Retrieval-Augmented Generation connects an LLM to your approved knowledge base. The model receives relevant documents at answer time, reducing hallucinations and keeping responses aligned with your current business data.
A Large Language Model understands and generates language. We choose whether to use a commercial model, open-source model, or smaller task-specific model based on privacy, latency, cost, and accuracy needs.
Embeddings convert text, products, documents, or records into numeric meaning. They make semantic search possible, so the system can find related information even when the user does not use exact keywords.
Agent architectures define how a model plans, calls tools, reads data, asks follow-up questions, and hands off to humans. Good orchestration keeps the assistant useful without giving it unsafe autonomy.
Guardrails are rules around privacy, permissions, prompt injection, output format, escalation, and business policy. They prevent the model from exposing data, inventing actions, or acting outside its role.
AI quality must be tested with real examples. We define expected answers, edge cases, refusal behavior, latency, cost, and human review signals so the integration improves with evidence.
Retrieval-Augmented Generation connects an LLM to your approved knowledge base. The model receives relevant documents at answer time, reducing hallucinations and keeping responses aligned with your current business data.
A Large Language Model understands and generates language. We choose whether to use a commercial model, open-source model, or smaller task-specific model based on privacy, latency, cost, and accuracy needs.
Embeddings convert text, products, documents, or records into numeric meaning. They make semantic search possible, so the system can find related information even when the user does not use exact keywords.
Agent architectures define how a model plans, calls tools, reads data, asks follow-up questions, and hands off to humans. Good orchestration keeps the assistant useful without giving it unsafe autonomy.
Guardrails are rules around privacy, permissions, prompt injection, output format, escalation, and business policy. They prevent the model from exposing data, inventing actions, or acting outside its role.
AI quality must be tested with real examples. We define expected answers, edge cases, refusal behavior, latency, cost, and human review signals so the integration improves with evidence.
Retrieval-Augmented Generation connects an LLM to your approved knowledge base. The model receives relevant documents at answer time, reducing hallucinations and keeping responses aligned with your current business data.
A Large Language Model understands and generates language. We choose whether to use a commercial model, open-source model, or smaller task-specific model based on privacy, latency, cost, and accuracy needs.
Embeddings convert text, products, documents, or records into numeric meaning. They make semantic search possible, so the system can find related information even when the user does not use exact keywords.
Agent architectures define how a model plans, calls tools, reads data, asks follow-up questions, and hands off to humans. Good orchestration keeps the assistant useful without giving it unsafe autonomy.
Guardrails are rules around privacy, permissions, prompt injection, output format, escalation, and business policy. They prevent the model from exposing data, inventing actions, or acting outside its role.
AI quality must be tested with real examples. We define expected answers, edge cases, refusal behavior, latency, cost, and human review signals so the integration improves with evidence.