Seeking n8n/GHL Automation Developer — Insurance Lead Pipeline + AI Agents {Ongoing}

Jobs
1

Hi all — looking for an experienced automation developer to build out a multi-agent lead pipeline for a licensed life insurance business (NC/SC markets).

Stack: n8n or similar, GoHighLevel (CRM/calendar/SMS), Twilio, Claude API for the conversational/AI-agent layer.

What’s being built (phase 1):

**•**	Lead capture → speed-to-lead SMS response (sub-60-second target) → AI-driven qualifying conversation → calendar booking → CRM sync, all as connected agent workflows

**•**	State-based filtering logic (NC/SC only)

**•**	Conditional branching for qualified vs. nurture paths

**•**	Reminder/confirmation automation tied to booked appointments

I have the full architecture and workflow logic already mapped out (agent roles, data flow, branching logic) — I need someone who can translate that into a working, production-ready build, not someone starting from a blank canvas.

Ideal experience:

**•**	Production n8n or GHL workflow builds involving AI agent nodes (Claude or GPT), not just basic Zapier-style automations

**•**	Comfortable with conditional logic, webhook triggers, and CRM API integrations

**•**	Bonus: experience with real-time audio/transcription pipelines (a later phase involves live-call AI assistance)

This is scoped as an initial paid project with strong potential for ongoing work as the system expands (a second phase covers SEO/content agents, referral tracking, and reporting dashboards).

Please share relevant portfolio examples or workflows you’ve built — happy to hop on a call once I’ve reviewed a few.

8 Likes
2

Hi I am Vaar.

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This is our youtube channel: https://www.youtube.com/@blankarray

My linkedin: https://www.linkedin.com/in/iamvaar

You can schedule an appointment with this link: N8N Project Consultation with Vaar | Iamvaar | Cal.com

And for more links: iamvaar Official: X | Linktree

3

Hi @J_C1, welcome to n8n community, I would love to collaborate wth you on this, multi-agent n8n builds with GoHighLevel, Twilio, and Claude for lead qualification and booking, and I’m comfortable working from your already-mapped architecture rather than starting blank

Upwork: https://www.upwork.com/freelancers/~01446d60f782215efa
Website: https://www.pathfinderautomationsolutions.com
Calendly: Calendly
Email: Taiwo@pathfinderautomationsolutions.com

Taiwo

4

Hey! this is close to a build I already shipped: an AI voice/SMS receptionist on GoHighLevel, with n8n as the orchestration layer and Claude driving the qualifying conversation logic. Structured JSON output feeds back into GHL’s CRM and calendar. Same shape as your phase 1: speed-to-lead, AI-qualifying conversation, booking, CRM sync.

I’ve also built a production content pipeline for a retainer client, 500+ scheduled social posts across 5 platforms, n8n handling webhooks, REST integrations, and OAuth across the stack. So the conditional branching and state-based filtering you’re describing isn’t new territory, I’m not coming from single-path automations.

Portfolio: https://gianbportfolio.netlify.app/
LinkedIn: https://www.linkedin.com/in/gian-benedict-6043193a4/
Happy to walk through the GHL/Vapi build live, or send a Loom of the workflow logic. If it’s useful to talk.

5

J_C1, one design choice will make or break the sub-60-second target: acknowledge and persist the lead before any model call, then run qualification as a state machine that can resume after each inbound message. Keep NC/SC eligibility, consent evidence, and booking rules deterministic. Let Claude select from bounded prompts and approved follow-up actions, with a human handoff on ambiguity or sensitive questions.

TinyOps Studio LLC can take phase 1 from your mapped architecture through production. I would scope it as ingress and deduplication, GHL contact and opportunity sync, Twilio messaging, state filtering, a qualifying state machine, calendar booking, reminders and nurture, replayable error handling, synthetic tests, and a runbook. The later live-call assistant should remain a separate phase.

