Katonic

Provides access to a curated catalog of 75+ LLMs (proprietary and open-source) with automatic model selection and routing logic based on task requirements. The platform abstracts model-specific API contracts, tokenization schemes, and rate limits behind a unified interface, allowing users to swap models without code changes. Implements a provider-agnostic abstraction layer that normalizes inputs/outputs across OpenAI, Anthropic, Hugging Face, and other endpoints.

Provides a visual drag-and-drop interface to construct chatbot flows without writing code, including built-in conversation state management that persists multi-turn dialogue context. The platform maintains conversation history in a managed backend store, automatically handling context windowing to fit within model token limits. Supports custom knowledge base integration (document upload, RAG indexing) and conversation branching logic through conditional routing nodes.

Provides controls for data handling, retention, and compliance with regulations (GDPR, HIPAA, SOC 2). The platform enables users to configure data retention policies, encryption at rest and in transit, and audit logging for compliance audits. Supports data anonymization and PII redaction in conversation logs, with configurable rules for sensitive data patterns.

Enables chatbots to query external data sources (databases, APIs, web services) in real-time to provide current information. The platform provides a visual integration builder for connecting to common data sources (Salesforce, Stripe, REST APIs) without code. Implements automatic schema discovery, query result formatting, and error handling to ensure reliable integrations.

Provides a no-code interface to fine-tune selected LLMs on custom datasets without manual hyperparameter tuning or infrastructure management. The platform handles data preprocessing (tokenization, train-test splitting), training orchestration on managed compute, and model versioning. Implements automated hyperparameter search (learning rate, batch size, epochs) and early stopping based on validation metrics, with results tracked in a model registry.

Automates deployment of trained models and chatbots to production with built-in load balancing, auto-scaling, and monitoring. The platform manages containerization, API endpoint provisioning, and traffic routing without requiring DevOps expertise. Implements health checks, automatic failover, and version management to ensure high availability. Supports both synchronous REST APIs and asynchronous job queues for long-running inference tasks.

Enables users to upload documents (PDFs, text files, web pages) and automatically indexes them for retrieval-augmented generation (RAG) to ground chatbot responses in proprietary knowledge. The platform handles document parsing, chunking, embedding generation, and vector storage without requiring manual configuration. Implements semantic search to retrieve relevant context for each user query, with configurable retrieval parameters (top-k, similarity threshold).

Automatically generates REST API endpoints for deployed models and chatbots with OpenAPI documentation, request/response validation, and rate limiting. The platform handles API key management, authentication, and usage tracking without manual configuration. Supports both synchronous request-response and asynchronous job submission patterns for long-running inference tasks.

Provides dashboards and metrics for tracking chatbot performance, including conversation volume, user satisfaction, intent classification accuracy, and response latency. The platform logs all conversations (with privacy controls) and enables filtering by user, intent, or time period. Implements automated alerting for anomalies (sudden error spikes, latency degradation) and provides recommendations for model or knowledge base improvements.

Manages multiple versions of trained models and deployed chatbots with automatic version tracking, rollback capabilities, and built-in A/B testing infrastructure. The platform routes traffic between model versions based on configurable rules (percentage split, user segment, time-based) and tracks performance metrics for each variant. Enables safe experimentation without manual traffic management or infrastructure changes.

Enables chatbots and models to operate across multiple languages with automatic language detection, translation, and locale-specific response formatting. The platform handles language-specific tokenization, embedding models, and LLM selection (choosing models optimized for each language). Supports custom terminology and glossaries to ensure consistent translation across conversations.

Provides tools for iteratively testing, refining, and optimizing prompts without deploying to production. The platform includes a prompt editor with syntax highlighting, variable substitution, and prompt templates for common use cases. Implements automated prompt optimization that tests variations and recommends improvements based on output quality metrics (relevance, coherence, factuality).