Identify the right product to promote in your chat widget — in under 75ms
ChatAds extracts product keywords from conversational text so you can match them to your own catalog, serve sponsored products, or surface the right offer. No LLM call required.
Your chat widget discusses products but you have no way to detect which ones in real time. Calling an LLM on every message adds 1-3 seconds — too slow for a shopping assistant that needs to feel instant.
Purpose-built NLP extracts the product keyword in under 75ms — no LLM needed. You take that keyword and match it to your own catalog, sponsored products, or inventory.
How it works
Extract product intent from chat in real time — then match against your own inventory.
Send the chat message
Pass your chatbot's response to the ChatAds extraction API. Works with any AI model or chat platform.
We extract the product keyword
Our NLP pipeline identifies the product mention in under 75ms — no LLM call, no 1-2 second delay. Just fast keyword extraction.
You match to your catalog
Use the extracted keyword to query your own product database, serve a sponsored listing, or surface the right SKU from your inventory.
Three ways to extract products from AI conversations
| Build It Yourself LLM prompts or custom NER models | Existing Tools Entity extraction APIs, search platforms | ChatAds Product extraction for retail media | |
|---|---|---|---|
| How it works | Prompt an LLM to identify products, or train a custom NER model on product entities | Generic entity extraction (AWS Comprehend, Google NLP). Not tuned for product mentions in chat. | Purpose-built NLP for product extraction from conversational AI text. One API call. |
| Latency | 1-3 seconds for LLM extraction. Custom NER is faster but requires ML infrastructure. | 200-800ms for generic entity APIs. Then you still need to filter for products. | Under 75ms. Non-product messages detected even faster. |
| Product-specific | Partial LLMs extract too broadly or too narrowly. Constant prompt tuning. | No Generic entity extraction. "Nike Air Max" might return as an organization, not a product. | Yes Trained specifically on product mentions in AI conversations. Handles brands, generics, and context. |
| Chat-optimized | LLMs understand chat context but at 1-3s per call, they're too slow for real-time widgets | Built for documents, not conversational AI. Misses chat-specific phrasing. | Trained on conversational AI output. Understands recommendations, comparisons, and casual product mentions. |
| Time to integrate | Weeks (LLM) to months (custom NER) | Days — plus ongoing filtering and tuning | Under an hour. One endpoint, one response format. |
Build It Yourself
LLM prompts or custom NER models- Latency: 1-3 seconds (LLM) or custom ML infra
- Product-specific: Partial — constant prompt tuning
- Chat-optimized: Too slow for real-time widgets
- Time to build: Weeks to months
Existing Tools
Entity extraction APIs- Latency: 200-800ms + filtering
- Product-specific: No — generic entities
- Chat-optimized: No — built for documents
ChatAds
Product extraction for retail media- Latency: Under 75ms
- Product-specific: Yes — trained on AI conversations
- Chat-optimized: Yes — understands conversational phrasing
- Time to integrate: Under an hour
