How One Builder Scores B2B Leads 1-10 in Seconds With n8n + AI

is article, you’ll understand exactly how that workflow is structured, why it’s a sellable service for small agencies and B2B firms, and how to build a first version yourself with n8n and an LLM like Google Gemini 2.5 Flash. Low-code automation tools like n8n and fast, inexpensive LLMs have made it realistic for a solo builder to ship something that used to require a dev team. The pain point in this case is extremely common: inbound inquiries arrive, someone has to read them, and the good ones get buried under the bad ones. That’s the opportunity.

Lots of service businesses still triage leads by hand, and they understand the cost of a lost deal intuitively. You don’t have to sell them on AI. You sell them on never missing a hot lead again. According to the original poster, their client runs an agency that receives dozens of project inquiries every month.

The team had to manually read every submission, figure out who was serious, and write back. The author describes the core issues like this: Most leads had zero budget or vague project details. Filtering out the junk ate up hours of manual review. By the time the team replied to high-budget, ready-to-go clients, those prospects had often already hired someone else.

In other words, the agency was paying twice: once in wasted staff hours, and again in lost revenue from slow responses. The builder’s stated goal was to “completely remove the human bottleneck.” The system the author built does three things: Captures every inquiry from a web form. Scores each lead from 1 to 10 using AI, in seconds. Routes the lead based on that score.

The tech stack is intentionally simple: n8n as the automation backbone and Google Gemini 2.5 Flash for the scoring logic. Factor Manual review (before) AI scoring engine (after) Who reads each lead Agency staff Gemini 2.5 Flash via n8n Time to score a lead Depends on team availability Seconds, per the author Data safety Lives in inboxes and forms Logged to Google Sheets first with a “Pending” status Prioritization Gut feel, often delayed Consistent 1-10 score Next step Someone decides manually Routed automatically by score Here’s how the author structured the build, based on the shared write-up. A web form captures the key qualification fields: budget, timeline, and project details. Before the AI touches anything, an n8n Google Sheets node logs the raw submission with a “Pending” status.

The author calls this a crucial safety net: if the AI API crashes, no lead data is ever lost. This is the detail that separates a demo from a system a client will actually trust. If you sell this as a service, lead with this point. Business owners care far more about “no lead ever disappears” than about which model you use.

Next, the lead data is passed to Gemini 2.5 Flash, which evaluates the submission and returns a score from 1 to 10. The publicly available portion of the post doesn’t detail the exact prompt, so you’ll need to design your own scoring rubric with your client. A practical starting point is to have the model weigh the same fields the form collects: how concrete the budget is, how soon the timeline is, and how specific the project description is. A tip from experience: ask the model to return structured output (for example, a score plus a one-line reason).

The reason field makes it much easier to debug bad scores and to show the client why a lead was ranked the way it was. The score drives what happens next. The author describes routing leads “intelligently” based on their score. How you split the tiers is up to you and your client, but a common pattern looks like this: High scores: instant alert to the sales owner (Slack, email, or SMS) so they can reply fast.

Middle scores: a nurture sequence or a request for more details. Low scores: a polite automated reply and a log entry, with no staff time spent. Treat these tiers as suggestions, not the author’s exact setup. Tune the thresholds with real data after the first few weeks.

This case is a client project, which points to the clearest way to monetize it: build lead qualification systems for businesses that receive inbound inquiries. The source doesn’t share what the builder charged, so you’ll need to price based on your market and the value you deliver. Marketing, design, and dev agencies are a natural fit because they get custom project requests with wildly varying budgets. You build the workflow, connect it to their form and CRM or sheet, and charge a setup fee.

APIs change, prompts need tuning, and clients want tweaks. A small monthly retainer for monitoring, adjusting score thresholds, and reporting is a reasonable add-on. Hosting and API costs vary, so check the official n8n and Google sites for current pricing before quoting anything. Once you’ve built this for one niche, the structure transfers to others with inquiry-heavy pipelines, such as B2B consultancies, commercial contractors, or software firms.

Rework the form fields and scoring rubric for each vertical and you have a repeatable offer. Be realistic with clients and with yourself: AI scores can be wrong. A vague but serious lead might score low. Build in a way for humans to review low scores occasionally.

Garbage in, garbage out. The engine is only as good as the form fields. If the form doesn’t ask about budget or timeline, the AI can’t score them. Speed only matters if someone acts.

Routing a hot lead instantly is pointless if nobody answers the alert. Ongoing costs. You’ll have n8n hosting and LLM API usage. Check the official sites for current pricing.

Results vary. The original post describes the problem and solution, not a specific revenue lift. How much this helps any given business depends on its lead volume, sales process, and follow-through. Here’s a checklist you can start on today to build your first version and turn it into an offer.

Set up n8n. Choose self-hosted or cloud (check the official site for current pricing) and get familiar with webhook and Google Sheets nodes. Get a Gemini API key. Create one through Google’s developer tools and test a simple prompt inside an n8n AI or HTTP Request node.

Build the intake form. Collect at minimum budget, timeline, and project details, mirroring the author’s setup. Send submissions to an n8n webhook. Add the safety net first.

Log every raw submission to Google Sheets with a “Pending” status before any AI step runs. Write a scoring rubric. Define what a 9-10 lead looks like versus a 1-2. Prompt the model to return a 1-10 score and a short reason in a structured format.

Update the sheet. After scoring, write the score and reason back to the same row and change the status from “Pending” to “Scored.” Configure routing. Use an n8n IF or Switch node to send high scores to an instant alert, middle scores to a follow-up, and low scores to an automated reply. Add error handling.

If the AI call fails, keep the row as “Pending” and notify yourself so nothing slips through. Test with real examples. Run 10-20 past inquiries (with permission) through the system and compare AI scores with how the team would have rated them. Adjust the rubric.

Package the offer. Write a one-page pitch focused on outcomes: faster replies to serious prospects, fewer hours spent on junk leads, and zero lost submissions. Then reach out to a handful of local or niche agencies and offer a pilot. Start small, prove it on one client’s real pipeline, and refine from there.

The builder in this case started by solving one client’s frustrating problem, and that’s often the most reliable way to find a service people will pay for. Based on a write-up by the original builder on r/n8n: I got tired of my client losing hot B2B leads to slow response times... Originally published on SoloAgentFlow.