✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount

AI chatbot for Size and Fit: recommend the right size in chat

SleekAI pulls per product size charts from postmeta, fit notes from product descriptions, and return reasons from past orders, then asks two or three questions on your own OpenAI, Anthropic, Google, or OpenRouter key to recommend a confident size.

♾️ Lifetime License available

SleekAI chatbot for Size and Fit Recommendations

Sizing returns are the most expensive guess in apparel

Roughly 30 percent of online apparel orders come back, and the leading reason is sizing. Customers order two sizes to try, ship one back, and the round trip eats your shipping margin and your packaging. They are not lazy. They genuinely do not know if a brand runs small or large, and your product page does not tell them in a way they trust.

SleekAI fixes the trust gap in chat. The bot reads your per product size chart from postmeta keys like _size_chart, fit notes from the product description, and past return reasons from wp_wc_orders meta. It asks two or three questions, height, usual brand and size, fit preference, and recommends a size with a real explanation. Not a banner, an actual reply that says you usually wear medium in Brand X but our cut runs slim so try a large.

Generic bots cannot do this. They do not know what your size chart says, they do not know that your size 12 fits like a Zara 10, and they have no return data to anchor their recommendation. SleekAI grounds every reply in your actual product data, so the recommendation is specific and the shopper buys one size instead of two.

Workflow

How size and fit advice runs in your store

1

Connect product sizing data

Map your size chart postmeta key, fit type field, and any custom notes to SleekAI variables. The bot now reads the right chart and fit story for each product without you copying anything into the instruction.
2

Write brand benchmark notes

List the three or four common brands shoppers compare to and how your sizes translate. This handles eighty percent of recommendations because shoppers always anchor to a brand they already wear.
3

Enable on product pages

Use display conditions to show the bot only on product pages, with the current product passed in. Now the conversation starts with the bot already knowing what the shopper is looking at.
4

Watch the returns trend

Open the SleekAI log a few weeks in and compare return rates on bot assisted orders versus the baseline. Refine the instruction or fit notes for products that still come back, and the gap will widen.

Try it now

A typical size and fit conversation

Shopper considers a fitted dress, gives her measurements and usual brand, and gets a confident size recommendation tied to the product's actual cut.

Comparison

Generic chatbot vs SleekAI for size and fit

Generic chatbot

  • Does not know your size chart or how your cuts compare to other brands
  • Cannot read fit notes buried in the product description or postmeta
  • Has no return reason data to learn which products run small or large
  • Gives the same generic sizing advice on a bodycon dress and a relaxed jacket
  • Will not factor the shopper's past purchases on your store into the answer

SleekAI chatbot

  • Reads per product size charts from _size_chart or your own postmeta key
  • Pulls fit notes from product description and short description fields
  • Cross references return reasons stored in order meta to flag risky sizes
  • Compares your sizing to common brands when the shopper names a benchmark
  • Remembers logged in customers' previous purchases to anchor the next recommendation

Features

What SleekAI gives you for Size and Fit Recommendations

Per product fit awareness

Each product can have its own size chart, fit type, and notes. The bot picks the right chart based on which product page or product the shopper is asking about, so a recommendation on a fitted dress differs from a relaxed coat.

Brand benchmark recognition

Shoppers say I usually wear medium in Brand X. The bot uses brand notes you provide in the instruction to translate that benchmark into your own size, instead of guessing or asking for measurements every time.

Return reason feedback loop

If a size routinely comes back as too small, the bot learns to nudge shoppers up. You feed it the return tag from order meta and the instruction adjusts recommendations in the weeks that follow.

Use cases

Where this chatbot earns its keep

Fashion and apparel

Dresses, jeans, and outerwear where cut varies sharply between styles. The bot keeps each product's fit story straight and reduces the buy two return one habit.

Footwear

Half sizes, narrow versus regular, and brand to brand differences. Shoppers give foot length and usual brand, the bot maps to your fit and width.

