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

Page: https://stenobird.com/podcast/just-now-possible-7481489/building-tendos-ai-how-an-agent-swarm-turns-construction-emails-into-quotes
Text version: https://stenobird.com/podcast/just-now-possible-7481489/building-tendos-ai-how-an-agent-swarm-turns-construction-emails-into-quotes.md
Podcast: [Just Now Possible](https://stenobird.com/podcast/just-now-possible-7481489)
Published: 2026-01-15T09:00:00+00:00
Episode link: https://justnowpossible.podigee.io/16-building-tendos-ai
Audio file: https://audio.podigee-cdn.net/2299436-m-1cefc3fc4a6ae94bea53572687400c67.mp3?source=feed
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JSON: https://stenobird.com/v1/public/podcasts/just-now-possible-7481489/episodes/building-tendos-ai-how-an-agent-swarm-turns-construction-emails-into-quotes
Duration seconds: 3946

## Resource

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.

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## Transcript

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