I’ve Been Building, trust me!

I didn’t go quiet because I ran out of ideas. I went quiet to rebuild the machine. Less noise. More systems. The goal hasn’t changed: ship boring, reliable workflows that beat hustle every day of the week.

Where I’ve been

CRM - The spine

GoHighLevel (newly chosen during this rebuild) now runs,

📶

capture → pipeline → follow-up → reviews.

It’s the operating spine and client management layer for most builds.

Automation layer, upgraded, not new

You already know the stack. I pushed it harder: n8n when I need control/versioning, Make when I need velocity and quick hand-offs.

Tool tests & tactics, what’s actually worth caring about

I didn’t chase shiny apps. I stress-tested what we had, and during the break I chose GoHighLevel as the new spine, then rebuilt around it and used new business knowledge to push it to failure points and fix them.

🥇

GoHighLevel as the new spine for MAGICAIFLOW

Voice agent (pre-qual + routing).
Answers basic questions, screens by criteria, and routes into the right next step. This gets its own deep dive.
Text agent across social inboxes (now a client offer).
Connected via GHL’s native FB/IG/Google My Business integrations. One job: qualify against rules and auto-schedule a free strategy call. I use it myself; clients wanted it, so it became a productized add-on.
Schedulers & team calendars → automation-driven handoffs.
Not “wired to pipelines”—wired to automations. When a task hits done or a project crosses a trigger, the next owner gets an email + SMS with link, context, and due time.
Yes, our internal copy is blunt:

‼️

“Time to get some work done, asshole — you won’t automate your entire life by staring at it.”

For critical errors I get a ping with flow name + node ID so I can jump straight to the break

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“Mario, you fucked up again — fix {flow_name} @ {node_id}.”

Recent n8n logging upgrades made tracebacks much faster

Social automations (honest status).
Posting is easy; quality isn’t.

Our blocker is trusted sources + classification/categorization.
We’re improving the taxonomy before scaling output.

Research + cold-call scripts live inside the CRM.
No loose docs. Every asset is
- attached to the contact/company. Callers see scripts inline in the dialer.
- All calls are recorded → transcribed (OpenAI Whisper + GHL native, under test) → AI-reviewed for improvement points and clearer buyer needs.

(We’ll show exact flows/screenshots in the follow-ups. Error-alert subject/payload format lands in the next post.)

What changed in my approach

Custom scrapers → real data, faster. Built per-directory scrapers to avoid blocks and speed capture. One scraper per source for reliability.
Automated enrichment (n8n + HTTP). Every lead runs through enrichment for company info, socials, contacts, and context. One record, fully armed.
Rebuilt the entire go-to-cash path. Client acquisition → sales → project management rewritten for clean handoffs and clear “done” definitions.

Operating rules (non-negotiable):

Niche focus

Speak to service-based businesses first; help others as capacity allows.

Manual first

If a process works by hand and makes money, then automate it.

Bottleneck-first

Fix speed-to-lead before anything else.

Data discipline

Standardized forms for client interviews, pre-qualification, project management, and project updates, so automation isn’t guessing.

Minimal > maximal

Ship the 3-step flow that never fails; kill 12-step Rube Goldberg builds.

Guardrails for AI

Strict prompts, tool limits, and KB retrieval. No KB = no answer.

Assistants I actually use, as a flow (manual → SOP → automate)

Manual first. I test everything by hand. No new process “because automation.” If it works and repeats, then we scale it.

Step 1 — Team Psychological Profiler

We brought in an “internal psychologist.” The team ran through business-oriented questionnaires (screenshots later). Outcome: better task alignment, clearer roles, cleaner handoffs.

Step 2 — SOP Generator

If a process works, we lock it into an SOP so anyone can follow. If the task burns > 2 hours/day, we scope automation. Frequency drives priority—nothing else.

Step 3 — Targeted Audience Researcher

While niching, this agent merged our goals/vision with real market placement. It maps accounts, roles, and talk tracks so we’re not shouting into the void.

Step 4 — Pain Point Researcher

Plain English: find the headaches our ICP actually pays to solve.
Feeds Step 5.

Step 5 — Lead Magnet Builder

Builds value assets straight from Steps 3–4 (not fluff). Practical, specific, downloadable.

Trend-Based Prompt Generator (bonus)

Not a step—more like a gym break. We built goofy prompts (e.g., “mom’s prompt”) to decompress during the grind. Fun, sometimes surprisingly useful.

Net effect: tighter roles, repeatable procedures, and automations that only exist where they pay back.

Speed-to-Lead Playbook: Voice + Text Agents That Book Calls

Angle: the exact flows for the voice pre-qual and the cross-inbox text agent (GHL native FB/IG/WhatsApp), including:

qualification rules → calendar booking,
error alerts (subject/payload with flow name + node ID),
transcript pipeline (OpenAI Whisper vs GHL native),
handoff notifications (email + SMS),
what’s not working yet (social source trust/classification) and how we’re fixing it.