Two details would let me give you a firm written milestone plan: where is SMS opt-in evidence stored for each lead, and are the GHL sub-account, Twilio messaging service and registration, and sending number already active? I work asynchronously and can provide a written architecture sample here or by community message.

6

Hi J_C1 — this is interesting and the fact that you already have the architecture and workflow logic mapped out is a good fit for how I work.

My strongest background is in AI application/backend engineering, API integrations, webhooks, asynchronous workflows, and AI-agent systems using Node.js/TypeScript and LLM APIs. I’ve built production-oriented AI workflows involving external AI providers, background processing, job state tracking, retries, webhooks, and notification flows.

I can work from an existing architecture and translate the agent logic into reliable production workflows rather than treating this as a basic Zapier-style automation.

I want to be transparent that I wouldn’t position myself as a long-time GoHighLevel/n8n specialist if you’re specifically looking for someone with years of GHL implementation history. My strength is the engineering/AI side, and I’d be interested if you’re open to someone who can own the integrations, agent logic, APIs, reliability, and backend side of the build.

If useful, I can share a relevant AI workflow/architecture example and discuss how I’d approach the lead → qualification → booking → CRM flow.

7

Hi J C — since your architecture is already mapped, I would treat Phase 1 as an implementation and acceptance sprint rather than redesigning it.

For USD 950 and seven business days, I would deliver:

  • one agreed lead-source webhook into n8n, with validation and duplicate protection;
  • sub-60-second Twilio SMS under the agreed controlled test load;
  • Claude qualification constrained to your approved script and structured outcomes, with no independent insurance advice or eligibility decision;
  • NC/SC routing plus qualified, nurture, opt-out, and licensed-agent handoff paths;
  • one GoHighLevel contact, opportunity/pipeline, calendar-booking, confirmation, and reminder flow;
  • retries, idempotency, visible error logging, and safe-stop behavior; and
  • 20 agreed synthetic acceptance cases, exported workflows, a short runbook, and Loom handoff.

Acceptance means all 20 fixtures enter the correct state and branch; the agreed test lead receives the first SMS within 60 seconds under controlled conditions; each contact, opportunity, and booking is written once with the correct fields; reminders and stop conditions fire as specified; and failed runs remain visible for recovery.

The fixed phase excludes live-call or audio assistance, lead sourcing or advertising, Phase 2 SEO/content/referral/reporting agents, CRM migration, custom dashboards, legal or compliance approval, carrier registration or provider approval delays, telephony/model/platform fees, and an ongoing production SLA.

I run AI Firm and can share AI Firm-owned n8n, conversational, and human-handoff reference builds at https://aifirm.app. I will label them accurately as owned demonstrations, not insurance-client deployments. I’m also happy to walk through the architecture with you on a call.

Arda Ertürk
AI Firm

Three questions:

  1. What is the first lead-source payload, and which GoHighLevel fields, pipeline stages, and calendar must it update?
  2. Is the Twilio number A2P/10DLC-ready, and are the opt-in record, required disclosure, and STOP language already approved?
  3. What is the approved qualification script, where must Claude hand off to a licensed agent, and which 20 cases should define acceptance?
8

Hi J_C1, sub-60-second speed-to-lead with an AI qualifying layer on top is close to what I run in production today, on WhatsApp rather than SMS: replies in under 30 seconds, 24/7, AI qualification, document sending, appointment creation and rescheduling, CRM logging, and handover to a human the moment the conversation leaves its defined scope. Voice-note transcription is already part of that build, which should help when you reach the live-call phase.

Since you already have the logic mapped, three notes on phase 1 rather than a pitch.

State filtering: I would put the NC/SC check before the AI layer, not inside it, so an out-of-state lead never consumes a Claude call and never enters the booking branch.

Booking and reminders: the failure point is rarely the initial booking, it is reschedules and cancellations coming back from the GHL calendar. I build that as a two-way sync keyed on the appointment ID, so a reminder never fires on a slot that has moved.

