Everybody is talking about AI. Most of it is either unrealistic hype or vague generalisation. “Use AI to transform your business!” doesn’t tell you anything useful. This post focuses on AI integrations we’ve actually built in production applications — what they do, how they work technically, and what results they produce for real businesses.
No hype. Actual use cases.
Use Case 1: AI-Powered Customer Support Chatbot
What it does: Handles routine customer questions (order status, return policy, account issues) automatically, with human handoff for complex queries.
How it works: We use OpenAI’s GPT-4 API with a system prompt that defines the assistant’s role and provides your business context. The chatbot has access to your product/service data via Retrieval-Augmented Generation (RAG) — a technique where we index your documentation, FAQs, and product data into a vector database, retrieve relevant context for each user question, and pass it to the LLM for response generation. This prevents hallucination (making up answers) and keeps responses relevant to your specific business.
Technical stack: Node.js backend, OpenAI API (GPT-4 Turbo), Pinecone or pgvector for the vector store, React frontend with streaming responses for the chat UI.
Real results we’ve seen: One e-commerce client handles 65% of support tickets automatically, reducing support staff costs by 40%. Response time went from 4 hours (human agent) to under 5 seconds (AI). Customer satisfaction scores actually increased because of 24/7 availability.
When it makes sense: If you’re handling 500+ support tickets per month with consistent question types. Below that volume, the setup cost doesn’t justify the saving.
Use Case 2: Intelligent Document Processing
What it does: Extracts structured data from unstructured documents — invoices, contracts, application forms, medical reports, resumes — and populates your database automatically.
How it works: Documents are passed to the OpenAI vision API or a specialised document AI service (Azure Document Intelligence for complex layouts). The model is prompted to extract specific fields (“extract invoice number, date, line items, and total amount”) and return structured JSON. Confidence scores flag low-confidence extractions for human review.
Real results: A logistics client was manually processing 400 delivery notes per day. After AI integration, 85% are processed automatically with 98%+ accuracy. Staff now review only the 15% that the model flags as uncertain — reducing manual data entry by 85%.
Cost: OpenAI vision API charges approximately $0.01 per document page. Processing 10,000 documents per month costs about $100 in API fees — a fraction of manual processing labour costs.
Use Case 3: Content Generation and SEO Assistance
What it does: Generates first drafts of product descriptions, blog posts, marketing copy, or personalised email content at scale — then humans review and edit.
How it works: A structured prompt template + your brand voice guide + relevant data inputs → GPT-4 generates a first draft. The key is the “human-in-the-loop” approach — AI generates, humans approve. We never recommend fully automated content publishing without review.
Real results: An e-commerce client with 15,000 products was missing product descriptions for 8,000 SKUs. Manual writing at $0.05/word for 150-word descriptions would cost $60,000. AI generation + human review took 3 weeks and cost $4,000 total (API costs + review labour).
Important caveat: AI-generated content needs human editing to be genuinely good. It’s a starting point accelerator, not a replacement for human writing quality.
Use Case 4: Recommendation Engine
What it does: Suggests relevant products, content, or actions based on individual user behaviour and preferences.
How it works: Two approaches depending on data volume: (1) Collaborative filtering — “users who bought/viewed X also bought/viewed Y.” Requires at least 1,000 users with interaction data to be useful. (2) Content-based filtering using embeddings — represent each item and user as a vector, find nearest neighbors. Works with less data and cold-start situations.
Technical stack: Python (pandas, scikit-learn for collaborative filtering, OpenAI embeddings for content-based), PostgreSQL with pgvector extension, served via a REST API to the application.
Real results: An education platform saw a 28% increase in course completion rates after adding “recommended next course” suggestions based on user progress patterns. Revenue per user increased 18% from upsell recommendations.
Use Case 5: Intelligent Search
What it does: Replaces keyword-matching search with semantic search that understands intent — “affordable running shoes for beginners” finds what the user means, not just pages containing those exact words.
How it works: When a user searches, we convert the query to an embedding vector using OpenAI’s text-embedding-ada-002 model. We then find the most semantically similar items in our vector database. This handles synonyms, typos, natural language queries, and context that keyword search misses.
Real results: An e-commerce client saw search conversion rate increase from 8% to 19% after switching from Elasticsearch keyword search to semantic vector search. Users were finding what they wanted on the first search rather than refining multiple times.
The Practical AI Integration Checklist
Before integrating AI into your product, ask:
- Is there a clear, specific problem? “Use AI” is not a problem. “Reduce time to classify customer tickets from 45 minutes/day to 5 minutes/day” is.
- Do you have the right data? AI systems need data to train on or context to work with. Recommendation engines need user behaviour data. Document processing needs sample documents.
- What does “good enough” look like? 70% accuracy in ticket classification might be fine (review the 30%). 99% accuracy in medical diagnosis is not fine. Know your threshold.
- How will you handle AI errors? Every AI system makes mistakes. Design your UX for graceful failure — human review queues, confidence scores, easy correction workflows.
- API costs at scale? At 100 users, API costs are negligible. At 1 million requests/month, they can be significant. Model before you build.