Recommend products to customers with an AI chat assistant

An AI chat assistant searches your product catalog and instantly recommends the best matches to each customer.

How the work actually flows

A straight line.

Pattern: Sequence (1)

flowchart TD trig>"customer describes what they want"]:::trig s0["search catalog for matches"]:::svc s1["rank best matches"]:::task s2["reply with recommendations"]:::task trig --> s0 s0 --> s1 s1 --> s2 out[/"personalized product recommendations delivered"/]:::out pay{{"customers find the right product faster"}}:::pay s2 --> out out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
Starts itA stepAn outside serviceResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
AI Agents & Autonomous SystemsAI Chatbots & AssistantsKnowledge Base & RAGDocument Processing & OCRDevOps & IT Operations
Connects
OpenAIQdrantGitHub

The problem it solves

Customers browsing your catalog often can't find what they want and give up before buying. You don't have time to build a recommendation engine from scratch, and generic search doesn't understand what someone actually means when they describe what they're looking for.

Who it fits

Online retailers, streaming services, or any business with a large catalog customers need help navigating.

How it works

  1. Product descriptions are pulled from your catalog and turned into searchable data
  2. A customer describes what they want and don't want in a chat
  3. The system searches for the closest matches using AI
  4. The chat assistant replies with personalized recommendations
What you get

Customers matched to the right products fast

You get an AI chat assistant that instantly points each customer to the products in your catalog that best fit what they described.

What you get

A chat assistant that gives customers personalized, ranked recommendations from your own catalog.

What you need

An OpenAI API key, a Qdrant vector database account, and a place to host your catalog data such as GitHub.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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