insivo
Insivo is an AI-powered process analysis platform that interviews employees and analyzes system data to identify workflow bottlenecks, quantify their cost in hours and euros, and suggest concrete improvements with calculated impact. The platform runs continuously as software, allowing teams to onboard additional departments while its AI model becomes more precise with each interview and data point.
- Enterprise Software
- Artificial Intelligence
- Data & Analytics
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Companies often struggle to identify where their operational workflows get stuck, relying on anecdotal evidence or manual analysis that misses root causes. Process bottlenecks in order processing, purchasing, quoting, and planning go undetected, costing organizations significant time and money without clear visibility into the problem.
Solution
Insivo combines AI-driven employee interviews with analysis of company system data to build a detailed model of how an organization actually works. The platform identifies bottlenecks, quantifies their cost in hours and euros, and generates concrete improvement recommendations with calculated impact. Insivo operates continuously as software rather than a one-time consulting project, allowing teams to onboard additional departments themselves. Each interview and data point makes the model more precise, and the platform suggests which person to interview next for the biggest gain in insight.
Target Audience
Primary customers are operations managers and department heads in mid-sized companies across industries such as manufacturing, logistics, and services who need to identify and fix workflow bottlenecks in order processing, purchasing, quoting, planning, and other cross-functional processes.
Features
- AI-conducted employee interviews that capture workflow knowledge with every statement attributed to a source
- System data integration that combines operational data with interview insights to model company processes
- Bottleneck quantification in hours and euros, sorted by impact to prioritize improvement efforts
- Concrete improvement suggestions calculated per bottleneck with expected impact
- Continuous learning model that becomes more precise with each interview and data point
- "Who to onboard next" recommendation engine that identifies the highest-value interview targets
- Self-service onboarding enabling teams to extend the platform to new departments without external help