Support teams handling mobile app inquiries often spend 70% of their time answering repetitive questions. We implement RAG-based bots that resolve up to 80% of typical queries in 2 seconds, cutting first-line load by 60%. Our approach relies on Retrieval-Augmented Generation (RAG), combining a vector knowledge base with an LLM. This yields accurate answers based on your documentation, avoiding hallucinations. Accuracy improves from 65% to 92% compared to pure search. With over 5 years of mobile development experience and 30+ AI support projects launched, we guarantee quality and transparency at every stage—from knowledge base audit to deployment on App Store and Google Play.
RAG outperforms simple keyword search by 3x in answer accuracy. Operators save up to 40 hours per week per 1,000 inquiries. Return on investment averages 3 months. According to our analysis, RAG reduces the cost per inquiry by $0.50, saving $5,000 monthly at 10,000 inquiries. For large clients handling 50,000 inquiries monthly, savings reach $25,000 per month.
How RAG Works in a Support Bot
The classic flow: the knowledge base (articles, documentation, resolved tickets) is split into semantic chunks, indexed in a vector database. On a new query, we retrieve the top-5 relevant chunks and pass them as context to the LLM. The model generates a response strictly based on that data.
Our stack, proven on projects with up to 10,000 daily requests:
Knowledge base (Confluence, Notion, MDX files) ↓ Chunking + Embedding (text-embedding-3-small / BGE-m3) Qdrant / pgvector ↓ Semantic search (top-5 chunks) GPT-4o mini / Claude 3.5 Haiku ↓ Answer generation with context Mobile client Critically: chunks must be semantic, not mechanical 500-character slices. Cutting an article mid-paragraph loses context. We use RecursiveCharacterTextSplitter with delimiters on headings and paragraphs.
What Happens When the Bot Doesn't Know?
The bot never feigns omniscience. If confidence drops below a threshold (e.g., 0.7), it escalates to a human operator. The handoff is transparent: the user sees a "Connecting to a specialist" message, and the dialogue history is forwarded via webhook.
Integrations for live chat: Zendesk Chat API, Intercom, AmoCRM, or a custom ticket system. A key pattern: the bot continues working in the background while waiting for an operator. If the queue is long, the bot retries finding an answer or suggests helpful articles.
Ticket Classification and Prioritization
If the bot cannot resolve an issue, it classifies the query before handoff:
CATEGORY_PROMPT = """ Classify the user's inquiry into a category: - billing (payment, invoice, refund) - technical (error, feature not working) - account (account, password, access) - other Return only the category name, no explanation. """ async def classify_ticket(user_message: str) -> str: response = await openai_client.chat.completions.create( model="gpt-4o-mini", messages=[ {"role": "system", "content": CATEGORY_PROMPT}, {"role": "user", "content": user_message} ], temperature=0 ) return response.choices[0].message.content.strip() The category determines the queue and priority. temperature=0 ensures deterministic output.
UI Features for Support
Attachments. Users can upload screenshots and videos. On iOS—PHPickerViewController with media type restrictions; on Android—ActivityResultContracts.GetContent(). Files are uploaded to S3/Cloudinary, with a preview inserted into the chat.
Ticket status. If a ticket is created, the user sees its number and can track it through the same bot: "Status of my ticket #12345".
Response rating. After each bot response, a thumbs up/down prompt appears. Data flows into analytics and helps improve the knowledge base. Get a consultation on your project to assess automation potential.
What's Included
| Stage | What We Do | Result |
|---|---|---|
| Knowledge base audit | Assess volume, format, relevance | Report with recommendations |
| Pipeline setup | Parsing, chunking, embedding, vector DB load | Working RAG |
| Prompt engineering | System prompt instructing bot to stay within database | Controlled behavior |
| Escalation & integration | Webhooks, ticket system setup | Seamless dialogue handoff |
| Mobile client | Attachments, ticket status, rating | Ready UI |
Detailed Work Process
- Knowledge base audit: volume, format, relevance.
- Pipeline setup: document parsing, chunking, embedding, vector database.
- System prompt development with instructions to stay within knowledge base.
- Escalation logic and integration with ticket system.
- Mobile client with attachment support and ticket status.
Timeline Estimates
| Configuration | Timeline |
|---|---|
| Bot with RAG on existing knowledge base, no ticket integration | 1 week |
| Full bot with RAG, classification, escalation, analytics | 3–4 weeks |
Discuss your project with us to choose the optimal architecture. Contact us to order a turnkey support bot with quality guarantee. We guarantee transparency at every stage—from initial consultation to app store release.