A sales agent that
never sleeps — on WhatsApp.
Every message becomes a qualified lead, a behavioral profile, a CRM record, and a proactive follow-up — automatically. Gabbar Singh responds in under 10 seconds, reads behavioral signals to craft intelligent, personalized replies, and keeps conversations alive with daily follow-ups until the lead is ready to close.
One WhatsApp message. Nine systems working together.
From the moment a prospect sends "Hi" to the moment your sales team walks into a booked demo — and through every quiet day in between — the entire pipeline runs automatically. Here's how every layer connects.
1 · WhatsApp (Meta Cloud API)
Prospect sends a message to your business number. Meta's Cloud API delivers it via webhook with HMAC-SHA256 signature verification — every payload authenticated before processing starts.
Inbound trigger2 · Parallel AI Processing
The moment a message arrives, three systems fire simultaneously using parallel HTTP: a fast Ollama classifier extracts structured fields (name, company, industry, pain point, timeline, objection type), the BCompute behavioral engine profiles the conversation turn, and the RAG knowledge base is queried — all at once, cutting total wait time from 26s to under 10s.
Parallel · curl_multi · <10s total3 · Behavioral Compute (BCompute)
The BCompute engine builds a psychological profile per turn — intent score, urgency, trust deficit, objection type, conversion probability, and recommended tone. These signals are injected at the top of the system prompt, where small LLMs weight them most, so the reply strategy is shaped by real behavioral data, not guesswork.
Intent · Urgency · Trust · Strategy4 · CRM — Lead Upsert & Enrichment
Every newly extracted field flows into the CRM automatically. New contacts create a lead record; existing contacts update it. Phone number matching uses digit normalization so "+91-9876543210" and "9876543210" resolve to the same lead.
Multi-tenant · auto-matched5 · Customer vs. Lead Detection
The agent checks deal stages in real time. A contact with a Won deal becomes a customer mid-conversation — switching the agent from sales discovery to customer support and expansion mode, with no human intervention required.
Live deal-stage awareness6 · RAG Knowledge Base
Before generating a reply, the agent queries your vector knowledge base — product documentation, FAQs, case studies — and injects the most relevant chunks into the prompt. Your team manages the knowledge base through the CRM settings UI.
Semantic search · live context7 · Intelligent Reply (Gabbar Singh)
The main Ollama model generates a reply shaped by behavioral signals, profile-based persuasion strategy (industry-specific stats, tool contrasts, decision-maker framing), catalogue-aware product context, RAG knowledge base results, and detected objection type. If the lead has explicitly asked not to be pushed about demos, that preference is permanently respected — no exceptions. One question per message. Always conversational.
Persona-driven · behavioral · catalogue-aware8 · Tasks, Notes & CRM Activity
If the agent commits to a follow-up ("I'll have someone call you tomorrow"), a CRM task is automatically created with the right assignee, priority, and due date. Conversation summaries, behavioral scores, and extracted data all appear in the lead's CRM record in real time.
Zero manual data entry9 · Proactive Follow-up (Daily Cron)
When a conversation goes quiet, the system doesn't wait. A scheduled cron runs every 30 minutes, checks for leads with no recent response, and sends a contextually generated follow-up message — varying the angle each day (value proposition, objection reframe, urgency nudge). If the lead is still active, the next follow-up is auto-scheduled 22–30 hours later. Stops automatically when a demo is booked or the lead converts.
Auto-scheduled · no-contact detected · stage-awareSystem Architecture
The agent reads the room — every message, every time.
Behind every reply, BCompute runs a behavioral analysis pass that produces live psychological signals. Those signals shape tone, pacing, objection handling, and next action — invisibly, automatically.
Objection Detection
Price objection? Timing hesitation? Trust gap? Feature doubt? Each type triggers a different handling strategy — specific phrasing suggestions injected directly into the prompt.
Turn-by-Turn Update
Signals aren't static. They're recalculated after every message. A conversation that starts cold can reach strong intent by turn 6 — the agent adapts in real time.
Conversion Probability
A 0–1 probability score is maintained per session. Your CRM team can see which WhatsApp contacts are hot without reading a single message.
Profile Signals Visible in CRM
Intent, urgency, and trust scores surface as a visual signal bar on every lead's CRM record — giving your sales team an instant read before they pick up the phone.
