Multi-Platform AI Sales Agent with RAG
Engages leads across WhatsApp, Instagram, Facebook, Telegram, and web chat using RAG for answers, Airtable for CRM.
founders and execsfreelancers and consultants
What it does
Get 24/7 sales support across WhatsApp, Instagram, Facebook, Telegram, and your website where the agent transcribes voice messages and answers your leads using a knowledge base. It automatically updates your CRM in Airtable and books appointments on Google Calendar so you never miss an opportunity.
Step by step
- Set up n8n with HTTP nodes to connect to the WhatsApp Business Cloud API and Airtable.
- Configure PostgreSQL with pgvector extension and load product data into a vector table.
- Define the OpenAI Chat Model system prompt to enforce persona constraints and RAG retrieval logic.
Where the LLM does the work
- Generate natural language responses that adapt tone based on the specific social platform context.
- Design the RAG query rewriting logic to handle ambiguous customer queries effectively.
- Create error-handling messages for failed calendar bookings or missing CRM data.
Watch out for
WhatsApp template approval can delay deployment so design message structures early and get them approved before launch.
Vector search relevance may drop with noisy sales jargon so implement text preprocessing to clean up the knowledge base entries.
Context window overflow occurs during long voice transcriptions so truncate history or summarize previous turns before sending to the LLM.
Tools that fit
OpenAI Chat Model LLM Main AI agent reasoning and conversation
Postgres PGVector Store Service RAG knowledge base for accurate answers
WhatsApp Business Cloud Service Omnichannel lead engagement trigger
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Multi-Platform AI Sales Agent with RAG: Engages leads across WhatsApp, Instagram, Facebook, Telegram, and web chat using RAG for answers, Airtable for CRM. ## Specification - What it does: Get 24/7 sales support across WhatsApp, Instagram, Facebook, Telegram, and your website where the agent transcribes voice messages and answers your leads using a knowledge base. It automatically updates your CRM in Airtable and books appointments on Google Calendar so you never miss an opportunity. - Trigger: Triggered by an event (Event · on new message) - Autonomy: Fully hands-off - Expected setup effort: a few focused days - Tools/services involved: - OpenAI Chat Model: Main AI agent reasoning and conversation - Postgres PGVector Store: RAG knowledge base for accurate answers - Airtable: CRM contact and opportunity management - Google Calendar: Appointment booking and scheduling - WhatsApp Business Cloud: Omnichannel lead engagement trigger ## Known pitfalls, handle each one explicitly in your implementation 1. WhatsApp template approval can delay deployment so design message structures early and get them approved before launch. 2. Vector search relevance may drop with noisy sales jargon so implement text preprocessing to clean up the knowledge base entries. 3. Context window overflow occurs during long voice transcriptions so truncate history or summarize previous turns before sending to the LLM. ## Reference implementation https://n8n.io/workflows/4508 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/multi-platform-ai-sales-agent-with-rag/ via usecasesforagents.com
Frequently asked questions
Can I use a different LLM than OpenAI?
Yes. OpenAI is only the example LLM in this recipe. The same flow works with Anthropic, Cohere, DeepSeek and Google Gemini. Swap the LLM connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for founders and execs who need sales handled without hiring for it.
Freelancers and consultants can run this per-client with no changes beyond the source data.