Deploy production-ready AI agents in weeks

The base Knowledge Agent is the core intelligence and conversational front-end of the adaptable knowledge agent framework. Designed as a multi-agent system, it manages complex conversations, learns from user feedback, and delegates tasks like information retrieval (enhanced by vector search, knowledge graphs, and filtering), summarization, translation, and mind map generation to dedicated sub-agents, with the flexibility to integrate new functionalities as needed.

The Pluggable UI components includes a natural language search bar deployable across various pages, a conversational chat UI that integrates into core applications, and an intelligent Reader for document exploration that saves progress and notes, and supports personalized research through persistent document storage with highlights and annotations.

The Data integration framework allows users to configure feed or URL synchronization for importing documents into cloud storage, enabling automated data ingestion from external knowledge sources. It also facilitates easy scheduling and orchestration of these data pipelines, ensuring the knowledge agent has access to up-to-date information.

The configurable RAG pipeline converts unstructured data into searchable vector embeddings using OCR, chunking, and embedding, storing them in a vector index for efficient retrieval. A task orchestrator, initiated by new documents entering cloud storage, manages the RAG pipeline to ensure the search index is continuously updated.

The Retriever tool functions as a sophisticated search engine. It performs semantic searches against a vector index to pinpoint the most relevant pieces of information (chunks) that match a user’s query. These chunks are then returned, often with accompanying metadata and context, which can be further enriched with knowledge graphs, and can be further refined through re-ranking or filtering to ensure the agent can formulate the most accurate and comprehensive answer.

The AgentOps framework component provides observability by analyzing the agent answers to drive continuous performance improvements. It also implements security guardrails and manages the agent’s cost, scalability, and operational efficiency.

The agent first understands user requirements through clarification, then transforms them into precise queries for a vector search tool to find relevant products. It maintains conversational memory to ensure context continuity while delivering ranked, personalized product recommendations based on the user’s specific requirements.

The Pluggable Search UI serves as the front-end interface—a flexible, embeddable component—that facilitates the entire natural language interaction between the user and the Recommender Agent. It seamlessly integrates the multi-turn dialogue with the presentation of personalized, re-ranked product results.

The data integration framework component enables users to configure either feed or URL synchronization to import product and service information into cloud storage. It facilitates easy scheduling and orchestration of the data pipeline.

The RAG pipeline’s Data Enrichment module improves product data by adding descriptive captions to images and summarizing lengthy text fields. Following enrichment, embeddings are generated for this data and stored for indexing in a vector search index, which then allows for efficient semantic querying and retrieval of related items.

The Retriever tool uses semantic search on a vector index to find relevant products based on user queries (text/image). It returns these results with metadata, and can optionally re-rank or filter them for personalization.

The AgentOps framework component provides observability by analyzing recommendation outcomes to drive continuous performance improvements. It also implements security guardrails and manages the agent’s cost, scalability, and operational efficiency.

Agentic AI refers to systems that combine intelligence with autonomous decision-making and action-taking capabilities. Unlike traditional automation tools that follow predefined rules or workflows, agentic AI solutions can understand complex requirements, reason through situations, make decisions independently, and take actions based on those decisions. In the context of Rappit, agentic AI means building solutions where the system can observe data, analyze it, make intelligent decisions about the next steps, and execute tasks, including capabilities like having conversations, asking clarifying questions, and negotiating with people, without requiring human intervention at each step. This represents a fundamental shift from automating individual steps in a process to automating entire decision-making workflows.​

While the terms are related, there’s an important distinction. Agentic AI is the broader philosophical approach to building systems with autonomous decision-making and action capabilities, it’s a design pattern and architectural principle. AI agents are the specific implementations of agentic AI. Rappit’s Knowledge Agent and Recommender Agent are examples of AI agents built on agentic principles. These agents represent functional solutions designed for particular business problems, whereas agentic AI describes the underlying capability for autonomous reasoning and action. Think of it this way: agentic AI is the concept; AI agents are the products.​

Both the Knowledge Agent and Recommender Agent are designed for rapid implementation. Typical deployment takes 6-8 weeks from contract signature to production launch. This assumes your organization has prepared the necessary data sources and API infrastructure for integration. The timeline is significantly compressed because these are proven, adaptable solutions rather than custom-built systems developed from scratch. Rappit’s dedicated implementation team handles configuration, data integration, testing, and deployment, while you focus on defining business requirements and validating the solution.​

Yes, they can be adapted and integrated into any open core system or website. For example, the Recommender agent can be integrated with your ecommerce website or CRM system and the Knowledge agent with your Transportation Management System (TMS).

Both agents are configured during implementation with your specific data, business rules, and workflows. For the Knowledge Agent, this means connecting to your document repositories, databases, and knowledge systems so it can retrieve accurate, contextual information specific to your organization. For the Recommender Agent, it means integrating with your product catalogs, inventory systems, and customer data so recommendations reflect your actual offerings and customer preferences. Rappit’s AI agents are engineered for continuous improvement, featuring built-in observability and feedback mechanisms.

Rappit’s architecture is built for extensibility. The configurable nature of these agents allows for significant customization during the initial implementation phase. For more advanced customization needs or unique requirements beyond standard configurations, we work with your team to assess the scope. Some extensions can be handled within the existing agent framework, while others may require dedicated development work. We can discuss your specific needs during the requirements phase to provide accurate timelines and costs for any custom enhancements.​

To ensure the utmost protection of your data and customer interactions, Rappit’s AI agents are fortified with robust security measures, including a multi-layered guardrail strategy and Google Cloud’s Model Armor service. This comprehensive approach treats safety as a continuous defense system operating throughout the entire lifecycle of a user interaction. Data security is built into Rappit’s platform and agent architecture. Beyond this multi-layered guardrail strategy, all communication between agents and your data sources is encrypted, and access is controlled through authentication and authorization mechanisms you define. Agents operate within your infrastructure environment and you maintain control over where data is processed and stored. Our annual subscription includes security monitoring, compliance oversight, and regular security patches. We comply with major data protection standards and can work with your security and legal teams to meet industry-specific compliance requirements (HIPAA, SOC 2, GDPR, etc.). Data retention policies are configurable based on your requirements.

Rappit AI agents are designed to demonstrate tangible business value, which is measured through an integrated observability framework. This framework allows us to model and track customer-defined success metrics from the outset of implementation. Regular reporting dashboards provide ongoing performance analytics, enabling continuous identification of optimization opportunities. Most organizations experience a measurable return on investment within 3-6 months of production launch.