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 connects data across tables and screens, along with related documents, by business terms, 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.

Inquiry, then understanding the work and the data, then about a month of setup, then operation. No one designs the ontology by hand; Qurify learns as it is used. You choose between subscription (SaaS) and on-premise. Tell us one task you want solved and where the data lives and we reply within 2 business days. We learn the questions your teams keep asking, where the data lives, and your closed-network and security requirements, then set up. After setup you run it as a subscription or on-premise, and your FDE keeps adding agents for the work you want covered. Deliverables and criteria for each stage are agreed before we start.

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, thumbs up/down ratings are used for improvement. 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.

On your servers or in your cloud, where it already is. We connect to your ERP, business databases, and document stores without moving the data to us. We also build and operate on closed networks with no external connection; one system runs inside a government office building today. Where security policy restricts external API calls, we run an open-source LLM on your own GPU servers.

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.

Yes. Qurify connects to your databases where they are and queries the data it needs at question time. Documents such as PDF, HWP, Word, and Excel are collected by upload or storage integration and then searched. Each answer shows the document location and SQL behind it, and separates what the data confirms from what it does not, including which data is missing for verification. Rather than connecting all company data at once, we start with one task and the data it needs, and expand from there.

At Qurify's center are evidence-based answers and their verification, and it can be extended to support decisions within business procedures. It works in four steps: Interpret → Verify → Process → Approve. AI reads and organizes unstructured inputs such as emails and PDFs; checks like registered vendor or contract price match run against connected data, with unconfirmed items flagged. Journal entries and notifications go through workflows the customer defines, and high-risk items above a threshold stop at a human approval gate (HITL, Human-in-the-Loop). AI organizes the evidence to support the decision; staff make the final call.