NULLBIT
NULLBIT
العودة إلى الأعمالAI Case Study

A concierge chat that qualifies leads while it talks to them

Multi-agent AI chat for a travel and destination-management business

وكلاء الذكاء الاصطناعيمحادثة وكيليةتقنية السفرتنسيق النماذج اللغوية
السنة

2026

المدّة

تسليم متعدّد المراحل

القطاع

السفر / إدارة الوجهات السياحية

A concierge chat that qualifies leads while it talks to them
Starting position

Starting position

النقطة الأساس

  • First response time

    During business hours only

    ≈ 2-6 hours
  • Enquiries qualified before a human reviews them

    Every enquiry manually triaged

    0%
  • Time to a complete, usable assessment

    Back-and-forth over email and WhatsApp

    ≈ 1-2 days
  • Enquiries handled per team member per week

    Manually, across text and voice channels

    ≈ 40-60

حجم السوق

≈ 500-700 / month

Inbound enquiries eligible for AI-first triage

النطاق: SOM (client's own enquiry channels)المصدر: Client enquiry-volume logs, pre-launch benchmark

الميزانية التقريبية

اتفاقية سرّية

50,000 – 199,999 €

توزيع الميزانية

Multi-agent architecture & orchestration30%
Streamed chat + voice infrastructure25%
Brand-voice retrieval pipeline20%
Lead scoring, QA & regression testing25%

Budget bucket per Clutch.co project cost category. Exact figures under NDA.

5-phase delivery

Multi-phase

  1. 1

    Phase 1

    Assessment schema + multi-agent architecture design

  2. 2

    Phase 2

    Streamed chat + voice input over a single session model

  3. 3

    Phase 3

    Brand-voice retrieval over curated past correspondence

  4. 4

    Phase 4

    Lead scoring, qualification thresholds + concierge sub-agents

  5. 5

    Phase 5

    Regression testing + production rollout

The Challenge

Every enquiry needs the same information, gathered by hand

Before a human can respond meaningfully to a travel enquiry, someone has to extract budget, traveler count, dates and preferences from a free-form message or call - then reply in a tone that matches the brand, whichever person on the team picks it up. None of that scales with enquiry volume, and none of it is captured anywhere structured.

Every enquiry manually triaged before anyone knows if it's worth a human's time

Tone and voice depend on whoever happens to answer

Text and voice enquiries handled through completely different, ad-hoc processes

No structured record of what a lead actually needs until a human writes it down

Our Solution

A multi-agent chat that gathers, scores and hands off - in the brand's voice

We designed a chat system built from purpose-scoped agents rather than one general-purpose bot: a conversational agent that guides the enquiry, an assessment agent that scores and qualifies it against configurable thresholds, and a set of concierge agents that handle the surrounding workflow once a lead is qualified.

Guided assessment, not a form

The conversation itself gathers budget, dates, travelers and preferences, tracking what's still missing turn by turn.

Streamed text and voice

Replies stream token-by-token for a natural feel; voice messages are transcribed and answered in the same flow.

Brand-matched tone by retrieval, not fine-tuning

The agent's voice is anchored by retrieving real past replies that match the situation, curated by the team rather than baked into a model.

Qualification before hand-off

Configurable lead-score and budget thresholds decide which conversations reach a human, and when.

What changed

How enquiry handling changed

Enquiry handling moved from manual, person-dependent triage to a structured, brand-consistent chat that does the qualifying work before a human is needed.

Enquiry channel

Before

Manual email and WhatsApp threads

After

Guided AI chat, text and voice

One conversational surface instead of two disconnected, manual ones.

Tone of voice

Before

Depends on whoever replies

After

Retrieved from curated past correspondence

Consistent brand voice without training a model on it.

Lead qualification

Before

Every enquiry triaged by hand

After

Automatic scoring against configurable thresholds

Humans see qualified leads, not raw inboxes.

Enquiry data

Before

Buried in free-form messages

After

Captured as a structured assessment as the conversation happens

Nothing needs re-typing once a human takes over.

Investment → Time Savedاتفاقية سرّية

Payback inside the first quarter

النسبة

≈ 2-4x

first year

الإيراد الإضافي

≈ 25,000-45,000 € / year

estimated triage-labor time reclaimed

الاستثمار

50,000 – 199,999 €

فترة الاسترداد

≈ 3 months

الطريقة

Manual-triage hours saved pre/post, priced at blended fully-loaded team cost

درجة الثقة

Medium - based on enquiry-handling time comparison, not a controlled trial

Savings = reclaimed triage time; incremental bookings from faster response are not quantified here.

The Outcome

From manual triage to a qualified hand-off in one conversation

The chat system is live and handling enquiries end to end, from first message to qualified hand-off. Client-specific volumes are confidential; figures below are ranges validated against the pre-launch benchmark.

First response time

قبل

≈ 2-6 hours

بعد

< 2 seconds

~99% fasterHours to the first streamed reply

Business hours vs. 24/7

Time to a complete assessment

قبل

≈ 1-2 days

بعد

Single conversation (minutes)

~95% fasterDays to minutes

Enquiry to structured hand-off

Enquiries auto-qualified before human review

قبل

0%

بعد

≈ 60-70%

+60-70 p.p.Roughly 2 in 3 enquiries reach a human already scored

Post-launch, rolling average

Assessment field coverage captured automatically

قبل

0%

بعد

≈ 80-90%

+80-90 p.p.Most required fields captured without manual note-taking

Per completed assessment

Stack & Services
TypeScript / Node.js
LLM Agent Orchestration
Streamed Chat (SSE)
Voice Transcription
Postgres + Vector Search
Queue-based Workers
Enquiries used to sit in an inbox until someone had time to work through them. Now most of the groundwork is already done by the time we open the conversation.

Operations Lead

Confidential - Travel & Destination Management Company

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