中文版

The AI automation partner for operations-heavy businesses.

AI systems that eliminate operational waste before it compounds. Implemented and measured by someone who has done it at scale.

Background

Philip Chen

AI & Automation Consulting

I have worked inside manufacturing and engineering operations across aerospace, energy, and defense — embedded with the teams doing the actual work, fixing the things that were slowing production down.

Previously at Meta, I built operational software to ensure Facebook, Instagram, WhatsApp, Reels, Ads, and 15+ product areas met their server resource demands while balancing cost and operational constraints.

I translate between what the business needs and what a system has to do. In physical operations, systems run in real time, failures propagate, and reliability requirements are significantly higher than software-only systems. This is where I combine operational precision with intelligent automation.

First Resonance

On-site operations consultant for advanced manufacturing in aerospace, energy, and defense.

Astranis couldn't safely continue production. Their factory and engineering teams were running on different versions of reality.

Read case study

Cost: production halts · engineering rework from stale BOMs · compounding risk as build complexity grew

Brought in to find why product updates weren't landing reliably across Astranis's design and manufacturing systems. During high-frequency release cycles, product updates failed to propagate or arrived inconsistently between the design system and the manufacturing OS. Factory teams built against stale BOMs. Engineering assumed their changes had landed. Neither team had a reliable way to verify. The gap was widening as volume increased.

The problem wasn't the tooling — it was the integration layer underneath. The data pipeline couldn't handle concurrent release events at that volume. I rebuilt the propagation architecture so updates arrived reliably under load, with visibility when they didn't.

71% integration latency reduced

Production
resumed They made their launch

Venture-backed satellite manufacturer, 510-person engineering and factory team.

At Meta, $20M a year in infrastructure was being wasted. No one knew where, and no one had time to find out.

Read case study

Cost: $20M+ annual over-provisioning · weeks-long planning cycles · analyst time consumed by spreadsheet maintenance

Brought in to find where the spend was going — and why the teams closest to it couldn't see it.

Across Meta's 20+ product areas, infrastructure requests were submitted manually, planned in spreadsheets, and coordinated across multiple teams. The process worked well enough that nobody questioned it — until I looked at what it was actually costing. Waste was distributed invisibly across hundreds of small decisions made with incomplete data.

I traced the waste to the planning layer, not the teams. Replaced the manual request process with purpose-built tooling, eliminated spreadsheet workflows, and gave capacity teams real-time visibility into consumption versus allocation.

The system scales — as infrastructure grows, the waste doesn't.

$20M over-provisioning prevented per year

7× core tooling speed improvement

300+ hrs recovered annually from ops teams

Technology company, global infrastructure team serving 20+ product areas.

At Joby Aviation, Helion Energy, and others — scan failures, inventory drift, work orders stuck before production could move. Treated as normal. They weren't.

Read case study

Cost: manual correction baked into every shift · inventory counts that drifted · work orders requiring fixes before production could move · hours lost to rework treated as normal operating cost

Brought in across multiple manufacturing customers running the same recurring drag. Scan failures, inventory drift, work orders needing manual correction before anyone could move forward. Each error was small. Across a shift they added up to significant lost time and unreliable data coming out of the floor.

The real problem was not operator error. The flows were designed around how the software worked, not how the floor worked. Workers were being asked to adapt to the tool instead of the other way around.

I went into the field, watched how work actually happened, and rebuilt the barcode, inventory, and work order flows around how floors actually run. Not how the software assumed they ran.

$750K account expansions from redesigned flows

Error rates and rework fell sharply across customers on the redesigned flows.

Multiple manufacturing customers, high-mix factory floor operations.

Let's find your bottleneck.

30 minutes. You describe what feels broken or expensive. I'll tell you honestly whether I can fix it.

philip@unlockops.co Send me a message.

unlockops.co