Speed to lead: sub-60 seconds is comfortable if the first SMS is fired by the webhook itself and the AI qualification starts on the lead’s reply, rather than waiting for a model response before the first touch.

Stack I work with daily: n8n, Claude and OpenAI APIs, Twilio, REST APIs and webhooks, Airtable, WhatsApp Business API. Background: state engineer, fifteen years running industrial and commercial operations before specialising in automation.

Happy to walk you through the WhatsApp agent live, screen shared, on a real conversation. It is the fastest way to judge whether I build the way you need. Remote, and I can cover US EST hours.

Jamal

9

Hi, I build production n8n + GoHighLevel systems, including AI agent qualifying
flows and CRM sync, close to what you’ve described here. Two relevant examples:

  1. A Retell AI voice agent + WhatsApp chatbot for a UK fibre ISP that handled
    6,700+ live customer conversations and auto-routed 2,400+ tickets across
    7 departments into their CRM via 6 n8n workflows, saving roughly 21,000
    agent minutes.

  2. A full GoHighLevel implementation: 30+ calendars, ~10 pipelines with
    automations, invoicing and contract workflows. I also hand-built a Jobber
    CRM OAuth2/GraphQL integration when the off-the-shelf connector couldn’t
    do what the client needed.

On your Phase 1 specifically (state-based filtering, conditional branching for
qualified vs nurture, sub-60-second SMS response, appointment reminders), that
maps almost directly onto work I’ve already shipped, so translating your
existing architecture into working workflows is the straightforward part.

Available immediately, remote, any timezone works on my end (Pakistan, UTC+5).
Happy to hop on a call once you’ve had a look.

Muhammad Haris
hariswassan001@gmail.com | linkedin.com/in/muhammad-haris-wassan

10

Hi J_C1 — your mapped phase-1 architecture is a good fit for a bounded implementation milestone.

I work under Automation Note and can deliver the first slice in writing, without a client-facing call:

  • lead webhook intake, validation, consent/opt-out handling, and duplicate protection
  • deterministic NC/SC routing before any model call
  • GHL contact, opportunity, and calendar mapping
  • Twilio first-response flow and stateful handoff to Claude
  • qualified, nurture, and agent-review branches with retries, idempotency, and visible failure logging
  • synthetic acceptance fixtures, exported workflows, and a short runbook

I would keep insurance advice and licensing decisions outside the model, with production credentials, carrier registration, and live-call/voice scope remaining with your team. A first end-to-end synthetic slice can be scoped at USD 750 fixed / 5 business days against your existing architecture; the full production phase stays separate.

Public proof (synthetic examples, not insurance-client work):

If a written-first paid milestone works before any call, send the phase-1 input/output contract and required GHL/Twilio states here or through the site. If a live call is mandatory before written scope, I’m not the right fit.

11

Hey, I’m actually in Simpsonville, SC!

N8N and GHL are my area of expertise. I was one of the first people building AI Agents in 2023 and it was with n8n and GHL. I even built the GHL course for a marketing agency guru to teach agency owners GHL on the agency and submit account level. At this point I’ve done nearly 100 n8n/GHL builds like:

  • AI lead engagement
  • Extracting insights from transcripts and automatically creating contacts notes in GHL and updating custom fields
  • Automations for syncing pipeline beyond GHL capabilities
  • Calendar automations beyond GHL capabilities concerning recurring appointments
  • And so much more!!!

I’ve even built a full software that serves as a remote control for GHL, brining many of its functions into one user interface and simplifying the action down to a click or drag and drop with logic in the n8n workflow on what to do in GHL with each.

Checkout my portfolio and let’s setup a zoom or meet for coffee if you’re close!

12

Hi J_C1 — this is a strong match for what I’ve been building.

