Catalog
concept#Product#Integration#Architecture#Software Engineering

Chatbot

A system for automated text- or voice-based communication that receives, interprets, and responds to user requests. Focuses on dialogue management, integrations, and user-centered design.

A chatbot is an interactive software system that enables automated text or voice-based conversations with users.
Established
Medium

Classification

  • Medium
  • Business
  • Design
  • Intermediate

Technical context

CRM systems (e.g., Salesforce)Ticketing and support platformsCalendar and booking systems

Principles & goals

User-centered dialogue: responses should be clear, concise and context-aware.Data minimization and privacy: collect and store only necessary data.Fallback strategies and escalation: defined paths to hand over to human agents.
Build
Domain, Team

Use cases & scenarios

Compromises

  • Misinterpretation of sensitive data and privacy breaches.
  • Incorrect or misleading answers that damage trust.
  • Over-automation that degrades the customer experience.
  • Implement small, well-defined use cases first.
  • Communicate bot capabilities transparently to users.
  • Continuous monitoring, logging and model retraining.

I/O & resources

  • User requests (text/voice)
  • Knowledge base / FAQ
  • Authentication and context data
  • Response texts, actions, hand-offs
  • Tickets or entries in backend systems
  • Conversation transcripts and logs

Description

A chatbot is an interactive software system that enables automated text or voice-based conversations with users. It encompasses dialogue management, backend integrations, and can operate rule-based or with NLP/ML components. Common applications include customer support, virtual assistants, and information retrieval. Design balances UX, security, and operational integration.

✔

  • Scalable customer interaction 24/7.
  • Fast automation of repetitive tasks.
  • Improved availability and reduced wait times.

✖

  • Limited understanding of complex queries without extensive NLU training.
  • Limited emotional intelligence and empathy compared to humans.
  • Operational and maintenance effort for knowledge and integration upkeep.

  • Success rate (intent correctly detected)

    Share of requests where the system correctly identifies the user intent.

  • First contact resolution

    Percentage of requests resolved without escalation to a human agent.

  • Average response time

    Average time from request arrival to the bot's first response.

Rule-based FAQ bot

A simple chatbot that uses keywords to return predefined answers.

NLU-backed support bot

A bot with intent and entity recognition connected to CRM for personalization.

Hybrid model with hand-over

Automated initial handling combined with seamless hand-over to human agents.

1

Define goals and success metrics; prioritize use cases.

2

Design knowledge base and dialog flows; plan fallbacks.

3

Choose technical architecture; implement integrations.

4

Train and test NLU models; set up monitoring.

5

Roll out in controlled phases; establish feedback loops.

⚠️ Technical debt & bottlenecks

  • Monolithic integrations without an abstraction layer.
  • Hardcoded responses instead of central knowledge base.
  • Insufficient monitoring for NLU drift and performance.
NLU accuracyIntegration latencyKnowledge freshness
  • Bot answers complex legal questions without human review.
  • Deployed models return personal data unfiltered.
  • Users are misinformed about bot capabilities (oversell).
  • Underestimating maintenance effort for knowledge upkeep.
  • Lack of measurements for real user satisfaction.
  • Insufficient security review of external integrations.
Conversational UX designNLU/NLP fundamentals and model fine-tuningSystem integration and API design
Dialogue management and context retentionPrivacy and authorization conceptsScalability and resilience
  • • Compliance with privacy regulations (GDPR)
  • • Limited APIs or missing interfaces
  • • Budget and operational resources for maintenance