Catalog
concept#AI#Platform#Security

Conversational AI

AI systems for natural language dialogues and automating user interactions.

Conversational AI refers to technologies and systems that understand, generate, and manage human language in interactive applications.
Established
High

Classification

  • High
  • Business
  • Architectural
  • Intermediate

Technical context

CRM systems (e.g., Salesforce)Ticketing and support platformsSpeech recognition and TTS services

Principles & goals

User centricity: Align dialogues to real user needs.Privacy by design: Minimize and protect sensitive data.Reliability: Define clear fallbacks and escalation paths.
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Data breaches from insecure integrations.
  • Bias and discriminatory responses without data control.
  • Users overtrusting unverified information.
  • Iterative development with real user testing and A/B tests.
  • Strict access control and data minimization.
  • Explicit fallback strategies and transparency toward users.

I/O & resources

  • Conversation transcripts and chat logs
  • Domain-specific knowledge data
  • System interfaces (APIs) for actions
  • Response texts or speech outputs
  • Action calls to backend systems
  • Logging, monitoring and audit trails

Description

Conversational AI refers to technologies and systems that understand, generate, and manage human language in interactive applications. It includes speech and text models, dialogue management, NLU/NLG components, and integrations into business workflows. The focus is on user-centered interaction, automating services, and improving customer experience while ensuring responsible data handling.

✔

  • Scalable customer interaction around the clock.
  • Automation of repetitive tasks to reduce costs.
  • Improved user experience through context-aware responses.

✖

  • Limited domain competence without extensive training.
  • Misinterpretations for ambiguous queries.
  • High infrastructure and maintenance costs for real-time operation.

  • Drop-off rate after bot interaction

    Percentage of conversations without a successful resolution.

  • Intent recognition accuracy

    Share of correctly recognized user intents based on annotated samples.

  • Response latency

    Average time between user query and response output.

FAQ chatbot in e‑commerce

An online retailer uses conversational AI to automate frequent customer questions and reduce live support calls.

Virtual HR assistant

An HR team implements an assistant for employee queries about leave, payroll, and compliance.

Voice UI for IoT devices

A manufacturer integrates voice control into connected devices for hands‑free control and status queries.

1

Define goals and success criteria; involve stakeholders.

2

Collect, clean and annotate data sources.

3

Develop models and dialog flows; run iterative tests.

4

Go live with monitoring, fallbacks and maintenance processes.

⚠️ Technical debt & bottlenecks

  • Monolithic architecture without modularity of NLU components.
  • No automated tests for dialog flows.
  • Insufficient documentation of integration endpoints.
Data qualityLatencyIntegration complexity
  • Using sensitive personal data for unlimited training.
  • Using it as the sole decision-maker in legal matters.
  • Providing unverified answers to customers without sources.
  • Underestimating effort for annotation and domain tuning.
  • Lack of monitoring leads to gradual quality degradation.
  • Undefined escalation paths on failures.
NLP engineering and data annotationSoftware integration and API designSecurity, privacy and governance
Latency requirements for interactive dialoguesPrivacy and compliance requirementsIntegration capability with backend systems
  • • Available training data and its quality
  • • Regulatory requirements for data protection
  • • Budget for infrastructure and operations