Suggested Follow-up Messages
BCompute pre-writes a context-aware follow-up message for the lead — based on signals, objection type, and conversation stage — ready for your team to send with one click.
Full Conversation Archive
Every WhatsApp conversation is stored, searchable, and viewable inside the CRM — with timestamps, direction (inbound/outbound), and a live behavioral score history.
Profile-Based Persuasion Strategy
Once the agent knows the lead's industry, current tools, team size, and pain points, it synthesizes a tailored talking strategy — industry-specific stats, tool-by-tool contrasts, decision-maker framing, and urgency signals — all injected as specific talking points, not generic sales lines.
Respects User Intent — Permanently
If a lead says "don't push me on demos" or "I'll reach out when ready," the system sets a permanent no-demo-push flag on that session. No demo slots, no scheduling nudges, no interactive buttons — ever again on that contact. Detected by both LLM classifier and keyword scan, stored in session state.
Zero manual data entry. Everything flows to the right place.
The agent doesn't just talk — it works. Every useful piece of information extracted from a conversation is automatically written to the right CRM field, the right lead record, and the right activity log.
🔍 Lead Identification
Phone numbers are digit-normalized and matched against existing CRM records. If a match exists, the agent greets them by name and references prior history. If they're new, a lead record is created on the first message.
📝 Bio Extraction
Name, email, company, designation, city, industry, team size, current tools, pain points, and decision timeline are extracted conversationally — one field per message, never interrogating the user — and written to the CRM automatically.
🏆 Customer Recognition
If a contact has a Won deal in the CRM, the agent switches to customer support mode mid-conversation. No discovery questions. Warm, helpful, focused on expanding usage or solving problems.
✅ Automatic Task Creation
When the agent says "I'll have someone call you tomorrow," a CRM task is created instantly — with the right assignee, priority level, and due date. Follow-up commitments never slip through the cracks.
📅 Appointment Booking
The agent can present available demo slots, collect confirmation, and create a booked-appointment record in the CRM — moving the lead from conversation to confirmed meeting without human involvement.
🗒️ CRM Notes & Activity Log
Every conversation summary, behavioral score update, and enrichment event is logged as a CRM activity — giving your team a full timeline of every touchpoint before their first live call.
📦 Catalogue-Aware Pitching
The agent queries your product and service catalogue in real time, scoring items by industry match, company size, and keyword relevance. The top 3 most relevant offerings — with pricing, key talking points, and objection-handling guidance — are injected into the prompt so the agent pitches the right product to the right person, every time.
What gets captured automatically
One platform. Every client gets their own agent.
The entire ecosystem runs multi-tenant from the ground up. Each client deployment is fully isolated — a different WhatsApp number, a different AI persona, a different knowledge base, and a different CRM namespace — all on the same infrastructure.
📱 Separate WhatsApp Numbers
Each client connects their own Meta-registered business number. Inbound messages are routed by phone_number_id — never mixed between tenants.
🤖 Custom Agent Persona
Every deployment can have a unique agent name, personality, and system prompt. One client runs "Gabbar Singh," another runs a formal enterprise assistant — same engine, different character.
📚 Per-Tenant Knowledge Base
Product documents, FAQs, case studies, and pricing are stored in isolated vector collections — one per client. The agent only retrieves knowledge relevant to their business.
🗃️ Isolated CRM Data
All leads, tasks, conversations, and behavioral profiles are scoped to the tenant's client_id. Zero data leakage between deployments — enforced at every query level.
🔐 Per-Integration Security
Each WhatsApp integration has its own app_secret for HMAC signature verification and its own verify_token for webhook subscription. Credentials never shared across tenants.
📊 Separate Analytics & Profiles
WhatsApp conversation history, behavioral signal dashboards, and BCompute profiles are scoped per tenant. Each client sees only their own data in their own CRM dashboard.
⚙️ Independent Configuration
Booking calendars, CRM assignees, callback workflows, and demo slot sources are all configured per deployment. No cross-tenant configuration interference.
🔄 Shared Infrastructure, Zero Overhead
All tenants run on the same Raspberry Pi cluster with a local Ollama model — keeping costs near zero while maintaining full isolation. Add a new client in minutes.
The agent follows up. Every day. Without being told to.
Most AI chatbots only respond when a user writes first. Gabbar Singh actively initiates — sending contextually generated follow-up messages to every lead that goes quiet, varying the angle each day until the lead responds or converts.