I developed an AI SMS assistant specifically for recovering missed calls: it responds automatically, answers service questions, qualifies the prospect, and can move them toward an appointment. I’ve also built an internal Android calling/SMS application, so I’m familiar with the practical issues around phone workflows, message state, API failures, duplicate events, and reliable follow-up.

For your first phase, I’d build one auditable pipeline:

lead capture → NC/SC validation → sub-60-second SMS → Claude qualification → calendar booking → GoHighLevel sync → reminders and failure alerts.

I’d keep the qualification rules, prompts, and routing configurable so the system remains understandable and maintainable as you expand it. My strongest experience is in the custom API and workflow layer rather than as a GHL-only consultant, but I’m comfortable implementing against its APIs and webhooks.

Relevant work:

I work well from a written specification and would be happy to start with a small paid milestone using test leads and sandbox credentials. If the portfolio looks relevant, I’m available for a short call to confirm the field mappings, qualification criteria, and booking rules.

— Vlad

13

Hi J_C1 — one risk I haven’t seen raised here, and it’s the one I’d worry
about most in a licensed insurance build.

The replies so far are focused on speed-to-lead and state routing. Both
matter. But the failure that actually hurts you is the model inventing
something. If Claude generates a premium number, a coverage term, or
anything that reads as an eligibility decision in an outbound SMS, you now
have a fabricated statement with a timestamp on Twilio’s servers, sent to a
consumer in a regulated market. That’s not a bug you patch later.

I learned this the expensive way. In a multi-agent research pipeline I run
on GCP, one of my models fabricated statistics confidently enough that they
passed my own review round. The rule that came out of it: the model checking
a field can never be the model that produced it, and anything unverified
gets marked unknown rather than guessed.

Applied to your phase 1 — Claude never composes free text about coverage. It
picks from approved response templates and extracts structured fields into a
schema. Anything falling outside the script routes to licensed-agent handoff
instead of improvising. Your qualification logic stays deterministic; the
model only handles language.

One question before I’d put numbers on anything: does your mapped
architecture already define the handoff trigger, or is that a call you want
the implementer to make? The answer changes the build a lot.

Ameer Hamza

14

Hi @J_C1 - this maps closely onto what I run day to day.

I operate a self-hosted n8n server (Hetzner VPS, Docker): 103 workflows, 74 of them live 24/7, plus around 20 Telegram bots. Most of them are exactly the shape you describe - webhook trigger, conditional branching, an LLM agent step, then a write back into a system of record.

Relevant to phase 1 specifically:

  • Claude API as the conversational layer, with structured JSON output parsed and validated before anything is written downstream. I do not let free-form model output reach a CRM.
    • Speed-to-lead: a sub-60-second SMS response is a webhook path, not a cron, and I would build it that way. My own flows run scheduled where latency does not matter and webhook-driven where it does.
      • Conditional and state-based filtering (your NC/SC rule) and qualified-vs-nurture branching - routine work in my flows.
        • I build the failure paths too: retries, an error workflow, and a check that measures the result rather than the exit code. I have been burned by workflows that report success while silently writing garbage, so I test for that now.
      • Straight answer on the gaps: I have not used GoHighLevel or Twilio in production. I have built against comparable CRM and messaging APIs, and I would rather tell you that up front than have you find out mid-build. If that is a blocker for you, no hard feelings.
    • Working from an architecture that is already mapped out is how I prefer to work.
  • Portfolio: https://alexolimb.github.io/portfolio/
  • GitHub: Alexolimb · GitHub

Happy to walk you through one of my live workflows so you can judge the build quality before committing to anything.

15

Before you pick a developer, there’s a constraint that will decide your timeline more than the build will: A2P 10DLC registration.

Sub-60-second SMS to inbound leads means a registered campaign, and insurance and financial-services content gets vetted harder than most. Campaign approval can run weeks, and a lead-gen use case needs your consent language and opt-in flow documented at registration time. If that isn’t already done, it’s the critical path, the n8n side is the easy part.

