Building Sustainable AI That Actually Works
We started machiidenfile in 2021 because too many chatbot projects burned through budgets without clear returns. Our mission is simple: help businesses measure what matters and reduce waste in AI deployments.
Our Story: From Frustration to Solutions
Four years ago, we noticed a pattern. Companies were investing heavily in chatbots without knowing if they were actually helping or hurting customer relationships. So we built analytics that showed the truth.
Started in Lugano with a team of three. Our first client was a Swiss retailer who wanted to know why their chatbot had a 78% abandonment rate. We built the first version of our analytics platform in six weeks.
Developed our carbon tracking module after realizing how much energy unnecessary bot interactions consumed. Helped five clients reduce their AI-related energy costs by identifying redundant conversation loops.
Opened offices in Zurich and Geneva. Started working with financial services clients who needed strict compliance tracking. Built custom reporting tools that made audit processes actually manageable.
Launched performance benchmarking that compared chatbot efficiency across industries. Our clients started seeing 30-40% improvements in resolution rates once they could measure what was working.
Currently working on predictive maintenance features that alert teams before chatbot performance degrades. Planning to expand our environmental impact tracking to cover the full AI lifecycle by autumn 2025.
People Who Get It
Our team comes from different backgrounds, but we share one thing: we've all dealt with AI systems that looked impressive but didn't deliver. That experience shapes everything we build.
Rolf Bachmann
Spent eight years in datacenter operations before joining us. Rolf builds the tools that track energy consumption in real-time. He's obsessed with finding inefficiencies that most people miss.
Petra Lindgren
Former customer service manager who understands what good bot performance looks like from the user side. Petra designs our reporting dashboards and makes sure metrics tell useful stories rather than just showing numbers.
Common Questions About Sustainable AI
Here's what clients usually ask when they start thinking about efficiency and environmental impact.
Every chatbot interaction uses server resources and energy. When bots fail to resolve issues, customers repeat requests or escalate to human agents. That doubles the resource consumption.
We've tracked cases where fixing conversation flows reduced total interactions by 40%, which directly translates to lower energy costs and better customer experience.
See our efficiency tools →We track several things: server request volume, response generation time, conversation length, and escalation rates. Each metric connects to actual energy consumption.
Our dashboard shows estimated carbon footprint based on datacenter location and energy mix. It's not perfect, but it gives teams a baseline to work from.
Ask about custom tracking →We focus on helping clients maintain chatbot performance over time. Most bots degrade slowly as business needs change, but nobody notices until customer complaints spike.
Our monitoring alerts teams to performance drops before they become problems. This preventive approach reduces the need for expensive emergency fixes and complete rebuilds.
Learn about our training programs →Actually, smaller operations benefit more. When you're running lean, every inefficient process hurts. A chatbot that wastes customer time or requires constant monitoring is expensive regardless of company size.
We work with businesses spending as little as CHF 200 monthly on chatbot infrastructure. The analytics pay for themselves when they prevent one bad deployment decision.
