In-depth technical writing on AI, ML, distributed systems, and modern engineering.
The negotiation engine group detects BATNA positions, recognises tactical patterns, and models authority-dependency relationships — giving p…
The the chatbot platform adapter converts six behavioral engine outputs into actionable lead-scoring signals: intent, engagement, objection …
client_id as the fundamental unit, config-driven behavior, the DB schema for clients and agent_modes, and why the system prompt must be a ru…
The channel adapter pattern isolates WhatsApp, widget, and mobile channel handling from the shared intelligence core. Same LLM, same RAG, di…
Linking WhatsApp conversations to CRM contacts, LLM-powered lead field extraction, pushing behavioral scores as CRM custom fields, and sched…
HMAC-SHA256 signature verification, the 20-second response requirement, deduplication by wa_msg_id, and webhook verification challenge handl…
Resolving a phone number to user+role, persona classes (Lawyer/Client/Staff/Unknown) with role-aware system prompt context, and fallback for…
Two-layer classification: Layer 1 keyword matching for common intents, Layer 2 LLM classification for ambiguous messages, confidence thresho…
Per-user session schema, storing last N turns, context injection into LLM prompts, session expiry, and multi-device handling.
Script-based language detection (Devanagari/Gurmukhi Unicode blocks), storing preferred_lang per contact, language-specific error messages i…
Client asks the WhatsApp AI agent → bot forwards to lawyer → lawyer replies via WhatsApp → bot forwards to client. Relay detection, forwardi…
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