Our Methodology

Every AgentForge AI agent is built using a structured process designed for reliability and transparency. We start by understanding the specific workflows of each industry — dental practices handle appointment queries differently from law firms handling case enquiries. We map these workflows before writing a single line of code.

Our agents are built on proven large language model foundations, fine-tuned with domain-specific knowledge for each industry. We use retrieval-augmented generation (RAG) to ensure agents answer from your actual business data — not hallucinated information.

Every agent includes configurable escalation rules. When a query exceeds the agent's confidence threshold or involves sensitive topics, it hands off to a human team member. We believe AI should handle the routine so your team can focus on what matters.

Before any agent goes live, it undergoes a multi-stage quality assurance process. We test with realistic scenarios from the target industry, including edge cases like ambiguous questions, multi-topic queries, and deliberately adversarial inputs.

We measure accuracy, response appropriateness, escalation behaviour, and response latency. Only agents that meet our quality thresholds are deployed. After launch, we monitor performance continuously and iterate based on real interaction data.

Data privacy is non-negotiable. All data processing complies with UK GDPR and the Data Use and Access Act (DUAA) 2025. We process data only as needed to operate your AI agents, and we never sell or share customer data with third parties.

Business data used to train or configure your agents remains yours. You can request deletion at any time. We use encryption in transit (TLS 1.3) and at rest for all sensitive data.

Every statistic on this website comes from a named, verifiable source. We maintain a central database of 60+ statistics, each tagged with:

• The originating research organisation and publication URL
• The publication date
• A confidence level (high, medium, or low)
• Whether it is a projection or a verified historical fact
• Relevant caveats and context

An automated weekly process checks for stale data — any statistic not re-verified within six months is flagged for review. We would rather remove an outdated figure than mislead you with old data.