Building Tendos AI: How an Agent Swarm Turns Construction Emails into Quotes

Описание: 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.

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
Tendos AI: https://tendos.ai

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

Music License: 7YNNOYKP96OBQUKY