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

Why It Matters

By eliminating manual parsing and pricing, Tendos AI cuts cycle time and errors, giving manufacturers a competitive edge in the construction supply chain. The approach showcases how agentic AI can modernize legacy CPQ processes across heavy‑industry sectors.

Key Takeaways

• Started narrow with radiators, then expanded product range
• Built web app for full control over UX
• Per‑agent evaluation simplifies debugging
• Review agents catch errors before human handoff
• Customers replacing CPQ shows strong product‑market fit

Pulse Analysis

The construction industry’s tendering process has long been a bottleneck, with engineers sifting through massive PDFs to generate quotes. Tendos AI’s multi‑agent architecture reframes this challenge as a series of discrete, automatable tasks. By delegating email triage, document extraction, product matching, and pricing to specialized agents, the platform reduces human effort from hours to minutes, while a dedicated review agent safeguards quality before any human interaction. This modular design not only accelerates response times but also creates a transparent audit trail, essential for compliance‑heavy projects.

Beyond speed, the strategic decision to build a standalone web application gives Tendos AI full ownership of the user experience. Unlike legacy CPQ integrations that are constrained by outdated interfaces, the web app enables rapid iteration, A/B testing, and direct feedback loops with customers. This agility allowed the team to validate their value proposition with a single design partner, then scale to broader product categories without re‑architecting the core system. The result is a scalable, user‑centric solution that can be customized for diverse construction segments.

Tendos AI’s emphasis on per‑agent evaluation and custom observability tools addresses a common pain point in complex AI pipelines: debugging. By measuring each agent’s performance in isolation, the team can pinpoint regressions and optimize components without disrupting the entire workflow. Coupled with human‑in‑the‑loop learning, the system continuously refines its models, moving toward a self‑learning CPQ engine. As more manufacturers replace legacy quoting software with Tendos AI, the platform could set a new standard for AI‑driven automation in heavy‑industry procurement.