AI Agents for E-commerce: Where They Pay Off First

In an online store, AI agents pay off first where the same work repeats hundreds of times: answering shoppers, product advice, tracking competitor prices, personal offers and product copy. In each of these processes the data already lives in your CRM, ledger or catalog, so the agent has something to rely on, and the manager keeps decisions and exceptions.

Below are five agents from the Initask catalog, examples from our retail projects, and where to start.

1. First-line chatbot

The first-line chatbot answers routine shopper questions and hands complex conversations to a manager with the full context.

• For: stores with 100 or more requests a day.
• Example: a shopper asks in Telegram about their order status. The agent pulls the status and tracking number from the ledger and replies in seconds.
• Catalog target: 60-70% less load on agents in month one, round-the-clock replies with no night shifts.

From our work: for a silk accessories brand we built an AI assistant in the site widget. It shows print galleries, answers "in stock" or "out of stock" by print, size and print type from the CRM data, recognises returning customers and brings the shopper to checkout, handing complex cases to a manager.

2. Personal shopper

The personal shopper asks about the occasion, budget, style and size and suggests a few options from what is in stock.

• For: stores with a wide range where shoppers get lost in the catalog.
• Catalog target: +15-25% conversion by month three, fewer returns thanks to a better first choice.

Next to the shopper sits a recommendation system. For a national electronics chain we built a recommendation system pilot in six weeks on catalog events, with metrics agreed before the start: CTR, conversion and an A/B comparison.

3. Market pricing

Dynamic pricing collects competitor prices daily, matches products and proposes a price change using a formula based on your rules.

• How margin is protected: the price comes from a formula with a minimum margin; the language model only collects and matches data. In the dynamic pricing demo the agent captured 2,315 competitor prices over 30 days and made 124 proposals, 24 of which the margin floor held back.
• Who decides: a manager approves each change with one click.

A related build from our work: for a bakery chain, a system checks the menus on Glovo and Bolt Food every hour for each location and alerts managers about items that cannot be ordered.

4. Personal offers

Personalized discounts and loyalty works out an offer for each customer from their order history.

• For: stores with a loyalty programme and 10,000 or more customers.
• Catalog target: +15-20% repeat purchases in month one.

5. Product copy and content

The content factory writes product descriptions, articles and posts from a brief and your brand voice, with SEO in mind.

• For: stores publishing dozens of pieces a week or adding thousands of new items.
• Catalog target: 70% less time on content in month one.

The five agents side by side

Catalog targets are the benchmark we take into a pilot. The actual result is measured on your store in the first month.

Where to start

1. Export a month of shopper messages and see which questions repeat most.
2. Check where the answers live: status and stock in the CRM or ledger, terms on the site.
3. Start with one agent on the most frequent question and one channel.
4. After a month, compare the share of conversations closed without a manager and the reply time with what you had before.

Describe your store and channels through the contact form, and we will reply with an estimate within one working day of the brief.