Two other things worth deciding before anyone writes a workflow:

  • GHL’s LC Phone versus your own Twilio. Running both against the same number causes duplicate sends and split conversation history. Pick one as the system of record for messaging before you build.
  • The 60-second target creates a race. If the AI replies before the contact record is committed in GHL, the qualification result has nowhere to land, and you lose it silently. It needs an idempotency key and a write-then-send ordering, not just a fast trigger.

Happy to talk through the rest of the design. Where Claude sits relative to GHL’s own workflows, and how to keep the AI from booking against a stale calendar.

16

Hey — one thought on de-risking this before you pick anyone.

Most of this build is predictable: webhook ingest, GHL contact/opportunity/calendar sync, Twilio hitting your sub-60-second target with NC/SC filtering. Where projects like this actually go sideways is the qualifying conversation. An LLM that improvises with a licensed insurance lead is a liability, and you normally find that out only after everything is already wired together and the estimate is spent.

So instead of quoting the full build blind, I’d scope it in two, with a small first phase: the lead-intake webhook plus the Claude qualification as a bounded state machine — fixed prompts per state, structured JSON out, deterministic branching for qualified vs. nurture — running against synthetic leads you can throw at it. You’d watch it handle the awkward ones (partial answers, someone going off-script, an out-of-state lead) before committing to the rest, and I’d be quoting the remaining work having measured the hard part instead of guessing at it. If it doesn’t convince you, you stop there.

Quick background so this isn’t coming from nowhere: I build automation that runs in production, not demos — a document-conversion tool in daily use at a public office in Argentina, a SaaS I run end to end, and open-source n8n + Anthropic Claude workflows on GitHub (sebastianquinteros08-cmyk (Sebastian) · GitHub). I’ve integrated plenty of CRMs over REST and webhooks; GHL’s API is well documented and I’d be building against the architecture you already mapped, not proposing my own.

Based in Buenos Aires (UTC-3), so solid overlap with your hours. I can record a Loom walking through the state machine before any call — for this kind of thing it’s usually faster to watch it run than to talk about it.

Sebastián

17

Hi — I build production n8n/GHL automation workflows, including lead-gen pipelines with state-based filtering, conditional branching, and SMS follow-ups via Twilio. I’ve also integrated Claude API for the AI-agent layer. Your project looks like a great fit for my recent work — happy to share portfolio examples and answer any questions here.

LinkedIn: https://www.linkedin.com/in/pavlo-veresiuk-295b84213

18

Hi J_C1,

I reviewed your phase 1 architecture and requirements. I have strong experience building multi-agent pipelines in n8n, integrating GoHighLevel (GHL), Twilio, and Claude API for lead qualification and automated appointment booking.

Here is how I can help bring this to production:

Speed-to-Lead Automation: Setting up webhook triggers from GHL/Twilio to handle sub-60-second SMS responses with persistent lead tracking.

Deterministic Logic & State Machine: Implementing strict NC/SC state-based filtering and conditional branching to route leads reliably between qualification, calendar booking, and nurture paths.

AI Agent Integration: Structuring bounded Claude API prompts and custom function/tool calls for accurate qualification before syncing data back to GHL CRM.

I am ready to work directly from your mapped workflow logic to build and test this system.

Let’s connect or hop on a call to discuss your timeline and project details.

19

@J_C1

We already shipped this for one of our clients few days ago. Let’s have some quick discussion and will ship it in next 24 hours.

You can book a call here: Calendly

20

Hey @J_C1

I can turn your mapped architecture into a production-ready multi-agent lead pipeline using n8n, GHL, Twilio, and Claude. I’ll connect lead capture, sub-60-second responses, AI qualification, booking, state filtering, CRM updates, and follow-ups into one reliable system.

• Build connected AI-agent workflows
• Implement NC/SC qualification logic
• Integrate GHL, Twilio & Claude
• Automate booking and reminders
• Test production workflows end-to-end