Cognitive Process Automation Company

RPA (Robotic Process Automation) follows fixed, pre-programmed rules using structured, predictable data - it performs exactly the same steps every time and fails when data does not match its expected format. Cognitive process automation uses AI to handle unstructured data and judgment-based decisions: reading invoices from any supplier without a template, classifying customer emails by intent, predicting which leads will convert, detecting fraudulent transactions from behavioural patterns. The key difference is adaptability - RPA rules are static and must be manually updated when processes change, while cognitive automation improves over time as it learns from new data and human corrections. Most mature automation programs use RPA for structured processes and cognitive automation for unstructured or judgment-intensive ones.

Intelligent Document Processing (IDP) uses AI - OCR combined with machine learning extraction models or large language models - to extract structured data from unstructured documents without requiring fixed templates. Traditional OCR extracts text character by character. IDP understands document semantics: it recognizes that 'Due Date', 'Payment Due', 'Pay By', and 'Balance Due Date' all refer to the same invoice field, even when they appear in different positions across documents from different suppliers. IDP systems achieve 95-99% extraction accuracy on common document types (invoices, purchase orders, bank statements) and improve over time through a human-in-the-loop feedback mechanism where human corrections on uncertain extractions are used to retrain the model.

Retrieval-Augmented Generation (RAG) is an AI architecture that allows a large language model (LLM) to answer questions from your specific documents and knowledge base - rather than from its generic training data. In RAG: (1) Your documents (policies, manuals, product specifications, FAQs) are split into chunks and converted to vector embeddings stored in a vector database. (2) When a user asks a question, the system retrieves the most semantically relevant document chunks from the vector database. (3) The retrieved chunks are provided as context to the LLM, which generates an answer grounded in your specific documents. RAG is used for HR policy chatbots, product knowledge bases, customer support AI, and any application where accuracy to your specific information matters more than general knowledge.

Yes. Data privacy is a critical consideration for many cognitive automation use cases - financial documents, medical records, legal contracts, and HR data contain sensitive information that cannot be sent to external AI APIs. Evolution Infosystem deploys AI models entirely on-premise: open-weight LLMs (Llama 3.1, Mistral, Qwen) run on your own GPU servers using frameworks like Ollama or vLLM, fine-tuned models are trained on your infrastructure and serve inference locally, vector databases (pgvector in PostgreSQL, Qdrant on-premise) store embeddings within your network, and the complete AI pipeline processes data without any external API calls. On-premise deployment is available for all cognitive automation services.

Human-in-the-loop (HITL) is an AI system design pattern where cases the AI is uncertain about are routed to a human reviewer rather than processed automatically with a potential error. Every cognitive automation system has a confidence score for each output - when the AI's confidence falls below a configured threshold (e.g., an invoice with an unusual format, a support ticket that does not fit any known category clearly), the case is queued for human review with the AI's best prediction pre-filled and highlighted. The human reviews, corrects if necessary, and confirms. These human corrections are fed back into the model training pipeline, making the AI more accurate over time. HITL is essential because production AI encounters cases outside its training distribution - the only question is whether those cases are handled gracefully (HITL) or fail silently (no HITL).

Cognitive automation performance is measured with task-specific metrics: IDP extraction systems are measured by field-level extraction accuracy (correct extractions / total extractions), precision (of fields extracted, what fraction are correct), and recall (of all fields present, what fraction are extracted). Classification systems (ticket routing, document classification) are measured by accuracy, precision, recall, F1-score, and confusion matrix analysis by class. Predictive models (churn prediction, lead scoring) are measured by AUC-ROC (overall discrimination ability), precision-recall at the operating threshold, and business metrics (precision at top-10% of scored leads). Generative AI systems (RAG chatbots) are measured by answer accuracy on a ground-truth evaluation set and human evaluation of response quality. Evolution Infosystem establishes performance benchmarks before deployment and measures against them continuously in production.

Intelligent Document Processing, NLP text analytics, predictive process automation, computer vision automation, conversational AI (RAG), decision intelligence, ML anomaly detection, and generative AI workflow automation.

Yes. Evolution Infosystem deploys open-weight LLMs (LLaMA 3.1, Mistral, Qwen) on-premise on GPU servers for data privacy compliance - sensitive documents never leave the client's network.

GPT-4o (OpenAI) and Gemini 1.5 Pro for cloud-based deployments; LLaMA 3.1 70B, Mistral 7B, and Qwen2 72B for on-premise deployments. Model selection based on task requirements and data privacy needs.

95%+ field-level extraction accuracy on production IDP deployments for common document types (invoices, purchase orders, bank statements) - with human-in-the-loop review for low-confidence extractions.

Yes. All predictive and classification models include SHAP (SHapley Additive exPlanations) values showing which input features drove each prediction - making AI decisions interpretable for users, auditors, and regulatory compliance.