Feedback requested: Building an Enterprise-grade Automation Suite for Info-Agencies & Academies (3 MVPs)

[Marwan1]

Hi everyone,

​I’ve been working on a suite of advanced workflows tailored specifically for Info-Agencies and E-learning Academies using n8n. My goal was to move away from basic, linear flows and implement enterprise logic (data validation, brand protection, error handling).

​Since I’m fine-tuning these assets, I would love to get some feedback from senior expert automators here on how to optimize them further, and see if any Info-Agency owners find value in this setup.

​Here is the breakdown of the 3 MVPs I built:

​1. Automated Testimonial & Reputation Generator

​How it works: Triggered by a native n8n Form Submission.

​The Logic: It checks the user rating via an IF Node. If the review is \le 3 stars, it triggers an instant Telegram Alert for crisis management and stops. If it’s > 3 stars, a second IF Node checks the text length. If it’s too short (< 40 chars), it triggers an automated email asking the student to expand it. If it passes all checks, Gemini cleans up the copy, parses a structured JSON, saves it to Google Sheets, and broadcasts it to the team’s Telegram channel.

​2. Competitor Intelligence Board (Market Research)

​How it works: A Gmail Trigger intercepts competitor newsletters on a dedicated inbox.

​The Logic: An AI Agent (Gemini/OpenAI) reads the email copy and extracts a strict JSON containing: competitor name, marketing angle, offer presence (true/false), pricing, and a brief strategic summary. Data is appended to Google Sheets. A second on-demand workflow aggregates the weekly data through code, feeds it to a Gemini Report Model, and sends a highly detailed strategic briefing directly to the CEO’s Telegram.

​3. Lead Capture & Qualified Assistant Chatbot

​How it works: Ingests raw data from Meta/LinkedIn lead forms or web webhooks.

​The Logic: An AI Agent performs deep lead qualification based on the prospect’s pain points and budget, routing high-priority leads to Slack/Telegram notifications, routing them to the CRM, and generating personalized booking emails, while sending polite, resource-rich rejections to out-of-target leads.

​My questions for the community:

​For the Testimonial workflow, how would you handle data types dynamically if the rating comes as a string instead of a number in certain edge cases?

​To any Info-Agency owner or operator here: Does this structure solve a real bottleneck in your operations? Which one of these 3 would you implement first?

​Looking forward to your technical feedback and suggestions!

[Michael_Frostbutter]

First of all welcome! @Marwan1

Solid structure. The crisis-detection branch on low ratings is the right instinct, most people skip it and just collect the good reviews.

On your direct question (string vs number rating):

This bites people because the modern IF node does strict type validation. A form field comes in as “3” (string), your IF compares against 3 (number), and the branch silently behaves wrong. Don’t try to handle it inside each IF. Normalize once, upstream.

Drop an Edit Fields (Set) node right after the trigger and coerce everything to the type you expect before any logic runs:

rating -> {{ Number($json.rating) }}

If you want to be defensive against blanks or garbage like “five stars”, do it in a small Code node instead so you control the fallback:

const raw = $json.rating;
const n = Number(raw);

return [{
  json: {
    ...$json,
    rating: Number.isFinite(n) ? n : null,   // null = unparseable, route to manual review
  }
}];

Then your IF nodes only ever see a real number, and null becomes its own branch (flag it, don’t let it fall through as a 5-star). This “normalize at the boundary, validate once” pattern is worth applying to all three workflows. Treat the trigger output as untrusted and clean it in one place rather than scattering Number() casts across every node.

Two more things while you’re hardening these:

• The AI JSON extraction in #2 and #3 will eventually return malformed JSON. Wrap the parse so a bad response routes to a retry or an alert instead of killing the run. A Structured Output Parser or an explicit try/catch in a Code node both work. Don’t assume the model always returns clean JSON, because it won’t. Lean on deterministic code here.

• Watch your Telegram/Slack notifications for loops and spam. If a batch of leads or reviews comes in at once, make sure you’re not firing one alert per item into the CEO’s DMs. Aggregate where it makes sense.

To your second question: of the three,

#3 (lead capture + qualification) is the one most operators would implement first. It touches revenue directly and the qualification + routing logic saves real time every day.

#2 is great but it’s a “nice to have” until lead flow is handled.

#1 is the easiest win to ship and demo, so it’s a good confidence-builder if you want a quick one in production.

Happy to dig into any of them.