloting and action suggestions within their daily workflows.
• Security & Control: Enforced workspace-level data isolation, private vector stores, and retrieval domains in line with data residency requirements and regulatory standards.

Technologies Used

The solution stack was designed for security, scalability, and future extensibility:

• LangGraph: Controlled agent flows, memory, and decision trees based on analyst roles and project type.
• FastAPI: Served as the backend API layer for agent orchestration and tool integration.
• PostgreSQL + Chroma: Combined structured metadata storage with vector embeddings for hybrid retrieval.
• Anthropic Claude / OpenAI GPT-4 / LLaMA: Routed based on task complexity, cost-efficiency, and sensitivity.
• Slack, JIRA, Refertest Connectors: Allowed agents to trigger actions, pull data, and update workflows autonomously.

Results

The agentic system exceeded expectations in both analyst productivity and enterprise integration quality:

• Reduced repeatable manual work for analysts by 60%.
• Cut compliance documentation preparation time from 3 days to 4 hours.
• Improved auditability through AI reasoning logs and version-controlled decision checkpoints.
• Accelerated onboarding of junior analysts via AI-led knowledge navigation and task walkthroughs.
• Enabled 24/7 availability of AI "assistants" that analysts could consult for guidance or delegation.

Competitor Differentiation

What distinguishes NodeNova from other AI engineering vendors is our business-aware approach to agentic system design. We don't just drop in a chatbot - we build intelligent teams of AI agents that reflect real analyst workflows and decision logic. In this project, we worked shoulder-to-shoulder with domain experts to embed institutional knowledge, reduce change management friction, and ensure the system was explainable, controllable, and secure.

• Deep BFSI process knowledge fused with modern LLM orchestration tools.
• Configurable AI agent architecture that adapts to new regulations or business domains.
• Security-first design approach with customizable workspace isolation and retrieval control.
• Focus on long-term ROI, not short-term automation hype.

Long-term Impact

We continue to support the client by evolving the agentic system as new regulatory frameworks emerge. Our AI crew framework is now being evaluated for rollouts in new departments such as risk management and customer onboarding. With every iteration, we collect usage signals and expand domain knowledge - ensuring that the AI workforce continues to scale, adapt, and deliver value without disrupting business continuity.