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A plain-English interface built on Next.js lets analysts upload files and ask questions directly. Answers arrive in minutes instead of days, lifting analyst productivity by approximately 35%.
Snowflake runs all SQL inside the client’s warehouse, and Neo4j stores unstructured data as a knowledge graph. Raw data never leaves the client’s environment, eliminating the privacy risks of external tools.
Budget, supply, and campaign numbers that previously took two days to compile are now surfaced in under ten minutes, accelerating decision-making across the organization.
Every file upload logs file size, owner, and processing status, creating a complete audit trail that supports compliance reviews without any manual record-keeping.
The client, a leading e-commerce platform for health and wellness serving 12M+ active customers across 180+ countries with over 50,000 products, wanted faster answers from their data without risking data privacy. The existing workflow created three core operational bottlenecks:
Analysts sifted through scattered files across multiple systems and formats, losing hours on search and copy-paste. Critical information was fragmented across PDFs, spreadsheets, and documents with no unified way to query it.
Stakeholders waited days for budget, supply, and campaign numbers, delaying action on time-sensitive decisions. The manual process of compiling insights from disparate sources created persistent bottlenecks in the analytics workflow.
Sharing raw data with outside tools threatened sensitive sales figures and customer information. The organization needed a solution that could deliver AI-powered insights without exposing proprietary data to third-party environments.
Unstructured data sitting in documents and files had no way to surface connections between products, campaigns, and suppliers. Without a knowledge layer, analysts could not explore relationships or discover hidden patterns across the data.
Inferenz delivered an AI assistant that speaks and responds in plain English yet keeps data locked inside the client’s cloud. The solution combines a natural-language interface, a knowledge graph, and a secure warehouse layer to turn scattered documents and structured tables into on-demand, privacy-safe insights.
Natural-Language Chat Interface
A Next.js web and mobile UI lets users upload files and ask questions in plain English. The AI assistant interprets natural-language queries, routes them to the appropriate data layer, and returns answers directly in the conversation, eliminating the need for analysts to write SQL or navigate complex reporting tools.
Knowledge Graph Integration
Unstructured data from uploaded documents is stored in Neo4j as a knowledge graph, enabling relationship-based querying across products, suppliers, campaigns, and sales data. This allows analysts to explore connections and surface insights that flat-file searches would miss.
Secure Query Layer
Snowflake runs all SQL queries inside the client’s warehouse so raw data never leaves the Snowflake environment. This architecture ensures that sensitive sales figures and customer data remain fully protected while still powering real-time, AI-driven insights.
File Upload & Processing
The system supports PDF, Excel, Docx, and other formats, processing uploaded files without altering original content. Documents are parsed, indexed, and made queryable through the natural-language interface and the Neo4j knowledge graph.
Structured Data Warehouse
Snowflake houses structured tables for quick joins across budget, supply chain, and campaign data. This complements the unstructured knowledge graph layer, giving analysts a unified view across both structured and unstructured information.
Full Audit Trail
Each file upload logs file size, owner, and processing status, creating a compliance-ready record of all data interactions. This audit trail supports internal governance reviews without requiring any manual tracking or additional tooling.
Answers arrive in minutes instead of days, freeing analysts from manual search and copy-paste.
Budget, supply, and campaign numbers now surfaced in under ten minutes, speeding critical decisions.
Faster treatment decisions cut critical-care time and free up beds with on-demand data visibility.
Raw data never leaves the client’s Snowflake environment, eliminating third-party privacy risk.
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