Retail & E-commerce
Analytics
How an international retail brand reduced weekly report preparation from 6 hours to 3 seconds by implementing local on-the-fly data processing.
Case Summary
- What it is
- Web application for retail sales analytics
- Client
- International premium textile brand
- Result
- Report in 3 seconds instead of 4–6 hours of manual work
- Technologies
- React 19, TypeScript, XLSX streaming, Google Gemini Pro
- Security
- Client-side — data never leaves the browser
- Launch time
- from 3 weeks
The Old Approach Barrier
Analytics turned into routine. Hundreds of tables, human error, and inevitable delays.
4–6 hours of routine weekly
A brand with a wide range of premium textile products had raw, fragmented data in exports from internal systems. Analysts assembled reports manually.
The result: constant VLOOKUP searches, aligning tens of thousands of rows, and inevitable 'broken' names distorting the metrics of the entire department.
Analysts
- Manual data cleaning
- Risk of SKU typos
- 4–6 hours per report
Management
- Late management decisions
- No trust in data accuracy
- Data delay up to 7 days
Local Solution
Web application for automatic sales processing. Secure streaming from Master Data directly in the browser, without sending to a backend.
Surgical SKU parsing
Recognizes 26 SKU attributes from raw tables. Normalizes colors, sizes, and automatically translates collections RU → EN on the fly.
Strict Protection
Multi-factor logic. For example: a 'Rug' with parameters of a 'baby crib' automatically goes to the Kids category.
Fuzzy Matching
Complex fuzzy name matching against the master catalog (99% accuracy) to prevent duplicates and report losses.
AI Sales Summarization
LLM integration to generate text summaries of key insights: anomalous drops, bestsellers, and returns as text for the CEO.
"The hardest part was teaching the system to 'think' like a category manager. We wrote more than 900 lines of logic just to handle the nuances of the product catalog, so the system would understand the difference between a 'Decorative Pillowcase' and a 'Sleeping Pillowcase' better than the Excel user himself."
Deep Dive
into the System Interface
1 Data Upload
The data file is loaded into the browser. The system instantly confirms the start of data processing and validates the file.
2 Smart Categorization
Metrics summary recalculates instantly: from total sales and conversion to category anomalies.
3 Analytics & AI Insights
Ready report. The engine processes 10,000+ rows in less than 2 seconds directly in the browser. Elimination of human error.
Key Advantages
We focused on speed, autonomy, and security (Zero Trust Data).
Ultra-high speed
10,000+ rows of data processed in less than 2 seconds directly in the browser. You no longer wait for reports to load — everything works instantly.
Client-Side
All processing is done on the client side. Files with commercial data never leave your browser.
Luxury UX
Interface with a strict premium design system with instant data validation (Instant Feedback).
900+ lines of protective logic
The system determines the product category on its own, relying not only on the direct name, but also on hidden indirect signs from 26 SKU attributes, guaranteeing 100% accuracy.
Technologies Under the Hood
React
TypeScript
Tailwind CSS
Implementation Results
For Analysts
- Report time: from 4–6 hours → 3 seconds
- Processing 10,000+ rows: less than 2 seconds
- Manual VLOOKUP formats: completely eliminated
- Nomenclature conflicts: detected automatically
For Management
- Sales report ready immediately after database export
- Data accuracy: 900+ lines of protective logic
- SKU-level detail: without additional analyst requests
- AI Insights (Executive Summary): generated by AI
Your sales analytics should be
just as autonomous.
- Integration of corporate data (XML/CSV/XLS)
- Confidentiality preservation (Client-Side)
- AI adaptation to your SKU catalog
- Minimization of analysts' routine work
Launch time — from 3 weeks. Custom built for your needs.