iNetiV

Cases

Task scenarios and business effect.

Typical practical requests we get: e-commerce, retail, customer service, accounting loops and AI automation.

PythonFastAPIPostgreSQLRedis

Call transcription and speech analytics

Task

Sales and support teams collect thousands of calls a week. Manual spot-listening covers only 3–5% of dialogues — conflicts, lost leads and process breaches stay unseen.

Result

100% of inbound and outbound calls are transcribed and scored by AI: key agreements land in CRM, while critical cases and a digest go to the manager by email and dashboard.

Headset and a laptop with a speech-analytics dashboard
PythonFastAPIPostgreSQLTransformers

Corporate knowledge base and RAG search

Task

Hundreds of policies, contracts and PDF guides sit in different folders. Finding the right clause takes 15–20 minutes, and senior specialists spend up to a third of the day answering the same questions.

Result

Intelligent search finds the exact clause in 2–3 seconds with a direct quote from the source, freeing experts from 80% of routine questions.

Documents next to a laptop with knowledge-base search
PythonFastAPIMCPRedis

AI agent for customer support

Task

60–70% of support tickets are repeats: order status, return rules, delivery hours. Operators are overloaded at peak, and clients wait 30–40 minutes for a first reply.

Result

First response drops from 40 minutes to 5 seconds: typical requests are closed instantly 24/7, and financial operations go to an operator as a one-click review.

Laptop and phone with an AI support chat
PythonFastAPIPostgreSQLOpenCV

Automatic invoice capture

Task

Accounting types data from hundreds of inbound PDF invoices into ERP by hand. Entering details, VAT rates and totals takes 3–4 hours a day, and typos delay payment.

Result

The system reads an invoice of any layout in 4 seconds, matches amounts to the purchase order and posts it in ERP: manual entry drops by 85%, and the accountant checks only exceptions.

Invoices on a desk and a laptop parsing a bill
Next.jsTypeScriptPostgreSQLRedis

Online store with ERP and logistics

Task

At 100–300 orders a day, manual transfer of payments, statuses and addresses between site, warehouse and couriers takes up to 5 hours a day, causing late shipments and sales of missing stock.

Result

Payment instantly reserves stock in ERP, the warehouse gets a pick list in 30 seconds, and the tracking number is emailed to the buyer and shown in the account with no manager involved.

Laptop with a storefront and a shipping box
PythonFastAPIPostgreSQLRedis

ERP, site and marketplace sync

Task

Selling on your own store and several marketplaces at once, stock changes every minute. Manual price and stock edits across cabinets lead to overselling and cancellation penalties.

Result

Two-way API exchange every 60 seconds: a sold item is deducted on all channels and the site, inbound orders land in one ERP view, and cancellation risk is near zero.

Several screens with stock synchronization
Next.jsTypeScriptPostgreSQLFastAPI

B2B portal for dealers and wholesale clients

Task

Wholesale partners ask for custom price lists by email, negotiate discounts for days and send orders as table scans or messenger files, loading managers with routine invoicing.

Result

The partner sees contract prices, credit limit and live stock 24/7. Repeat wholesale orders drop from a day to 3 minutes with no manager in the loop.

Laptop and tablet with a wholesale client portal
PythonFastAPIPostgreSQLTransformers

Semantic catalog search

Task

In a catalog of 20,000+ SKUs, up to 35% of queries contain typos, slang or vague parameters. Standard search returned “Nothing found”, losing up to 15% of potential purchases.

Result

Vector semantic search understands intent even with messy wording and returns a relevant set in 80 ms. Search drop-off falls 60%, and search-to-cart conversion rises 25%.

Laptop with catalog search and product suggestions
PythonFastAPIPostgreSQLRedis

Automatic inbound lead qualification

Task

Sales gets dozens of requests a day from different channels. Up to 50% of contacts are a poor fit, while managers spend hours qualifying and real buyers wait and leave.

Result

AI analyzes the request in 15 seconds, detects industry, budget and urgency. A CRM card is created with a brief, and a hot-lead alert goes to the on-duty manager.

CRM on a laptop and a hot-lead notification on a phone
PythonPyTorchPostgreSQLRedis

Demand forecasting and auto-replenishment

Task

Purchasing is averaged or done by gut feel: popular items run out before the weekend, and 15–20% of working capital sits in frozen slow stock.

Result

The model calculates replenishment with seasonality, promotions and sell-through per SKU 14 days ahead, cutting shelf gaps by 70% and freeing frozen cash.

Demand forecast chart on a laptop next to warehouse boxes
PythonFastAPIPostgreSQLRedis

Public price and assortment monitoring

Task

Staff spend up to 12 hours a week walking competitor sites and copying prices into sheets that are already stale the next day after a promo or FX move.

Result

A daily automated snapshot of prices and availability for 1,500 key SKUs lands in email and a dashboard, so marketers can adjust their price grid the same morning.

Price-comparison dashboard on a monitor and a laptop

Next step

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Cases · iNetiV