Performance and sportswear

Compression versus regular fit, body type questions, and material stretch all factor in. The bot asks what the gear is for and recommends accordingly.

The bigger picture

Why sizing advice belongs in chat

Sizing is the single biggest preventable cost in online apparel. Returns eat margin, packaging, and shipping, and the second time a customer ships back a size 10 they do not order from you again. A static size chart cannot stop this because shoppers do not know how to read it against their body and your specific cut.

They guess, they order two, and your margin pays for the round trip. Chat closes the gap because it asks two questions and gives one answer. The questions are the same ones a thoughtful sales associate would ask in a store.

What do you usually wear, what do you want it to look like, what is your height. The answers vary by product, so the bot needs the product's actual cut and chart, which is exactly what SleekAI reads from your data. The win shows up in two places.

Conversion goes up because shoppers who were going to order two sizes now order one with confidence. Returns go down because the size they ordered fits. The combined effect on apparel margin is dramatic, and it compounds because customers who get the right size the first time come back.

A bot that knows your size chart is doing the same job a great fitting room used to do, and it does it twenty four hours a day across thousands of conversations.

Questions

Common questions about SleekAI for Size and Fit Recommendations

You store the chart in product postmeta or in a custom field plugin like ACF and map it into the SleekAI variables panel. Each product can have its own chart, and the bot will pull the right one based on the product the conversation is about, whether the shopper is on the product page or names it.

 

Not always. If the shopper names a benchmark brand and size, the bot can map that to your sizing using brand notes in the instruction. If the shopper gives height and weight or actual measurements, the bot prefers those because they are more precise.

 

Yes, with a manual step. You expose the return reason field from order meta, and you periodically update the instruction or the per product fit notes when a pattern emerges. SleekAI does not retrain itself, but it picks up the new instructions immediately.

 

You decide the policy in the instruction. Most stores default to size up for fitted styles and size down for relaxed styles, with the bot explaining the reason. The customer leaves with a recommendation and the reasoning, not just a number.

 

Yes. The model detects the language and replies in kind. Brand benchmarks and size charts translate cleanly because the bot uses the underlying numbers regardless of the language the customer is typing in.

 

A modal shows a static chart. The bot interprets the chart against the shopper's specifics, accounts for the product's cut, and answers the actual question. It also remembers what was said earlier in the conversation, so it can refine the answer when new info arrives.

 

Yes. SleekAI reads from any postmeta, ACF field, or custom table you point it at. The size chart could live anywhere as long as you can map the field. WooCommerce is just the most common host for product data.

 

Reasoning improves with stronger models. GPT 4 class, Claude Sonnet, and Gemini Pro all produce solid size advice when given clear charts and fit notes. Cheaper models work too if the instruction is tight, but they may hesitate on complex between size cases.

 

Pricing

More than 1000+
happy customers

Explore our flexible licensing options tailored to your needs. Upgrade your license anytime to access more features, or opt for a lifetime license for ongoing value, including lifetime updates and lifetime support. Our hassle-free upgrade process ensures that our platform can grow with you, starting from whichever plan you choose.

Starter

€79

EUR

per year

  • 3 websites
  • 1 year of updates
  • 1 year of support

Pro

€149

EUR

per year

  • Unlimited websites
  • 1 year of updates
  • 1 year of support

Lifetime ♾️

Most popular

€249

EUR

once

  • Unlimited websites
  • Lifetime updates
  • Lifetime support

...or get the Bundle Deal
and save €250 🎁

The Bundle (unlimited sites)

Pay once, own it forever

Elevate your WordPress site with our exclusive plugin bundle that includes all of our premium plugins in one package. Enjoy lifetime updates and lifetime support. Save significantly compared to buying plugins individually.

What’s included

  • SleekAI

  • SleekByte

  • SleekMotion

  • SleekPixel

  • SleekRank

  • SleekView