From 4-Year Rebuild to 6 Months: How Duro and First Resonance Rewired Hardware PLM with AI

Company Profiles

Duro Labs is a cloud-native, API-first PLM platform built for modern hardware companies. Founded in 2018 by Michael Corr and team, Duro targets the emerging generation of hardware engineers who bring software-development sensibilities to physical product design — companies that want working PLM in weeks, not months, and expect their tools to talk to everything via API.

First Resonance is a factory operating system (OS) for mission-critical manufacturing. Founded by Karan Talati, who cut his teeth at SpaceX building the data pipelines that connected operational and manufacturing data, First Resonance now powers 50 to 60 companies in aerospace, defense tech, robotics, and energy. The platform sits between engineering and the shop floor, ensuring the right information reaches every step of production.

Both companies operate at the frontier of what hardware PLM can look like when you start from scratch with modern assumptions — cloud-native, API-first, and increasingly, AI-native.

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The Challenge

Duro: The Weight of a Four-Year-Old Platform

By 2024, Duro had a PLM platform that worked — but it was built the slow way. The original codebase took four years to mature. New features required full engineering cycles, extensive specification work, and multiple rounds of feedback before anything reached customers. For a startup competing against both legacy PLM giants and a wave of better-funded cloud competitors, that pace was a structural disadvantage.

Duro's founding insight — that hardware engineers increasingly think like software engineers and want tools that reflect that — was proving true in the market. But the internal tooling and development culture hadn't kept up. Corr had watched AI tools transform developer productivity in other sectors and wondered whether the same leverage was available in PLM.

The specific pressure point: a competitor starting from scratch in 2024 could catch up to Duro's feature set far faster than Duro had built it the first time. That realization forced the question — could AI compress the rebuild the same way it was compressing new development?

First Resonance: Connecting Islands of Manufacturing Data

First Resonance's challenge was architecturally different. The platform needed to connect what Talati calls "traditionally disconnected operations" — the islands of data that live in engineering systems, shop floor tools, supplier portals, quality management systems, and work instruction platforms. Getting all that to talk required custom integrations, which historically meant long engineering cycles.

When Anthropic's Model Context Protocol (MCP) emerged as an industry standard in early 2025, First Resonance saw an opportunity: use MCP to dramatically simplify how their platform connects to external systems. The question was how long it would take to build the first real MCP integration that could go to customers.

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What They Did

Duro: AI as Co-Developer

Corr's pivot to AI started with an observation at a startup conference in 2024: AI wasn't just going into products, it was being used to build them faster. Companies starting today could compress years of development into months. He came back and created an internal Slack channel where the entire Duro team started sharing AI tools and practices — Claude, ChatGPT, Cursor, whatever worked.

The shift had three layers:

Developer acceleration. Duro engineers adopted Cursor as their primary IDE, with Claude as the AI layer. The primary gains were in codebase comprehension — asking the AI to summarize how a specific feature was implemented so that new additions wouldn't break established architecture — and in boilerplate and unit test generation.

Product manager as developer. The more transformative change happened at the PM level. Corr, a double ECS graduate who had drifted from hands-on coding as Duro scaled, found he could write production-quality code again using AI assistance. Complex PLM business logic that he understood deeply but couldn't easily spec for developers, he could now implement directly, iterate on, and put in front of customers in a single cycle. The PM/developer feedback loop — historically measured in weeks — collapsed to hours.

Rapid prototyping. Duro used AI-assisted vibe coding to build prototype features fast enough to show customers before full engineering investment. Mock it, validate it, then build the real thing — with AI handling the initial scaffolding.

The result: the AI-assisted version of Duro's platform was rebuilt in six months. The original took four years.

