AI-Powered Development Services

Challenge

Requirements gathering is one of the most time-consuming and inconsistent phases of software delivery. Teams spend hours interviewing clients, organizing notes, clarifying details, and formatting documents. The process is slow, expensive, and varies widely between analysts.

The client needed a faster, standardized, and scalable way to capture requirements without losing depth or clarity. Their goal was simple:

• Deliver complete documentation in minutes - Not days
• Improve quality and consistency - Standardized output across all projects
• Reduce analyst workload - Free up time for higher-value activities
• Scale across teams - Enable any team member to conduct discovery sessions

Our Approach

We designed Bantum as a multi-agent AI Business Analyst system that orchestrates specialized agents to guide users through natural conversations and convert dialogue into structured, professional documentation. Each agent is optimized for a specific role, from live user engagement to deep report analysis.

Solution

Bantum is a sophisticated multi-agent AI system that orchestrates specialized agents to conduct guided discovery sessions and transform conversations into professional documentation in minutes. The system intelligently routes between agents based on session state, maintains strict dialogue quality, and produces dual PDF outputs optimized for different audiences.

Bantum delivers:

• 6-Agent Orchestration - Orchestrator routes to specialized agents: Responder (dialogue), CompletionTracker (readiness assessment), ReportAgent (synthesis), Analyst (deep insights), and ResponseValidator (quality control)
• Strict Dialogue Discipline - One-question-per-message enforced across multiple validation layers, preventing question fatigue and ensuring focused discovery
• Real-time SSE Streaming - Immediate user feedback with 15-second keep-alive intervals to handle extended LLM processing
• Comprehensive 12-Section Discovery - Tracks progress across extended BANT+ framework (Budget, Authority, Need, Timeline, Tech Stack, UX/UI, Competitors, Future Features, Success Metrics, Constraints, and more)
• Dual PDF Generation - Client-facing report (polished, executive summary) + Internal analysis (complete BANT deep-dives and strategic recommendations)
• Cost-Optimized LLM Strategy - Provider-specific routing (Grok for fast dialogue, Llama for analytical depth, Claude for coordination) delivers 95% cost reduction vs. single-model approach
• Session-Level Token Tracking - Per-agent token accounting with cost aggregation for full observability and optimization
• Persistent Sessions Across Devices - Resume conversations with 30-minute staleness detection and automatic restoration
• Enterprise Security - Firebase App Check, IP-based rate limiting (configurable 60 req/60s default), session limiting (5 per 24h), and strict Zod validation
• Automated CI/CD with GitHub Actions - Continuous deployment with automated testing and quality assurance

Why It Matters

Bantum demonstrates how sophisticated multi-agent orchestration can transform one of the slowest phases of software delivery into an instant, scalable, and cost-efficient workflow. This case showcases advanced AI integration patterns that go beyond single-model AI to achieve enterprise-grade reliability, quality, and economics.

The key innovation is the multi-agent routing architecture: Instead of forcing one model to handle all tasks (dialogue, validation, analysis, report generation), Bantum assigns each task to the optimal agent. Grok excels at fast, natural dialogue. Llama provides analytical depth for complex synthesis. Claude coordinates decisions efficiently. This specialization delivers 95% cost reduction and superior output quality compared to single-model approaches.

Beyond cost optimization, the system demonstrates critical production patterns:

• Quality validation layers (ResponseValidator prevents duplicate questions and maintains focus)
• Intelligent session completion detection (CompletionTracker assesses readiness across 10 BANT+ dimensions before generating reports)
• Dual output strategy (Client-facing summaries vs. internal deep-dives serve different stakeholder needs)
• Full observability (Session-level token tracking per agent enables continuous cost and quality optimization)

By combining multi-agent orchestration, provider-specific routing, and robust state management, Bantum reduces discovery costs by 90%, improves consistency, and frees business analysts from transcription work. This enables teams to move faster from discovery to delivery while maintaining the depth and quality of requirements that successful projects demand.

This case highlights the impact of AI Integration, AI-Powered Development, and advanced AI Agent orchestration from our service portfolio.

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