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GoHighLevel + AI automation systems, engineered end to end. See what we build

Our work

Systems we've built.

A large share of what we build ships under an agency partner's brand and is not ours to publish. What appears here is what we have permission to show, working automation canvases, with the client-identifying parts cropped out.

System gallery

The architecture, not the dashboard

Working canvases from builds we have permission to show. Client names, sub-accounts and workspaces are cropped out or the shot is not here at all, most of what we build ships under a partner's brand, and that is the arrangement they are paying for.

  • A GoHighLevel workflow canvas: an order-form submission trigger, a twenty-minute wait, and a condition branching into five parallel paths, four purchased-package paths that remove a tag and end, and one path that adds a tag and continues into a sequence of waits and follow-up emails.
    GoHighLevelGoHighLevelFunnelsAutomation

    Abandoned checkout, branched by what was actually bought

    An order-form submission starts a twenty-minute wait, then a condition splits on which package the buyer ended up on. Each purchase branch removes the abandonment tag and stops. The one branch where nothing was bought adds a tag and runs a three-email recovery sequence spaced two days apart, so the only person who gets chased is the one who did not buy.

  • A GoHighLevel workflow canvas running top to bottom: a trigger, a set-webinar-start-date step, then alternating wait-until-before-the-event steps and emails at three days, two days and six hours before the event.
    GoHighLevelGoHighLevelAutomationFunnels

    Webinar reminders anchored to the event, not the signup

    The sequence sets the event start date first, then times every email backwards from it: three days before, two days before, six hours before. Anchoring to the signup date instead is the common version of this build, and it is why someone who registers an hour before the webinar receives a "three days to go" email.

  • A GoHighLevel workflow canvas: a contact-tag trigger, an update-opportunity step setting the stage to Lead, then an SMS, an email, a five-minute wait, a voicemail drop, a one-day wait and a condition node.
    GoHighLevelGoHighLevelCRMAutomation

    Pipeline stage first, then the follow-up

    A contact tag starts it, and the first action is moving the opportunity, so the pipeline reflects reality before any message goes out, and a reply that arrives in ninety seconds lands on a correctly staged record. Only then does the multi-channel sequence run: SMS and email on day one, a short gap, a voicemail drop, a day’s wait, and a condition that decides what happens next.

  • A Miro board mapping a follow-up campaign: a lead added to the sales pipeline, an automated hook email, a one-day wait and a manual cold call, then a Response decision splitting into positive and negative branches that lead to an automated thank-you SMS or a booked discovery call, alongside a parallel sales track running from discovery call booked through meeting completed, proposal sent and follow-up call to a decision.
    MiroAutomationCRMGoHighLevel

    The campaign map the build was made from

    The one board here that is not a running system. It is the design the workflows were built from. Every branch is a decision the build has to answer: a cold call splits positive and negative, each with its own next action and its own wait, and the sales track runs on in parallel from booked call to proposal to decision. Agreeing this on a board first is the difference between building a campaign once and rebuilding it three times after launch.

AI agents

What the agent is allowed to do

Every one of these gives a model a small, fixed set of tools and nothing else, read the calendar, extract these fields, post to this channel. An agent that books an appointment by calling a booking tool cannot invent one; an agent asked to reply in prose can, and eventually will.

  • An n8n workflow canvas: a Gmail trigger feeds an appointment classifier built on a chat model and a structured output parser, then an agent that reads Google Calendar availability and composes a response, branching to send a reply, mark the message read, and post to Slack.
    n8nAI ChatAutomationIntegrations

    Inbound email triage into a booked appointment

    A Gmail trigger passes every incoming message through an LLM classifier that decides whether it is an appointment request. If it is, an agent queries Google Calendar for the next month, filters to confirmed events, composes a reply with real availability, sends it, marks the thread read and posts to a Slack channel.

  • An n8n workflow canvas with three colour-grouped branches, voice transaction recording, image transaction recording and document transaction recording, each running its own transcription and extraction chain before converging.
    n8nAutomationIntegrations

    Invoice capture from a text, a voice note or a photo

    Three parallel branches off one trigger, one per input type: voice transcription, image transcription and document parsing. All three converge on the same structured record, so the sender can send whatever they have to hand rather than being told which format to use.

  • An n8n canvas showing a chat message trigger connected to an AI agent, with a chat model, simple memory and an MCP client attached beneath it, and two HTTP tool nodes for getting free calendar slots and booking an appointment.
    n8nAI ChatCRMIntegrations

    Chat agent that reads the calendar and books

    A chat trigger drives an agent with a chat model, conversational memory and an MCP client. Two HTTP tools give it the only two capabilities it needs: fetch free slots, and post a booking. The agent cannot invent an appointment because booking is a tool call, not a sentence it generates.

