Use case

LLM API for E-commerce & Retail

Generate product descriptions at scale, personalize shopping experiences, and automate customer support with intelligent model routing that optimizes for both quality and cost across millions of SKUs.

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You only pay credits per request. No monthly subscription. Paid credits never expire.

Replace multiple AI subscriptions with one wallet that includes routing, failover, and optimization.

First success in 60 seconds
Step 01Sign up in 10 secondsGet 40 free creditsStep 02Open your dashboardCreate API keyStep 03Send first requestRun a sample
Why teams start here first
No monthly subscription
Pay-as-you-go credits
Start with trial credits, then buy only what you consume.
Failover safety
Production-ready routing
Auto fallback across providers when latency, quality, or reliability changes.
Data control
Your policy, your choice
BYOK and zero-retention mode keep training and storage scope explicit.
Single API experience
One key, multi-provider access
Use Chat/Compare/Blend/Judge/Failover from one dashboard.
Common problem
Generating unique, SEO-optimized product descriptions for thousands or millions of SKUs requires high-volume LLM throughput, and using a single expensive model for every description makes the project economically unviable.
Common problem
Customer support automation for e-commerce must handle everything from simple order status queries to complex return negotiations, and a single model either overspends on easy questions or frustrates customers on hard ones.
Common problem
Seasonal traffic spikes during sales events can overwhelm a single LLM provider's capacity, causing timeouts and failed requests at exactly the moment your AI-powered features need to perform best.

How LLMWise helps

✓Auto mode routes each task to the optimal model: fast, cost-efficient models for high-volume product descriptions and order status queries, and powerful models for complex customer negotiations and personalized recommendations.
✓Credit-based pricing with per-feature budgets lets you manage AI costs precisely across product content, customer support, and personalization, preventing any single feature from consuming your entire AI budget.
✓Mesh failover across multiple providers ensures your AI-powered features stay responsive during Black Friday, Prime Day, and flash sales, automatically routing around overloaded providers.
✓Compare mode lets your merchandising team evaluate how different models describe the same product, choosing the most compelling copy or using Blend mode to synthesize the best elements from multiple models.
Evidence snapshot

LLM API for E-commerce & Retail implementation evidence

Use-case readiness across problem fit, expected outcomes, and integration workload.

Problems mapped
3
pain points addressed
Benefits
4
outcome claims surfaced
Integration steps
4
path to first deployment
Decision FAQs
5
adoption blockers handled

Integration path

  1. Connect your product catalog system to the LLMWise API. Start with a batch pipeline that generates descriptions for new SKUs using a cost-efficient model like Claude Haiku 4.5 or DeepSeek V3 for high-volume generation.
  2. Set up real-time customer support automation using Mesh mode with a fast primary model and quality fallbacks. Route order-status queries to cheap models and complex complaints to GPT-5.2 or Claude Sonnet 4.5.
  3. Build personalization features using Chat mode with customer context in the system prompt. Use Auto mode to balance response quality against cost for high-traffic recommendation and search features.
  4. Before major sales events, load-test your AI features through LLMWise and verify your fallback chains are working. Monitor the dashboard in real time during the event to catch and address any routing issues immediately.
Example API call
POST /api/v1/chat
{
  "model": "auto",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "..."}
  ],
  "stream": true
}
Example workflow

An e-commerce platform with 50,000 SKUs needs to generate unique product descriptions for a new seasonal collection of 2,000 items. The merchandising team sets up a batch pipeline that sends each product's attributes — name, category, materials, dimensions, key features — to LLMWise Chat mode with a detailed brand voice system prompt. DeepSeek V3 generates descriptions at high throughput and low cost: 2,000 descriptions for 2,000 credits. Each description then passes through Judge mode with Claude Sonnet 4.5 evaluating brand voice compliance, SEO keyword inclusion, and factual accuracy. Descriptions scoring below 7 out of 10 are regenerated with a refined prompt. In parallel, the platform's live customer support chatbot uses Mesh mode with Claude Haiku 4.5 as the primary model — handling order status, return inquiries, and product questions. During a flash sale that triples traffic, Mesh failover seamlessly routes overflow requests to Gemini 3 Flash when Claude's rate limits are hit.

Why LLMWise for this use case

E-commerce AI needs to handle extremes: high-volume batch generation for product catalogs, real-time personalization for individual shoppers, and traffic spikes during sales events that would overwhelm a single provider. LLMWise handles all three scenarios through one API — cost-efficient models for catalog-scale generation, intelligent routing for real-time features, and multi-provider failover that absorbs seasonal traffic spikes without degradation. Credit-based budgeting lets you allocate AI spend across content, support, and personalization features independently, so one feature's usage never starves another.

Common questions

How much does it cost to generate product descriptions with LLMWise?
Each Chat mode request costs 1 credit. For high-volume product description generation, you can use cost-efficient models like Claude Haiku 4.5 or DeepSeek V3 to maximize output per credit. For a 10,000 SKU catalog, that is 10,000 credits — and with BYOK mode using your own API keys, you pay only the provider's token cost with no LLMWise markup.
Can LLMWise handle traffic spikes during sales events?
Yes. Mesh failover distributes your requests across multiple providers, so a capacity crunch at one provider does not affect your application. Circuit breakers detect slowdowns within seconds and automatically route to faster alternatives. This multi-provider architecture handles seasonal spikes more gracefully than relying on a single provider.
How do I maintain consistent brand voice across AI-generated product content?
Define your brand voice guidelines in a detailed system prompt that accompanies every generation request. Use Judge mode to score generated content against your brand criteria and automatically flag off-brand descriptions for human review. Over time, your system prompts and scoring criteria become a codified brand voice standard.
How do I use AI to generate product descriptions at scale?
Set up a batch pipeline that sends product attributes — name, category, features, specifications — to LLMWise Chat mode with a system prompt defining your brand voice and SEO requirements. Use a cost-efficient model like DeepSeek V3 or Claude Haiku 4.5 for high-volume generation, and add Judge mode to automatically score each description for quality, brand compliance, and keyword coverage. Descriptions meeting your threshold go directly to your catalog; those below it get flagged for regeneration or human editing. This pipeline can produce thousands of unique, on-brand descriptions per day at a fraction of the cost of manual copywriting.
Can LLMWise personalize the shopping experience for individual customers?
Yes. Use Chat mode with customer context — browsing history, past purchases, preferences — in the system prompt to generate personalized product recommendations, tailored descriptions, or custom shopping assistant responses. Auto mode routes each personalization request to the most cost-effective model that delivers the quality needed. For high-traffic storefronts, this approach delivers individualized experiences at scale without the cost of using a frontier model for every customer interaction.

One wallet, enterprise AI controls built in

You only pay credits per request. No monthly subscription. Paid credits never expire.

Replace multiple AI subscriptions with one wallet that includes routing, failover, and optimization.

Chat, Compare, Blend, Judge, MeshPolicy routing + replay labFailover without extra subscriptions
Start free with 40 creditsSee pricing examples
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