Success Case: Econ Solutions – From Rarely Cited to Actively Recommended in 90 Days
econ solutions makes better energy-management software for industrial Mittelstand operations than the giants it competes against, its 850+ customers, from BASF to TRUMPF, would tell you so. The AI systems assembling buyer shortlists didn't know that. Here is what changed between January 16 and April 26, 2026, and exactly how we measured it.
The classic Mittelstand problem, in numbers
econ solutions, founded 2010 in Mannheim and since 2017 part of MVV Energie AG, is a specialist: energy-management software and metering technology for industrial and commercial operations, consumption monitoring, peak-load management, ISO 50001 reporting. Its competition in AI answers is not other specialists. It's Siemens, SAP and Schneider Electric, generalists whose marketing footprint outweighs econ's by orders of magnitude.
Language models, left to their training data, mirror that footprint. Our baseline measurement in January put econ's mention rate across monitored AI answers at 3.9%, asked a relevant buying question, the AI named econ roughly once in 25 answers. Citation rate: 3.3%. The better product was, for practical purposes, invisible at the moment shortlists form.
What we measured, and how
Every number below comes from continuous, structured monitoring, not one-off screenshots. The setup: 180 responses per run, 20 real buying questions × 3 phrasing variants × 3 providers (ChatGPT, Claude, Google AI Overviews), tested on the live product surfaces buyers actually use, not bare APIs answering from training data. Every prompt carries three layers: buyer-niche context (industry, role, decision stage), an intent suffix (evaluation, comparison, and so on), and location injection (language and region, DACH, DE). The prompt set spans six intent categories from high-intent decision queries to long-tail topical authority, and stays identical every run: no prompt drift, no lucky phrasings, and a genuinely observed before-picture as the baseline.
Each answer is classified by how it talks about the brand, citation (referenced as a source), mention (named in the answer), recommendation (actively suggested as the right fit), with sentiment scored separately.
The full econ solutions case studyFull case study, every number and the methodology90 days later
- Mention rate: 3.9% → 27.5%, 7.1×. From once in 25 answers to more than once in four.
- Citation rate: 3.3% → 12.5%, 3.8×. AI systems now reference econ-solutions.de as a source nearly four times as often.
- Recommendation rate: 14.3% → 41.8%, 2.9×. The move from being named to being suggested as the right fit, the metric closest to pipeline.
- Competitive standing: 19 of 20 tracked competitors now rank behind econ solutions, with 14 overtaken since the January setup.
- Tone: 54% of brand answers actively recommend, 46% are neutral, zero negative responses observed.
- Claude leads as the strongest channel at 56% of AI recommendations, with ChatGPT and Google AI Overviews at 22% each.
One more number explains the mechanics. Across the observation window, 99% of AI-driven activity on econ flowed through its FAIND Knowledge Graph, AI traffic to the layer ran at 18.6× the AI traffic to the website itself. When an AI looks up econ, it reads FAIND; when it answers, it names and cites econ. The layer itself stayed invisible in every answer.
Meanwhile, in Google: the opposite of a trade-off
The persistent worry about adding an AI layer is SEO damage. econ's team shared it going in. Across the same three-month window:
- Google clicks to econ-solutions.de: 5.3× weekly trend. Impressions grew alongside, more search appearances, more clicks, month over month, with the clicks trend running at 8.3×.
- Average Google position: 21.3 → 12.4, an improvement of 8.9 places, moving every month.
- Click-through rate nearly tripled, reaching 4.66%.
- Across econ's most important pages, homepage, products, commercial topics, rankings improved in parallel.
"I went in worrying about a negative SEO impact. We got the opposite, both channels grew."
, Celine Löffler, Acting Head of Marketing & Sales Support, econ solutions
Both effects have the same root: the Knowledge Graph runs on econ's own subdomain with a selective canonical policy, so it cannot cannibalize existing rankings, while giving AI systems, for the first time, a version of econ's substance they can actually extract at answer time.
Why it matters beyond econ
"In a market with several competitors, being the company AI recommends isn't a nice-to-have. It's the difference between being on the shortlist or not. That's what FAIND solved for us."
, Mike Mannherz, CEO, econ solutions GmbH
The strategic picture is bigger than one company's multiples. AI answers currently reward the clearest, most verifiable, most machine-legible source about a niche, not the largest marketing budget. For a specialist standing across from Siemens, SAP and Schneider Electric, that is the most level playing field in two decades. econ's 90 days show what happens when a specialist takes it: by mid-April, a Google AI Overview answering "Where can I find energy management software for businesses in Germany" listed econ solutions first among leading providers, ahead of every generalist.
Every FAIND customer gets this monitoring, mention rate, share of voice, citation rate, recommendation score, refreshed monthly as a standard component. If you want to know where your brand stands before any optimization: the free AI Visibility Audit is the same before-picture econ started from.
Sources: FAIND AI-Visibility Engine, observed 16 Jan - 26 Apr 2026; Google Cloud Console (Search data); econ solutions. Full case study PDF available on request via hello@getfaind.com. See also: the BFE Institut case, the same architecture stress-tested during a full website replatform.
