Frequently Asked Questions

A standalone GenAI / LLM can generate useful responses, but production workflows require more than prompts: system integrations, deterministic rules, error handling, security controls, monitoring, and audit trails. Qore provides the orchestration and governance layer that makes AI-driven workflows repeatable, controlled, and operational at enterprise scale.

Many enterprise systems and third-party portals (registries, sanctions sources, internal tools) either lack APIs or require UI-based access. Robotic Process Automation (RPA) allows agents to securely perform real actions—log in, navigate screens, extract records, download files—while maintaining consistent steps and traceability.
By combining RPA + APIs + GenAI, Qore agents can pull live system-of-record data at runtime, then interpret and act on it—rather than relying on static context or manual copy-paste.

High accuracy comes from layered controls, not GenAI alone:

• Deterministic validation and matching rules
• Normalization and enrichment using reference datasets
• Human-in-the-loop review for edge cases or high-risk decisions
• Monitoring, QA/QC, and regression checks when prompts, models, or data sources change

This combination supports consistent outcomes in data quality, screening, and compliance workflows.

Building agentic AI internally typically becomes a long-term engineering program: you must design an agent framework, integrate LLMs, RPA, ETL/data pipelines, security, monitoring, and user interfaces—then operate and maintain them.
Qore provides a ready-made agent platform and proven workflow patterns (e.g., restricted party screening, CRM data quality, investigations) so your teams focus on business rules and outcomes—not infrastructure and maintenance.

Qore is designed for data-intensive, rule-driven workflows where outputs must be explainable and auditable, such as:

• Trade compliance and restricted party screening
• CRM data cleansing, deduplication, and hierarchy management
• Master data management (MDM) and entity resolution
• Finance and back-office operations (reconciliations, checks, validations)
• Investigations and research workflows that rely on portals, spreadsheets, and email

Qore agents are built with explicit exception handling, including retries, fallback paths, and controlled escalation when confidence is low or a system is unavailable. Every run generates structured logs so teams can trace what happened, which data was used, and why a decision or outcome was produced.

You do not need to become prompt engineers. We collect requirements through a structured process template covering:

• Triggers and inputs
• Steps, rules, and decision criteria
• Systems to access (CRM/ERP/portals/files)
• Outputs, audit requirements, and exception rules

With sample data and acceptance criteria, Qore configures integrations, prompts, workflows, and testing—then delivers an agent for UAT and production rollout.

Qore is built on Microsoft Azure with enterprise security controls such as role-based access control (RBAC), encryption, and detailed audit logging. Data is isolated per customer and used only to execute your approved workflows.
Where required, Qore can support deployment models that align with customer policies (e.g., controlled access, tenant-based deployment patterns) and we work with security and compliance teams to review architecture, data flows, and governance before go-live.