How do you implement AI for natural language interfaces to business applications?
Natural language interfaces (NLI) allow users to interact with business software using conversational language instead of forms and menus. Architecture: input processing (speech-to-text for voice, text normalization for chat), intent and entity extraction (classify user intent and extract parameters), action mapping (translate intent + entities into application operations), execution (perform the operation via application APIs), and response generation (format results in natural language). Implementation: define the action space (what operations users can perform via natural language), create intent schemas mapping natural language patterns to application actions, implement entity extraction for parameters (dates, amounts, names, statuses), and build execution layer that calls application APIs. Examples: "Show me all invoices over $10,000 from last quarter" maps to a filtered query on the invoicing system. "Schedule a meeting with the design team next Tuesday at 2pm" maps to calendar API calls with attendee lookup. "What's the status of the Johnson account?" maps to CRM lookup by account name. Disambiguation: when the query is ambiguous, ask clarifying questions ("Did you mean the Johnson account in New York or London?"), present options when multiple matches exist, and confirm before executing state-changing actions ("I'll send the invoice to Acme Corp for $15,000. Should I proceed?"). Technical implementation: use function-calling LLMs (GPT-4, Claude) that receive tool definitions matching application operations, the LLM extracts parameters and selects the appropriate function, and the system validates and executes the call. Error handling: provide helpful error messages in natural language, suggest alternatives when the requested action isn't possible, and gracefully handle out-of-scope requests. Evaluation: task completion rate, parameter extraction accuracy, number of clarification turns needed, and user satisfaction. Progressive enhancement: start with read-only queries, add write operations with confirmation, and expand to complex multi-step operations as confidence grows.