Case Study — ML Chatbot Support

A retrieval-augmented, guardrailed support assistant that resolves customer requests across channels and languages, with secure data handling and robust analytics.

overview

We built a production chatbot that deflects tickets, accelerates agent responses, and keeps answers aligned to the company’s policies and knowledge. The assistant blends Retrieval-Augmented Generation (RAG), workflow automations, analytics, and human handoff to deliver measurable support outcomes.

Use cases

Self-service Q&A • Order/trouble-ticket updates • Troubleshooting flows • Policy & billing FAQs • Agent assist

Channels

Web widget • Mobile SDK • Email triage • WhatsApp • Facebook Messenger • Slack/Teams

Highlights
  • RAG over manuals, FAQs, tickets, and policies
  • Guardrails: PII redaction, tone & policy constraints
  • Multilingual (detection + translation) & locale formatting
  • Workflows: CRM/ITSM actions (Zendesk, Salesforce)
  • Human handoff with full transcript
  • Analytics: deflection, CSAT, AHT, containment

high-level architecture

Channels

Web/Mobile • WhatsApp • Email • Slack/Teams
Webhook adapters & SSO

Orchestrator

Intent & entity detection • Dialog state • Guardrails • PII redaction

RAG Layer

Chunking • Embeddings • Vector DB • Re-ranking • Source citing

Tools & Actions

CRM/ITSM APIs • Order lookup • Ticket create/update • Knowledge sync

Observability

Metrics • Traces • Feedback loops • Evaluation & experiments

capabilities

NLU & intent

Multi-turn intent classification, entity extraction, form-filling, fallback & clarifications.

RAG

Chunking & embeddings with re-ranking; citations to sources to reduce hallucinations.

Workflows

Secure tools for ticketing, account lookups, refunds, returns, and status updates.

Guardrails

PII redaction, toxicity filters, jailbreak resistance, policy & tone constraints, GDPR support.

Multilingual

Language detection + translation; locale-aware date, currency, and units.

Analytics

Deflection, containment, CSAT, AHT; session replays; evaluator dashboards.

delivery

1) Discovery & data audit

Ticket taxonomy, KB quality, data access, privacy & retention, success metrics definition.

2) Pilot & evaluations

Grounded responses via RAG; offline & human-in-the-loop evals; shadow mode on live traffic.

3) Launch & iterate

Progressive rollout, A/B tests, prompt/policy updates, auto-tuning rankings & guardrails.

Compliance-minded design with encryption in transit/at rest, access logging, and data minimization.

results

  • Ticket deflection increased through accurate self-service answers and guided flows.
  • Agent productivity improved via summaries, suggested replies, and relevant citations.
  • Customer satisfaction uplift from faster, policy-aligned responses.
  • Operational visibility with granular metrics and evaluators for continuous improvement.
35–55%
Deflection rate
+12 pts
CSAT lift
-25%
AHT
95%+
Grounded hits

tech stack

Python / FastAPI Node.js OpenAI / Azure OpenAI / local LLMs Sentence/Embedding models pgvector / Pinecone Kafka / Redis OpenTelemetry Docker / Kubernetes OAuth2 / SSO Zendesk / Salesforce / ServiceNow

We tailor models, prompts, and retrieval to your domain and governance requirements.