Scheduled Every 30 Minutes
A background cron runs every 30 minutes across all active integrations. It checks which leads are due for a follow-up, fires the messages, and auto-schedules the next one — 22 to 30 hours out, randomized to feel natural.
Context-Aware Message Generation
Each follow-up is generated by the AI using the current conversation state — stage, industry, expressed pain points, and objection type. No template recycling. Each message is unique and built from what's already been said.
Angle Rotation
Day 1 might be a value-add insight. Day 2 reframes a known objection. Day 3 nudges urgency. The angle shifts each cycle so the lead doesn't feel like they're receiving the same message with a different subject line.
Stops When It Should
Follow-ups stop automatically when a demo is booked, a deal is won, the lead is archived, or the lead has explicitly opted out of scheduling nudges. No awkward messages after a conversation has concluded.
CRM Activity Logged
Every proactive follow-up is logged as a CRM activity on the lead's record — with the full message text and timestamp. Your sales team sees the full picture before their first live interaction.
Retry on Delivery Failure
If a message fails to send (network error, WhatsApp API issue), it's automatically rescheduled for 6 hours later — not silently dropped. Every committed follow-up is eventually delivered.
It doesn't sound like a bot. That's the point.
Most AI chatbots feel like forms. Gabbar Singh feels like a knowledgeable person who's genuinely interested in your problem. That's an engineering choice, not a coincidence.
One Question Per Message
The agent never fires a list of questions. One natural question per reply — the kind a thoughtful human salesperson would ask, not a survey.
Warm Recognition
Returning contacts are greeted by name. The agent references previous conversations, demo history, and expressed interests — making it feel like picking up a real relationship.
WhatsApp-Native Length
Replies are 2–4 sentences. Natural line breaks. Occasional emoji when appropriate. Never a wall of text. Designed for how people actually read WhatsApp, not email.
Tone Adapts to Behavior
If signals show hesitation, the agent slows down and reassures. If intent is high, it moves confidently toward a next step. If the lead needs education, it simplifies. No generic template.
Never Says "I am an AI"
The agent presents as a team member. It doesn't volunteer its nature, doesn't use robotic disclaimers, and never breaks the conversational flow with system language.
Natural Link Sharing
Relevant product pages, pricing links, case studies, and demo URLs are woven into replies when the topic naturally calls for them — not dumped as a list of resources.
Four modes. One platform. Configured per client.
Every deployment gets the exact bot behaviour it needs — from a simple button menu to a full RAG-powered AI assistant. Modes can be switched anytime from the admin panel.
Your knowledge. Instantly retrievable. Always accurate.
Upload any document, point to any URL, and the bot knows it in minutes. Answers are grounded in your content — no hallucinations, always cited.
How the RAG pipeline works
Documents are ingested, chunked, embedded into vectors, and stored. On every query, the most relevant chunks are retrieved and injected into the LLM prompt to generate a grounded answer.
⚡ LLM Flexibility
Choose per tenant: GPT-4o, Gemini 1.5, Claude Sonnet, or local Ollama. Cost-optimised routing included.
🛡️ Hallucination Guard
If no knowledgebase match above confidence threshold → bot says "I don't have that information" rather than inventing an answer.
🔍 Knowledge Gap Log
Every low-confidence query is logged. Admin reviews and adds to the knowledgebase — closing gaps continuously.
Document Sources
Ingest knowledge from anywhere your content lives.
Your users speak their language. So does your bot.
Language detection is automatic. The bot replies in the user's detected language without any configuration. Users can also switch language at any time.
Go live in hours, not months.
Pre-built template packages — each includes ready-made flows, FAQ pairs, RAG prompts, and WhatsApp message templates tailored to your industry.
Reach thousands. One targeted message at a time.
Send targeted WhatsApp broadcasts to segmented audiences — scheduled, drip, or instant. Every campaign tracked with delivery, read, and reply rates.
Connects to everything you already use.
CRM, payments, calendars, ERP, and your own APIs — the platform pushes and pulls data from your existing stack without disruption.
Add a 24/7 WhatsApp sales agent to your CRM deployment.
Ready1Go's WhatsApp AI agent is part of the platform — not a bolt-on. It connects to your CRM, product catalogue, knowledge base, booking calendar, and behavioral intelligence layer from day one. Responds in under 10 seconds, reads behavioral signals to personalize every reply, and follows up proactively when leads go quiet — so no opportunity slips through.