Case study · Enterprise
Private RAG assistant for customer support
A services enterprise with thousands of support tickets monthly wanted faster first responses without sending customer data to public model providers. We built a grounded assistant over their knowledge base with role-aware retrieval, cited answers, and human approval before any customer-facing reply.
Representative engagement · details anonymized under NDA
Project overview
- Client
- Enterprise services company (name withheld under NDA)
- Industry
- Enterprise
- Services
- RAG Development, AI Agents, AI Automation
The problem
Agents searched across wikis, tickets, and PDFs manually; answers varied by agent. Compliance prohibited customer data in public LLM APIs, ruling out off-the-shelf chatbots.
Requirements
- Answers grounded strictly in approved internal content with citations
- Role-based access — agents see only permitted documents
- Human approval before customer-facing responses
- Deployment inside the company's cloud boundary
- Measurable quality gates before launch
Key challenges
- Messy corpus: duplicate wikis, stale runbooks, conflicting procedures
- Strict data residency and PII handling requirements
- Support leads distrusted AI drafts after a bad pilot elsewhere
Our solution
- Curated and deduplicated the corpus with owners, then built metadata-aware ingestion
- Implemented hybrid retrieval with reranking plus pre-retrieval permission filters
- Built a golden eval set from real tickets and gated releases on faithfulness scores
- Deployed open models in the client's VPC with PII redaction and full audit logs
Technology stack
Security implementation
- Pre-retrieval tenant and role filtering — restricted content never reaches the model
- PII redaction on inputs and outputs
- Immutable audit log of prompts, retrieved context, and actions
- Human-in-the-loop approval for all customer-facing replies
Performance considerations
Cached embeddings and rerank-only-top-k kept p95 answer latency within support-workflow budgets; nightly evals tracked drift.
Results
- Assistant drafts adopted by support leads after eval-gated pilot
- Every answer traceable to source passages for audit
- Eval harness retained by the client for ongoing model updates
Lessons learned
- Corpus curation beats model upgrades for answer quality
- Permission filtering must happen before retrieval, not after generation
- Trust comes from evals and citations, not demo fluency
Keep exploring
Related services
Have a similar challenge?
Tell our Gurgaon team about your project — reply within one business day.
Start the conversation