What is the difference between AI automation and RPA?
Robotic Process Automation (RPA) automates deterministic, rules-based digital tasks clicking buttons, copying data between systems, filling forms where the steps are always the same and the data is always structured. RPA tools (UiPath, Blue Prism, Automation Anywhere) record and replay UI interactions. AI automation handles tasks involving unstructured content or judgment: reading and extracting data from documents that are not in a fixed template, categorising inbound emails by intent, generating summaries of variable-length content, or making decisions that depend on context rather than fixed rules. The technologies are complementary many automation architectures use RPA for the structured, deterministic steps and AI (LLMs) for the unstructured or judgment-requiring steps.
How accurate is AI document processing?
AI document processing accuracy depends on document type and structure. Standardised forms (government forms, insurance forms, tax documents with fixed layouts) achieve 97-99% field extraction accuracy. Semi-structured documents (invoices from recurring vendors consistent but variable layout) achieve 93-97% accuracy after a learning period. Unstructured documents (contracts, emails, legal briefs) achieve 85-92% accuracy for key field extraction. ClickMasters designs all IDP systems with human-in-the-loop review: a confidence threshold is set per field type, and extractions below the threshold are queued for human review rather than processed automatically. This means the overall system accuracy for downstream processes is near-perfect AI handles the high-confidence majority, humans handle the uncertain minority.
How do you ensure AI automation decisions are auditable?
Every AI-automated decision in a ClickMasters system is logged with: the input data (document content, email text, or structured data the AI processed), the LLM prompt used (the exact instructions given to the model), the model's response (full output before parsing), the parsed structured output (the extracted fields or decision), the confidence score, and whether the decision was auto-applied or routed for human review. This audit trail is stored in PostgreSQL and retained per the client's data retention policy. For regulated industries (financial services, healthcare, insurance), the audit log includes the human reviewer's identity and review timestamp for every case that required human oversight. ClickMasters can provide the audit log in formats compatible with specific compliance frameworks on request.
Which AI models does ClickMasters use for automation?
ClickMasters selects AI models based on the specific requirements of each automation task. GPT-4o (OpenAI) is the primary model for document extraction its structured output (JSON mode with function calling) and instruction-following accuracy make it the most reliable for extracting specific fields from documents. Claude 3.5 Sonnet (Anthropic) is used for long-document tasks (contracts, reports) where the larger context window and strong reasoning are advantageous. For latency-sensitive applications (real-time API responses under 500ms), GPT-4o mini or Claude 3.5 Haiku provide significantly lower latency at lower cost, with accuracy sufficient for classification and routing tasks. Model selection is documented in every engagement clients can see which model is used for which task and why.
