AI WhatsApp Chatbot

Why it matters

Customer support is expensive and slow. Most inbound questions are repeat queries (order status, return policy, product availability, store hours) that an LLM with access to your data can answer correctly in seconds. The win isn't replacing your support team — it's freeing them to handle the genuinely hard 30%, while the 70% routine queries resolve without anyone touching them. Typical deployments cut ticket volume 40–60% within 90 days.

Features

• Grounded in Odoo data

  RAG-style retrieval over Odoo products, customer orders, invoices, and policy documents. Answers come from your actual data, not the LLM's training set.

• Multi-language support

  Native support for English, Hindi, Spanish, Italian, and major Indian regional languages. Auto-detects customer language and responds in kind.

• Order placement

  Customer can browse products, ask questions, and place orders entirely in conversation. Order lands in Odoo as a regular sales order with full audit trail.

• Order status lookups

  Customers ask 'where's my order' and get real-time status, tracking number, and estimated delivery — pulled from Odoo + carrier integrations.

• Lead qualification

  For B2B sales, the bot asks qualification questions (size, use case, timeline) and creates a qualified lead in CRM with full conversation history.

• Confidence-based escalation

  When the bot is unsure, it asks for clarification once, then escalates to a human agent with the full conversation context. No 'I'm just a bot, please contact support' dead-ends.

• Configurable persona

  Tone, vocabulary, and brand voice configured per deployment. The bot for a luxury retail brand sounds different from the bot for a B2B parts distributor.

• Compliance guardrails

  Topic blocklists (medical advice, legal advice, regulated financial advice), output filters, and full conversation logging for audit and refinement.

How it works

1. 1

   Discovery + content audit

   We catalog the questions your team handles most, the policy documents the bot needs access to, and the escalation rules. Output: a written playbook.

2. 2

   Knowledge base build

   Policy docs, FAQ, and product data ingested into a vector store (Pinecone or Upstash). Connected to Odoo for live data (orders, inventory).

3. 3

   Persona + prompt engineering

   System prompt tuned per brand voice. Topic blocklists configured. Escalation triggers defined.

4. 4

   Sandboxed testing

   Bot runs in a test WhatsApp number for 1–2 weeks with your team feeding adversarial scenarios. Confidence thresholds tuned based on results.

5. 5

   Production rollout

   Phased launch — start with one customer segment or one topic. Conversation logs reviewed weekly for the first month to refine responses.

6. 6

   Continuous tuning

   Monthly review of escalation reasons, customer-satisfaction signals, and false-confidence cases. Knowledge base and prompts updated based on real conversation data.

Deployment timeline

Standard deployment is 6–10 weeks: 1 week discovery, 2 weeks knowledge-base build and integration, 2–3 weeks prompt engineering and sandboxed testing, 1 week phased rollout, then continuous monthly tuning. The bottleneck is usually content quality — clean, current policy documents accelerate everything; outdated or contradictory docs add cleanup time. Existing WhatsApp ↔ Odoo integration cuts the timeline by 2 weeks.

Best for

B2C businesses with high customer-message volume on WhatsApp (retail, hospitality, healthcare front-desk, real estate); B2B businesses with predictable lead-qualification flows (SaaS demos, service inquiries); operations teams handling repetitive inbound queries (order status, return policy, inventory checks). Less useful for businesses where every conversation is unique and high-stakes (e.g. complex financial advisory, custom industrial sales).