Let Your Questions
Clarify.

BI tools like Tableau and Power BI are optimized for visualizing predefined metrics on dashboards designed in advance. Qurify, by contrast, lets you query and analyze data instantly with natural language — no upfront design required. When a new question arises that isn't on any dashboard, business users find the answer themselves instead of filing a request with IT. Qurify also searches unstructured data (documents, manuals, reports) alongside structured databases, delivering analysis that combines the numbers with their context. It complements rather than replaces BI, covering the ad-hoc analysis and document-driven insights that BI tools can't reach.

Many ERP products now ship with AI features, but most can only answer questions about the data on the currently open screen. For example, to trace why operating profit increased while viewing the income statement, you'd have to manually open the revenue-by-customer screen, margin-by-product screen, and journal detail screens one by one. Qurify's ontology connects data across tables and screens at the semantic level, so a single natural language question performs cross-analysis and drill-down. It doesn't replace your ERP — it adds an intelligence layer on top, so all core ERP functions stay exactly as they are.

Today, Qurify is delivered as an independent web interface separate from your ERP. It connects to the ERP database to query data but isn't embedded inside ERP screens. This is a deliberate design: as an independent system it isn't locked to any particular ERP, and it can connect multiple systems simultaneously (ERP + CRM + document storage) for cross-analysis. Based on PoCs and production experience, we are also exploring embedding Qurify inside ERP screens, with the ultimate goal of bringing natural language analysis into whatever environment users know best.

Qurify builds a production environment in 3–5 weeks through a standard 5-phase delivery process: week 1 for environment setup and data source connection, week 2 for ontology design and data indexing, week 3 for AI tuning and quality verification, week 4 for pilot operation and feedback, and week 5 for launch and stabilization. Work that takes 6+ months to develop in-house is dramatically compressed with our proven framework. Timelines adjust for project scale and data complexity, and we finalize the detailed schedule together at kickoff.

Qurify's NL2SQL engine supports major relational databases including PostgreSQL, MySQL, MariaDB, and MSSQL, as well as cloud data warehouses such as Databricks, Snowflake, and Amazon Redshift. The hybrid RAG engine handles diverse document formats including PDF, Word, Excel, PowerPoint, and HWP, ingesting data via file upload or storage integration. Supported databases and formats are continuously expanding, and specialized data sources can be connected through custom MCP connectors.

Qurify has a multi-layered system for preventing and correcting errors. First, every answer includes its sources (original document location or the SQL query), so users can verify results directly. Second, the LLM Judge evaluation system scores each answer's accuracy, evidence fidelity, and completeness in real time — low-confidence answers are flagged with a warning and administrators are notified. Third, user feedback (thumbs up/down) continuously improves the ontology and prompts. Fourth, an admin dashboard tracks accuracy trends by query and analyzes recurring error patterns for proactive fixes. This closed-loop structure means answer quality improves the more the system is used.

Both options offer identical functionality — choose based on your security policies and operating environment. SaaS requires no infrastructure and is ready immediately, with SmartMind AI handling all updates and maintenance for minimal operational burden; it suits companies that need fast adoption and flexible scaling. On-Premise installs on your own servers or private cloud so data never leaves your network; we recommend it for organizations in regulated industries (finance, public sector, healthcare) or with internal policies restricting external cloud use. Hybrid configurations (core data on-premise, general documents on SaaS) are also possible — we'll design the optimal setup together during consultation.

Qurify provides enterprise-grade security. All data is encrypted with AES-256, both at rest and in transit. Role-based access control (RBAC) finely regulates which collections and data ranges each user, department, or rank can access. Personally identifiable information (PII) is automatically detected and masked before AI answers are generated, with masking policies managed centrally via MCP. All queries, responses, and data access are recorded in an audit trail for internal audits and compliance reporting. SSO/LDAP integration applies your existing authentication as-is, and on-premise deployment supports fully closed-network operation.

Yes. Qurify's MCP (Model Context Protocol) based architecture integrates with a wide range of internal systems in a standardized way — major ERP/CRM platforms like SAP, Oracle ERP, and Salesforce, plus groupware, internal wikis, and file servers. Data sources are added simply by configuring an MCP connector, no custom API development needed, minimizing integration time and cost. REST APIs and webhooks are also supported for flexible integration with systems that don't support MCP. We design the integration scope and approach around your environment during consultation.

No. Attempting a perfect company-wide knowledge graph from day one makes verification impossible and lets errors accumulate — it's actually a shortcut to failure. Qurify builds in stages under the principle that simpler is better: first mapping the core entities of your structured data to standard terminology (Levels 1–2), then integrating unstructured data like contracts and emails once the value is proven (Level 3), and moving toward self-expansion (Levels 4–5) after that. Each level goes through 'ping-pong' verification — a back-and-forth on semantic definitions with your team — and is only promoted once fully validated, so you control risk while seeing results fast. And the highest level isn't always the right answer; we choose the appropriate level for your purpose together.

Qurify's core is data querying and analysis, but on top of the ontology, AI agents can be extended to connect and execute actual business procedures. It works in four steps: Interpret → Verify → Execute → Approve. AI interprets unstructured inputs (emails, PDFs); the ontology verifies facts (registered vendor, contract price match); and work that must not go wrong — like creating journal entries or sending notifications — is executed precisely through idempotent, auditable workflows. High-risk items above a threshold are never auto-processed; they stop at a human approval gate (HITL, Human-in-the-Loop). AI assists with ambiguous judgment, but humans keep final control over decisions where money moves — capturing automation's efficiency and safety at the same time.