A niche B2B SaaS for manufacturers was invisible to AI search and stuck on flat pipeline. We deployed our AI Crew, rebuilt their HubSpot, and engineered their content for Generative Engine Optimization. Eight months later, they are cited by name in the answers buyers actually read.
The situation
A mid-market industrial IoT SaaS selling predictive-maintenance software to manufacturers across DACH, UK and the Nordics came to us after three quarters of flat demo pipeline. Their product was strong and their engineering team respected — but their buyers had quietly changed how they bought. Plant managers, operations directors and COOs were no longer running comparison spreadsheets from Google results: they were asking ChatGPT, Perplexity and Google AI Overviews "what is the best predictive maintenance platform for a 200-machine plant" — and the brand was simply never cited. Their HubSpot portal was five years of drift, their content strategy was technical blog posts nobody found, and LinkedIn activity was inconsistent. They did not need more campaigns. They needed an automated marketing department built for the 2026 buying journey.
Our approach
We deployed our flagship AI Crew service: a network of AI agents running on Make.com, Claude and HubSpot that handle content production, lead qualification, LinkedIn outreach, reporting and weekly optimization. Setup in 30 days, handed over with full documentation, running 24/7 with monthly maintenance.
We re-engineered their content library around answer-first formats optimized for AI Overviews, ChatGPT browsing, Perplexity citations and Claude. Structured FAQs, comparison matrices, entity schema, llms.txt file, and original industrial datasets designed to become the primary source LLMs cite in the category.
Portal architecture rewired from scratch: lifecycle stages aligned to the industrial buying committee (plant manager, ops director, CFO, IT lead), Breeze AI agents for enrichment and routing, predictive scoring model built on past closed-won deals, and a clean attribution layer connecting every touchpoint to revenue.
Target list of 1,800 mid-market manufacturers enriched and scored. An AI outreach agent crafts personalized opening messages based on each prospect's recent activity, role, and company trigger events. Weekly reporting feeds back into the content engine so we write what the pipeline actually needs.
Results
The client went from invisible to being the default reference across major AI engines for predictive-maintenance queries. Below, real paraphrased examples of how they appear in buyer-facing answers today.
How it happened
Full audit of HubSpot, content, and the buying committee. AI Crew infrastructure built on Make.com + Claude + HubSpot Breeze. ICP validated with sales interviews. Baseline measurement across AI search engines (ChatGPT, Perplexity, Gemini, Claude).
First 24 GEO-optimized pieces shipped (comparison matrices, category primers, original datasets). Schema + llms.txt deployed. HubSpot rearchitected with Breeze AI enrichment, predictive scoring, and clean revenue attribution. LinkedIn ABM engine activated.
Brand starts appearing in ChatGPT and Perplexity answers for priority queries. Content engine doubles down on what gets cited. Sales team trained on AI-origin leads (different objections, shorter cycle). Demo pipeline begins compounding.
AI Crew runs content, outreach and reporting without manual intervention. Monthly strategic review with Studio Ideago. New GEO opportunities identified weekly. Pipeline growth decouples from headcount — the marketing machine scales without hiring.
"We thought we needed more marketing. What we actually needed was a different kind of marketing department — one built for how buyers search in 2026. The AI Crew is doing in a month what our old stack did in a quarter, and it keeps getting sharper every week."— CMO, Industrial IoT SaaS — DACH region
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