A chatbot is designed to answer questions in a conversational interface. An AI agent goes further — it can interpret complex inputs, reason through multi-step decisions, and take actions such as generating documents, creating records, or connecting to external systems. AI agents use a large language model (LLM) as their reasoning engine and can operate autonomously or semi-autonomously within a defined business process.

AI agents are best suited to problems that involve interpreting variable inputs, applying domain knowledge, and producing structured outputs. Common examples include quote generation from complex pricing data, answering specialist customer questions using a curated knowledge base, bulk scheduling across multiple resources, data extraction from unstructured documents, and compliance checking against defined rules.

Timeframes depend on the complexity of the process being automated and the integrations required. A focused AI agent for a single, well-defined workflow can often be scoped and delivered within weeks. Larger multi-system solutions with custom knowledge bases and enterprise integrations require a more involved discovery and build process. WebAlive works with clients to define scope clearly before committing to a timeline.

WebAlive builds AI agents using proven LLM platforms including OpenAI's GPT-4o. The choice of model is determined during the architecture phase based on the task requirements, cost profile, and performance characteristics needed. We design systems that can be updated or migrated to new models as the AI landscape evolves.

RAG stands for Retrieval-Augmented Generation. It is a technique where an AI agent retrieves relevant content from a knowledge base at query time and uses that content to inform its response — rather than relying solely on the LLM's training data. RAG is used when an agent needs to answer questions based on your specific documents, products, policies, or domain content. WebAlive used a RAG architecture to build the Cortex AI platform for Genotype Health.

Yes. AI agents built by WebAlive are designed to integrate with your existing data sources and systems via APIs and databases. This allows agents to access live data, write records back to your systems, and operate within your existing workflows rather than requiring you to migrate to a new platform.

WebAlive builds AI solutions with security as a core architectural concern, not an afterthought. Data handling, access controls, and integration pathways are designed in the architecture phase and implemented to enterprise standards. We can discuss your specific data residency and security requirements as part of a discovery conversation.

WebAlive follows a structured five-phase process: Discovery (mapping the process and identifying inputs, outputs, and data sources), Architecture (designing the agent's structure and integrations), Build (engineering the solution with senior oversight), Test & Tune (validating against real-world inputs), and Deploy & Support (go-live and ongoing iteration). AI-assisted tooling is used to accelerate delivery, but senior architects govern system structure throughout.

WebAlive has delivered AI agent solutions across healthcare, construction, and labour-hire industries — including a genetic health knowledge assistant for Genotype Health, a quoting agent for cabinet maker Cabco Kitchens, and a bulk scheduling tool for medical staffing platform LocumWest. We work across industries where complex, knowledge-intensive processes are candidates for automation.

Cost depends on the scope of the solution — the complexity of the process being automated, the number of integrations required, and the volume and nature of the knowledge base involved. WebAlive provides fixed-price scoping after a discovery phase so clients have full cost visibility before committing to a build. Contact us to discuss your requirements.