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Solution Blueprint — AI Agents

Solution Blueprint: A Bilingual AI Sales Agent for a Services Firm

Professional Services — UAE

The problem

A services firm receives inbound inquiries across website, WhatsApp and Instagram — in Arabic and English, around the clock. Response time averages 5 hours during the day and inquiries at night wait until morning. Half the leads never get a second touch; nobody knows how many were lost.

The existing process

A shared phone and a rotating duty schedule. Lead details live in chat threads; the CRM is updated “when there is time”. Follow-ups depend on individual memory.

The solution

A bilingual AI sales agent connected to all three channels: it answers service questions from the firm’s knowledge base, qualifies the lead with a short conversation, books meetings directly into the team’s calendar, writes everything to the CRM, and escalates to a human the moment confidence drops or the deal size crosses a threshold.

Architecture

  1. 01Channels — website chat, WhatsApp Business API, Instagram DM via one intake layer
  2. 02Grounding — retrieval over the firm’s services, pricing rules and past proposals
  3. 03Qualification — structured conversation capturing need, budget band and timeline
  4. 04Booking — calendar API integration with human confirmation rules
  5. 05CRM — every conversation logged; leads scored and routed automatically
  6. 06Guardrails — confidence thresholds, topic boundaries, instant human escalation
  7. 07Evaluation — weekly sampling of conversations scored against quality rubric

Technologies

LLM (bilingual EN/AR)RAG over knowledge baseWhatsApp Business APIInstagram Messaging APICalendar APICRM REST APIEvaluation harness

Projected outcomes

< 1 min

first response time, 24/7, in both languages (projected)

3×

more qualified meetings booked from the same inbound volume (projected)

100%

of conversations logged to CRM with qualification data

0

leads lost to overnight silence

Solution Blueprint: a technical demonstration of how we solve this class of problem. Figures are engineering projections from comparable deployments, not a specific client’s results.

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