  • An n8n canvas: a webhook node connects to an AI agent backed by a chat model and simple memory, then to an information extractor, then to a create-or-update contact node.
    n8nCRMGoHighLevelIntegrations

    Unstructured webhook into a clean CRM contact

    A webhook lands whatever the upstream system sends. An agent with memory reads it, an information extractor pulls the fields that matter into a fixed shape, and the last node creates or updates the contact, so a change of upstream payload does not become a change of CRM schema.

  • An n8n workflow canvas: a webhook node connected in a single horizontal chain to five Google Drive create-folder nodes.
    n8nAutomationIntegrations

    Client workspace provisioned by webhook

    A webhook triggers five Google Drive nodes in sequence, each creating a folder. It is the least interesting automation here and one of the most useful: onboarding admin is where a build quietly loses half a day, and a folder somebody forgot to create is found a week later by the person who needed it.

Routing and CRM writes

Search before you write

The unglamorous half of every integration, and the half that decides whether a CRM stays usable. Each of these searches GoHighLevel for the contact before it creates anything, and branches on what it finds, which is the difference between one record per customer and thousands of duplicates nobody will ever merge.

  • A Make scenario with a dense branching network of modules radiating from a router, each branch chaining HTTP requests and OpenAI calls back to a webhook response.
    MakeAI ChatGoHighLevelIntegrations

    An assistant behind the GoHighLevel chat widget

    One webhook fans out across a router into per-conversation branches, each holding its own assistant thread so two visitors chatting at once never see each other’s context. Each branch calls the model, then returns the reply to the widget through a webhook response.

  • A Make scenario: a custom webhook feeds a GoHighLevel LeadConnector create-contact module, then a router splitting into two symmetrical branches of HTTP, tools and OpenAI modules.
    MakeGoHighLevelCRMAI Chat

    Lead capture to AI qualification

    A custom webhook creates the contact in GoHighLevel first, so the lead exists in the CRM before anything else can fail, then a router sends it down one of two qualification paths, each ending in a model call and a webhook response.

  • A Make scenario: webhook, tools, CloudConvert, HTTP request, an OpenAI Whisper transcription module, a GoHighLevel search-contacts module, and a GoHighLevel add-note module in sequence.
    MakeAI VoiceCRMGoHighLevel

    Call recording to a transcript on the contact record

    A call recording is converted to a supported audio format, transcribed by a speech model, then matched to the right contact in GoHighLevel by search before the transcript is written back as a note. The search step is what stops a transcript landing on the wrong record.

  • A Make scenario: a custom webhook feeds a GoHighLevel search-contacts module, then a router branching to an update-a-contact module on one path and a create-a-contact module on the other.
    MakeCRMGoHighLevelAI Voice

    Create-or-update, on both inbound and outbound calls

    The scenario searches GoHighLevel before it writes anything. A router then takes the outbound path to update an existing contact, or the inbound path to create a new one, the small piece of logic that is the difference between a clean CRM and thousands of duplicates.

  • A Make scenario: a custom webhook feeding a router that fans out into four branches, each chaining a GoHighLevel search-contacts module into update-a-contact, create-a-contact, HTTP request and add-a-note modules, with each branch terminating in an Ignore module.
    MakeAI VoiceCRMGoHighLevel

    Call handling split four ways, before anything is written

    One webhook lands every call event and a router sends it down one of four paths. Each path searches GoHighLevel for the contact before it writes anything, then updates the record, sends an HTTP request and adds a note, or creates the contact when the search comes back empty. Paths matching no rule end in Ignore rather than falling through into the wrong branch.

Every caption describes only what is visible on the canvas. None of them claims a result, outcomes publish in case studies, with the client's approval and a stated measurement method, or they do not publish.

Why this page is shorter than most agency portfolios

Two reasons, and neither is that we have not built much.

Most of it is not ours to show. A large share of what we build ships under an agency partner's brand, under an agreement that we do not identify their clients or claim the work publicly. That is the arrangement partners are paying for. A fulfilment partner who quietly builds a portfolio out of your client work is not one worth having.

We will not publish results we cannot evidence. A screenshot of a dashboard proves nothing, anyone can produce one. A percentage with no baseline and no measurement method is not a result, it is a claim. That is why the gallery above shows workflow canvases rather than revenue charts: a canvas is a thing you can read and judge, and it is the same thing we would walk you through on a call. Ourcase studies publish the system architecture in the same spirit, and leave the outcome sections empty until a client approves numbers we can stand behind.

What to look at instead

If you are trying to judge whether we can build what you need, the honest substitute is the architecture. Read a workflow automation or AI voice agent page and see whether the edge cases we describe match the ones you have hit. That tells you more about whether someone has done this work before than any portfolio grid.

Or ask on a call and we will screen-share a live system.

Want to see a live build?

A walkthrough of a working system on a call tells you more than a screenshot ever will.

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