First Resonance: MCP as the Integration Layer

First Resonance's AI journey followed a different arc. Talati described three distinct eras of trying to embed AI in the product:

• 2023 (ChatGPT wrapper era): Tried wrapping LLMs for manufacturing queries. Underwhelming for industrial applications, though it seeded ideas.
• 2024 (Fine-tuning era): Invested real engineering effort in fine-tuning models for specific manufacturing tasks. The output-to-effort ratio was poor; the approach was quietly shelved.
• 2025 (MCP era): The release of a mature Model Context Protocol spec in early 2025 changed the equation entirely.

The MCP integration Talati referenced took two days to build. The equivalent in 2024 took two months and produced a result that worked inconsistently.

The difference: MCP gave First Resonance a standardized way to expose its data and actions to AI agents, without building a custom bridge for every integration scenario. Instead of writing bespoke translation layers, the team wrote once to the protocol and the AI agents handled the rest. It's the "USB-C for AI" analogy that has caught on across the industry — and at First Resonance, it delivered a measurable 30x compression in integration development time.

The manufacturing-specific application First Resonance targets with AI isn't automation of decision-making — it's augmentation of human judgment. The platform surfaces anomaly patterns, highlights when a quality nonconformance resembles a previous one, and flags work instruction gaps. It doesn't close issues automatically. It makes the human reviewing the issue faster and better-informed. Talati's framing: meet the AI where your customers can trust it, then expand.

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Results

| Metric | Before AI | After AI | Change | |--------|-----------|----------|--------| | Platform rebuild time (Duro) | 4 years | 6 months | ~8x faster | | MCP integration feature time (First Resonance) | 2 months | 2 days | ~30x faster | | PM-to-production code cycle (Duro) | Weeks | Hours | >10x faster | | Companies served by First Resonance | — | 50–60 mission-critical | Growing |

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Lessons Learned

1. The fear moment is the turning point. Corr's shift from "AI is interesting" to "AI is existential" came from a single realization: a competitor starting today could match Duro's feature set faster than Duro built it. That fear is a signal worth acting on.

2. Two failed attempts before the right framework. First Resonance's MCP success was preceded by two failed AI integration approaches. Talati didn't treat those as wasted effort — they identified what didn't work fast enough to reach the approach that did.

3. AI changes who can build. Duro's experience showed that AI doesn't just make engineers faster — it changes who can contribute code at all. PMs, founders, and domain experts can now implement logic they couldn't previously spec clearly enough for others to build.

4. Trust before automation. Both companies explicitly chose not to push AI into fully automated decision flows. First Resonance's approach — surface anomalies, recommend resolution paths, let humans decide — builds the track record needed to expand AI authority over time. Rushing to full automation without the trust foundation destroys adoption.

5. Standardized protocols unlock order-of-magnitude gains. The 30x compression First Resonance achieved wasn't from a smarter model — it was from a better interface standard (MCP). Protocol-level changes matter more than model-level changes for most manufacturing integration work.

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Implementation Advice for Similar Companies

If your team is still treating AI as a feature to add rather than a development tool to adopt internally, you are compounding your time disadvantage. Start with the internal use case — AI-assisted code development, PM-to-code, rapid prototyping — before worrying about what AI features to surface to customers.

For PLM integrations specifically: evaluate MCP as your interoperability layer. The two-day vs. two-month difference is not a Duro or First Resonance anomaly. It reflects what happens when you use a protocol that AI agents were designed to work with natively, rather than building around a custom bridge every time.

For manufacturing companies evaluating cloud PLM: Duro and First Resonance represent what the category looks like when the development culture is modern from the start. If you are running a 30-person hardware startup and your PLM evaluation is between a legacy on-premise system and a cloud-native platform, the six-month rebuild story is relevant to how quickly your vendor can respond to your feature requests, too.

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About the Source

This case study is drawn from AI Across the Product Lifecycle Episode 3, a podcast conversation with Michael Corr (CEO, Duro Labs) and Karan Talati (CEO, First Resonance). All metrics and outcomes are sourced from that conversation. See also: [[Duro PLM Spotlight]], [[First Resonance]], [[Cloud PLM vs Enterprise PLM]], [[PLM for Hardware Startups]].