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When a construction company receives a bid request, someone has to open that email, parse the attached PDF (sometimes 1,800 pages describing an entire building), figure out which products are relevant, look up pricing, and draft a quote—all before the deadline. It’s tedious, error-prone, and surprisingly manual.
In this episode of Just Now Possible, Teresa Torres talks with Daniel Kappler (CTO, Product & Design) and Matthias Hilscher (CTO, Engineering) from Tendos AI about how they’re automating this entire workflow for manufacturers in the construction industry. What started as a narrow prototype matching radiator requests to product catalogs has grown into a full agentic system that handles everything from email categorization to offer generation.
You’ll hear how they validated the opportunity with a design partner, spent a week on-site watching users work, and built a multi-agent architecture where specialized agents collaborate—complete with a “review agent” that checks the work of other agents before anything reaches a human. They dig into why they evaluate each agent independently (not just the whole chain), why they built custom observability tools when off-the-shelf solutions fell short, and how human-in-the-loop feedback is pushing them toward a self-learning system.
Show Notes
Guests
- Daniel Kappler — CPO (Product & Design), Tendos AI
- Matthias Hilscher — CTO (Engineering), Tendos AI
Key Takeaways
- Start narrow to prove value: Tendos AI began with just radiators for one design partner before expanding to all building products
- Own the interface: building a web application (vs. integrating into legacy systems) gave them control over UX and the ability to iterate toward full automation
- Evaluate each agent, not just the chain: per-agent evals make debugging tractable and show exactly where performance changed
- Use review agents: a separate agent that checks work (like code review) catches errors before they reach humans
- Let customers pull you: customers asked Tendos to replace their CPQ software—strong signals of product-market fit
Topics Covered
- The tendering chain in construction and why it’s ripe for automation
- How domain expertise (CEO’s construction background) helped identify and validate the opportunity
- Entity extraction from PDFs ranging from 1 page to 1,800+ pages
- Planning patterns in agentic systems—creating and updating plans based on findings
- How agents evaluate product fit against customer requirements
- Building custom tracing and observability tools for complex agent chains
- The path toward self-learning systems through human feedback loops
Links & Resources
Chapters
00:00 Introduction to Tendo and Key Roles
01:01 Understanding the Tendering Chain
02:26 Real-World Construction Analogy
03:34 Challenges in the Construction Industry
04:48 AI’s Role in Tendo’s Product
12:59 Early Prototypes and AI Integration
18:31 Expanding Product Capabilities
28:56 Customer Collaboration and Workflow Automation
33:15 Strategic Partnerships and Technical Groundwork
34:20 Focusing on Specific Customer Segments
36:03 Product Evolution and Current Capabilities
38:17 Technical Workflow and Automation
40:12 Evaluating and Matching Product Requests
47:00 Dynamic Agent Architecture
55:29 Quality Measures and Evaluation
01:02:59 Future Directions and Customer-Centric Development
Full Transcript
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