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AI Consulting · Customer Support
45%

lower support costs after moving to GraphRAG

How PuredotIndia rebuilt a client's support stack on a knowledge-graph RAG architecture — and cut resolution time along with it.

Case study reel Auto-playing
The starting point

Flat RAG was hitting a wall

01
Support answers needed context spread across multiple linked documents — plain vector search kept missing the connections between them.
02
Agents were re-reading the same policy PDFs for every escalation, because retrieval couldn't reason across related cases.
03
Chunking by fixed character count sliced answers mid-sentence, so the model retrieved fragments instead of full context.
04
Ticket volume kept growing faster than headcount could scale with it.
The rebuild

A custom GraphRAG pipeline

Ingest support corpus
Tickets, macros, policy docs, release notes
Entity & relationship extraction
LLM pass builds nodes for products, issues, fixes
Knowledge graph + community summaries
Related cases clustered and pre-summarized
Hybrid retrieval
Local graph traversal + vector search, merged
Grounded agent response
Answer with traceable source path
The toolkit

Built on production-grade infrastructure

AI & Agent Development
LangChain LangGraph AutoGen
Model Ecosystem
Hugging Face ChatGPT Copilot
Data & Retrieval
Pinecone PostgreSQL MongoDB
Workflow Automation
n8n
Design snapshot

How the stack fits together

Each layer maps to a specific job in the pipeline — swapped and tuned until retrieval stopped losing context.

Orchestration
LangGraphAutoGenLangChain
↓
Reasoning & models
ChatGPTHugging FaceCopilot
↓
Graph & vector retrieval
PineconePostgreSQLMongoDB
↓
Workflow automation
n8n
Design changes that moved the needle
LangGraph replaced a single-agent script with a stateful multi-agent graph, so escalations hand off cleanly between agents.
PostgreSQL now stores the entity graph alongside Pinecone's vector index — one hybrid query instead of two round trips.
n8n triggers the human handoff automatically once agent confidence drops below threshold.
Under the hood

Chunking strategy made the difference

Fixed-size chunkingbaseline
Equal-length slices, no regard for meaning — breaks context across chunk boundaries.
Semantic chunkingimproved
Splits at topic shifts, so each chunk holds one coherent idea.
Graph-aware hierarchical chunkingPuredot approach
Chunks are sized around entity boundaries and linked back into the graph, so retrieval pulls whole relationships, not fragments.
The outcome

What changed after go-live

Support cost per ticket−45%
Average resolution timefaster
First-contact resolutionhigher
Agent time on repeat lookupsreduced
The 45% cost reduction came from fewer escalations, not fewer agents — the graph did the cross-referencing work agents used to do by hand.
Why this architecture

Knowledge-graph RAG is already in production at scale

Independent of this case study, these organizations have published engineering work on graph-augmented retrieval for exactly this kind of problem.

MicrosoftCreated the original GraphRAG pipeline
LinkedInKnowledge graphs for customer service QA
DoorDashRAG for Dasher support automation
UberAgentic RAG for on-call support
PinterestRAG-driven retrieval at scale

Bring this to your support stack

PuredotIndia designs and ships GraphRAG systems for teams that need answers grounded in real, connected data — not guesses.

Book a strategy call
PuredotIndia AI Consulting — puredotindia.com