Understand
Interprets customer attributes, preferences, transactions, spend, and engagement cadence.
Applied AI / Customer Intelligence
Raw data.
Decisive action.
An agentic AI system that understands customer behavior, identifies meaningful segments, retrieves relevant context, and turns evidence into personalized business recommendations.
Multi-agent AI · LangGraph · RAG · Tool integration
What the system does
Intelligence across the customer journey.
Each specialist component has a clear business purpose—from understanding behavior to producing an action a team can use.
Segment
Identifies meaningful behavioral groups using a specialized classification agent.
Retrieve
Finds relevant customer evidence through vector search to ground the analysis.
Recommend
Ranks product opportunities and generates a personalized reason for every action.
AI system architecture
One orchestrated path from signal to strategy.
LangGraph coordinates specialist agents, retrieval, and structured tools through shared state—creating a repeatable workflow rather than a single prompt.
01
Customer data
Profile, purchases, spend, cadence
02
LangGraph orchestrator
State and conditional routing
03
Segmentation + RAG
FAISS retrieval and classification
04
Recommendation agent
Catalog and analytics tools
05
Intelligence report
Evidence, segment, next actions
Built withPythonFastAPILangGraphFAISSFastEmbedGroq / OllamaLangChain tools
Experience the AI pipeline
Choose a customer. Watch intelligence take shape.
This live workflow runs the actual segmentation, retrieval, analytics, and recommendation pipeline. No pre-scripted result.
01 / Choose a customer profile
Demo profiles use synthetic data. Model output may vary by run; workflow evidence is reported separately from model-generated narrative.
Customer intelligence consoleReady
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Ready to investigate
Select a profile and run the pipeline to see agents, retrieval, tools, and recommendations working together.
From AI prototype to business value
Architecture executives can put to work.
AI becomes valuable when it ends in a decision.
This system connects technical capability to a concrete commercial action, while preserving the evidence behind the output.
Customer intelligence
Understand individual customers beyond static demographic categories.
Personalization
Generate context-aware recommendations across customer profiles.
Revenue opportunities
Surface relevant cross-sell and engagement possibilities.
Enterprise AI pattern
Combine agents, retrieval, structured data, tools, and business logic.
Mini case study
Architected for action, not just analysis.
My role
Architecture → AI/ML → Backend → Integration → UX → Deployment
Architecture → AI/ML → Backend → Integration → UX → Deployment
Segments without decisions
Traditional segmentation produces customer groups, then leaves business teams to interpret what those groups mean and determine what to do next.
An orchestrated intelligence pipeline
I built a multi-agent application that combines structured customer data, retrieval-grounded segmentation, analytical tools, and LLM-generated recommendation narratives in one workflow.
Capabilities demonstrated
Multi-agent orchestrationRAGLLM application engineeringStructured-data reasoningTool integrationMCP-ready boundaryRecommendation generationFull-stack AI delivery
Transparent by design
The experience exposes workflow state, retrieved evidence, tool names, and output metrics—never private chain-of-thought. This makes the system easier to understand, evaluate, and trust.