Retail and e-commerce operations generate massive amounts of text data: product descriptions, customer reviews, inventory records, supplier communications, pricing strategies, and marketing copy. Chinese LLMs like DeepSeek V4, GLM-4, and Qwen3 can process this data to automate content creation, analyze customer sentiment, optimize operations, and personalize shopping experiences. TokenEase's unified API gives retailers access to these powerful models at a fraction of typical AI costs.
Generate SEO-optimized, platform-specific product descriptions for thousands of SKUs with consistent brand voice.
import requests
def generate_product_description(product_specs, brand_voice, target_platform, seo_keywords):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": f"You are an e-commerce copywriter. Write compelling, {brand_voice} product descriptions optimized for {target_platform}. Include SEO keywords naturally. Highlight benefits, not just features."},
{"role": "user", "content": f"Keywords: {seo_keywords}\nSpecs:\n{product_specs}\n\nWrite product description."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
specs = """
Product: Wireless noise-canceling headphones
Battery: 40 hours with ANC on
Drivers: 40mm custom-tuned
Connectivity: Bluetooth 5.3, multipoint pairing
Weight: 250g
Colors: Black, Silver, Navy
Price: $249
"""
description = generate_product_description(specs, "premium and tech-savvy", "Amazon", "wireless headphones, noise canceling, bluetooth headphones")
Analyze thousands of reviews to identify product issues, emerging trends, and competitive opportunities.
def analyze_reviews(review_texts, product_category, competitor_products):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Analyze customer reviews to identify product strengths, weaknesses, feature requests, and sentiment trends. Compare against competitors where data is available."},
{"role": "user", "content": f"Competitors: {competitor_products}\nCategory: {product_category}\nReviews:\n{review_texts}\n\nAnalyze and provide insights."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
reviews = """
1. "Battery life is amazing but headband hurts after 2 hours."
2. "Sound quality rivals Bose at half the price."
3. "ANC is good but not as strong as Sony WH-1000XM5."
4. "Bluetooth connection drops sometimes in crowded areas."
5. "Love the multipoint pairing - works great with laptop and phone."
6. "Wish it came with a hard case instead of soft pouch."
"""
competitors = "Sony WH-1000XM5 ($399), Bose QC Ultra ($429), Apple AirPods Max ($549)"
insights = analyze_reviews(reviews, "premium wireless headphones", competitors)
Analyze market data, competitor pricing, and demand signals to recommend dynamic pricing adjustments.
def recommend_pricing(product_info, competitor_prices, demand_signals, margin_targets):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "glm-4-plus",
"messages": [
{"role": "system", "content": "Recommend pricing strategies for retail products. Consider competitive positioning, demand elasticity, margin requirements, and promotional opportunities."},
{"role": "user", "content": f"Margins: {margin_targets}\nDemand: {demand_signals}\nCompetitors: {competitor_prices}\nProduct: {product_info}\n\nRecommend pricing strategy."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
product = "Smart home security camera, 2K resolution, AI person detection, local storage"
comp_prices = "Ring Indoor Cam: $59.99, Nest Cam: $99.99, Arlo Essential: $129.99"
demand = "Search volume up 35% MoM. Social media mentions trending. Back-to-school season approaching."
margins = "Target gross margin 45%. Current COGS $38. Current price $89.99."
pricing = recommend_pricing(product, comp_prices, demand, margins)
Let staff query inventory using natural language and receive intelligent reorder recommendations based on sales velocity and seasonality.
def inventory_query(question, inventory_data, sales_history):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "kimi-k2",
"messages": [
{"role": "system", "content": "Answer inventory questions and recommend reorder quantities based on sales velocity, lead times, and seasonality. Be specific with numbers."},
{"role": "user", "content": f"Sales history: {sales_history}\nInventory:\n{inventory_data}\nQuestion: {question}"}
]
}
)
return response.json()["choices"][0]["message"]["content"]
inventory = """
SKU-001: Running shoes, size 9, Black - Current: 45 units, Reorder point: 30, Lead time: 14 days
SKU-002: Running shoes, size 9, White - Current: 12 units, Reorder point: 30, Lead time: 14 days
SKU-003: Running shoes, size 10, Black - Current: 78 units, Reorder point: 35, Lead time: 14 days
"""
sales = "SKU-001: 120 units/month (avg), peak 180 in March. SKU-002: 95 units/month, trending up. SKU-003: 85 units/month, stable."
answer = inventory_query("Which SKUs need reordering this week and how many should I order?", inventory, sales)
Analyze return reasons and customer complaints to identify product quality issues and process improvements.
def analyze_returns_complaints(returns_data, complaint_tickets, product_line):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "Analyze returns and complaints data. Identify root causes, quantify impact, and recommend product improvements or process changes."},
{"role": "user", "content": f"Product line: {product_line}\nReturns:\n{returns_data}\nComplaints:\n{complaint_tickets}\n\nAnalyze and recommend."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
returns = """
July: 234 returns (4.2% rate). Top reasons: Size too small (28%), Defective zipper (22%), Color mismatch (18%)
August: 198 returns (3.8% rate). Top reasons: Size too small (31%), Defective zipper (19%), Not as described (15%)
"""
complaints = "Support tickets: 45 about zipper failure after 2-3 weeks. 30 about sizing running small. 12 about color fading after first wash."
line = "Women's casual jackets, $79-99 price range, 12 SKUs"
analysis = analyze_returns_complaints(returns, complaints, line)
Generate consistent marketing content adapted for each channel: email, social media, SMS, in-store signage, and web.
def generate_omnichannel_content(campaign_brief, channels, brand_guidelines):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Generate omnichannel marketing content. Maintain brand consistency while optimizing for each channel's format, audience, and best practices."},
{"role": "user", "content": f"Guidelines: {brand_guidelines}\nChannels: {channels}\nCampaign:\n{campaign_brief}\n\nGenerate content for each channel."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
campaign = "Back-to-school sale: 25% off all backpacks and laptops. Free shipping over $50. Valid Aug 15-31."
channels = ["Email subject + preview", "Instagram caption", "SMS (160 chars max)", "In-store signage headline", "Website hero banner"]
guidelines = "Brand voice: Friendly, helpful, not pushy. Avoid exclamation marks. Emphasize value, not discount percentage."
content = generate_omnichannel_content(campaign, channels, guidelines)
Get $1 free credits (1M tokens) to automate product content and analyze customer feedback.
Start with TokenEase →