핵심 요구사항
- 대화형 AI 에이전트 설계·학습·테스트 (인텐트 인식 90%+ 달성)
- Go High Level CRM 연동 (인증, 오브젝트 매핑, 커스텀 필드 처리)
- 계약서·수정본·NDA 등 문서 수신/태깅/버전 관리 저장
- 각 상호작용의 티켓화 및 CRM 파이프라인 자동 등록
- WhatsApp 알림 채널 및 팀 내부 알림·대시보드 연동
- 캘린더 예약 핸드오프 기능 구현
Required stack
필요 기술
GHL APIAI ChatbotNLP/Intent RecognitionREST APIWebhooksWhatsApp APIPHPC#
Project brief
프로젝트 내용
I’m ready to deploy a conversational virtual agent that will take a sizable load off our contracting team and tighten up our day-to-day workflows.
Core responsibilities for the bot
• Field incoming questions of every kind—general contracts questions for agents , light technical support, and real-time project status updates—without passing routine issues to a human rep.
• Offer a quick calendar hand-off so prospects or partners can secure times on our team’s schedule.
• Receive, tag and store contracts, amendments, NDAs and other paperwork so the right version is always at our fingertips.
• Register each interaction as a ticket and push it straight into our existing CRM pipeline. We’re on GHL (Go High Level), so you’ll need to handle authentication, object mapping and any custom field quirks that come with that environment.
Ideal workflow
I’ll outline sample conversations, user intents and document types. You’ll craft, train and test the agent, then wire it into GHL via their REST API / webhooks. A short knowledge-base handoff plus deployment instructions will round out the engagement. Check the emails come to agents requesting contracts carriers, send a notifications by WhatsApp channel to the agents for follow up, internal notifications to the team, registers in dashboards
Acceptance criteria
1. 90 %+ intent recognition on the supplied test set.
2. New tickets, appointments and files appear in the correct GHL stage within 30 seconds.
3. A concise README covering environment variables, retraining steps and fail-over logic.
If you’ve shipped similar bots—or already wrestled with Go High Level integrations—let’s talk.
Core responsibilities for the bot
• Field incoming questions of every kind—general contracts questions for agents , light technical support, and real-time project status updates—without passing routine issues to a human rep.
• Offer a quick calendar hand-off so prospects or partners can secure times on our team’s schedule.
• Receive, tag and store contracts, amendments, NDAs and other paperwork so the right version is always at our fingertips.
• Register each interaction as a ticket and push it straight into our existing CRM pipeline. We’re on GHL (Go High Level), so you’ll need to handle authentication, object mapping and any custom field quirks that come with that environment.
Ideal workflow
I’ll outline sample conversations, user intents and document types. You’ll craft, train and test the agent, then wire it into GHL via their REST API / webhooks. A short knowledge-base handoff plus deployment instructions will round out the engagement. Check the emails come to agents requesting contracts carriers, send a notifications by WhatsApp channel to the agents for follow up, internal notifications to the team, registers in dashboards
Acceptance criteria
1. 90 %+ intent recognition on the supplied test set.
2. New tickets, appointments and files appear in the correct GHL stage within 30 seconds.
3. A concise README covering environment variables, retraining steps and fail-over logic.
If you’ve shipped similar bots—or already wrestled with Go High Level integrations—let’s talk.
지원 기회 분석
지원 전에 볼 것
예상 산출물
- GHL 연동이 완료된 대화형 가상 에이전트
- 환경변수·재학습 절차·페일오버 로직을 담은 README
- 지식베이스 핸드오프 및 배포 문서
매력 포인트
- 정량적 인수 기준(인텐트 90%, 30초 내 반영) 명시로 스펙이 비교적 구체적
확인할 점
- 예산($250~750)에 비해 요구 기능(다중 연동·문서관리·AI 학습)이 과도하게 많음
- GHL 커스텀 필드·인증 등 통합 리스크가 크나 사양이 개괄적임
Client signal
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