Anyone who has lived inside construction tendering knows the grind. "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." That painful reality is exactly why this conversation about Tendos AI caught my attention—and why it matters for product leaders building agentic AI in complex, document-heavy workflows.
I listened as Daniel Kappler and Matthias Hilscher from Tendos AI walked through how they’re automating the tendering workflow for manufacturers in the construction industry. What began as a narrow prototype—matching radiator requests to product catalogs—has matured into a full agentic system that does the heavy lifting from email categorization to offer generation. The end result: a scalable AI workflow that tackles messy inputs, orchestrates specialized agents, and produces quotes that are ready for human review—or even straight-through processing.
What impressed me most was the rigor. They validated the opportunity with a design partner, spent a week on-site observing real workflows, and then engineered a multi-agent architecture where specialized agents collaborate, including a "review agent" that checks work before anything reaches a human. They evaluate each agent independently (not just the whole chain), built custom observability when off-the-shelf tooling fell short, and use human-in-the-loop feedback to push toward a self-learning system.
From a product management perspective, this is agentic AI done right. It blends continuous discovery with eval-driven development, thoughtful UX decisions, and pragmatic guardrails. Evaluating agents individually makes debugging tractable and change detection transparent; a dedicated "review agent" mirrors code review to reduce error propagation; and custom tracing plus Agent Analytics provide the observability needed to operate AI workflows reliably at scale.
My key takeaway: "Start narrow to prove value: Tendos AI began with just radiators for one design partner before expanding to all building products"—a classic wedge strategy that accelerates learning while building credibility.
Another takeaway I’ll adopt in future roadmaps: "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." Controlling the surface area let them move faster than a purely backend integration ever could.
On measurement and reliability, I loved this: "Evaluate each agent, not just the chain: per-agent evals make debugging tractable and show exactly where performance changed." That’s true eval-driven development—aligning metrics to decision points rather than only outcomes.
Quality gates matter in automation, and they nailed it: "Use review agents: a separate agent that checks work (like code review) catches errors before they reach humans." It’s a simple pattern with outsized ROI.
Finally, the product-market signal is unmistakable: "Let customers pull you: customers asked Tendos to replace their CPQ software—strong signals of product-market fit." When buyers invite you to displace existing systems, you’re past validation and into expansion.
If you’re exploring agentic AI for enterprise workflows, the themes here are gold: the tendering chain in construction is ripe for automation; domain expertise accelerates opportunity discovery; robust entity extraction across PDFs ranging from 1 to 1,800+ pages is non-negotiable; planning patterns for creating and updating task plans matter; agents must reason about product fit against customer requirements; custom tracing and observability unlock debugging for complex agent chains; and human feedback loops pave the path to self-learning systems.
Guests: Daniel Kappler — CPO (Product & Design), Tendos AI; Matthias Hilscher — CTO (Engineering), Tendos AI.
Want to dive deeper? Listen to this episode on: Spotify | Apple Podcasts.
Explore the team and product: Tendos AI.
For builders of agentic AI, here’s my playbook distilled from this story: start narrow to earn trust and accuracy; own the interface to speed iteration; use per-agent evaluations to localize issues; add a "review agent" as a quality gate; invest early in tracing, observability, and Agent Analytics; keep humans in the loop until your metrics justify autonomy; and let strong pull signals guide your roadmap. That’s how you turn complex emails and massive PDFs into precise, production-grade quotes—consistently.
Inspired by this post on Product Talk.
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