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AI Agents

Custom AI Agents

Purpose-built AI agents trained on your domain. Not generic chatbots — specialised systems that understand your industry terminology, processes, and compliance requirements.

3-Tier
Agent Architecture
RAG
Knowledge Retrieval
Live
Client Deployment
Auto
Escalation to Human

How It Works

Your team interacts with AI agents through a chat interface or API. An intelligent router directs each query to the right specialised agent — research, analysis, or action. Each agent retrieves knowledge from your domain-specific data, and escalates to humans when confidence is low.

3-tier AI agent system architecture showing user interface, agent orchestration with router and specialised agents, and data integration layer
1

Ask a Question

Your team asks questions in plain English via chat or your systems call the agent API. No special syntax or commands needed.

2

Intelligent Routing

The agent router analyses the query and directs it to the right specialist agent — research for information, analysis for data, or action for task execution.

3

Grounded Response

The agent retrieves relevant information from your knowledge base, generates an answer grounded in your actual data, or escalates to a human if unsure.

Technical Details

RAG Pipeline Vector Database Local LLM Agent Orchestration Embedding Models Python
Architecture Details

Agent Router: Intelligent query classification that routes to specialised sub-agents based on intent, domain, and required capability.

Knowledge Retrieval (RAG): Vector database with semantic search over your documents, policies, and data. Embedding models convert your content into searchable representations.

Sub-Agents: Specialised agents for research (document retrieval), analysis (data processing), and action (system integration). Each has domain-specific prompts and tool access.

Escalation: Confidence scoring on every response. Below-threshold responses are escalated to human operators with full context.

Hardware Requirements

Recommended: Apple Silicon (M1 Pro+) or NVIDIA RTX 3090 for local LLM inference and embedding generation.

RAM: 32GB+ recommended for concurrent agent processing and vector database operations.

Storage: Depends on knowledge base size. Vector databases are compact — 1M documents typically requires 10-50GB.

Who This Is For

Legal Firms

Legal research agents that understand case law, legislation, and citation formats. Search across thousands of documents instantly.

Real Estate

Property data agents that analyse market trends, comparable sales, and planning regulations for informed decision-making.

Technical Teams

Internal knowledge agents that search documentation, runbooks, and code repositories. Reduce onboarding time and knowledge silos.

Customer Service

Support agents trained on your products, policies, and procedures. Consistent, accurate responses with human escalation.

Frequently Asked Questions

How are custom AI agents different from ChatGPT or generic chatbots?
Generic chatbots have broad but shallow knowledge. Our agents are trained on your specific domain — your documents, your terminology, your processes, your compliance rules. They understand context that generic AI cannot. A legal research agent knows case law citation formats. A medical agent understands clinical terminology. They are specialists, not generalists.
What is a 3-tier agent architecture?
The three tiers are: (1) Interface Layer — where your team or systems interact with the agent via chat or API, (2) Orchestration Layer — the intelligent router that directs queries to specialised sub-agents for research, analysis, or action, and (3) Data Layer — your databases, document stores, and external integrations. This separation means each component can be optimised independently.
Can the agent access our internal documents and databases?
Yes. Agents are connected to your domain knowledge via retrieval-augmented generation (RAG). This means the agent searches your documents, databases, and knowledge bases in real-time when answering questions, ensuring responses are grounded in your actual data rather than general AI knowledge.
What happens when the agent cannot answer a question?
Configurable escalation is built into every system. When an agent encounters a question outside its confidence threshold, it transparently escalates to a human operator with full context of what was asked and what it found. No hallucinated answers — if it does not know, it says so and gets help.
Is the agent data kept private?
Yes. Agents run on local infrastructure with your knowledge base stored on your servers. No training data, queries, or responses are sent to external services. The entire system operates on-premise or on your private cloud infrastructure.

Build AI That Knows Your Business

Stop using generic chatbots. Get AI agents trained on your domain, your data, and your processes.

Book a Free Consultation Call 0400 819 487