How Engineers Deliver Customized Solutions

Our engineering team works directly with customers in dedicated Slack channels. No middlemen 🙅♂️ When customers and developers can connect directly, it creates a cycle of immediate improvement. Our engineers witness problems firsthand, fix them faster, and build features based on actual usage patterns rather than guesswork about what customers might want. Once, a customer struggled with an EIP-712 transaction. The error wasn't obvious from our logs, but within minutes, our engineer identified the issue and helped them resolve it. We also spotted a recurring bug and built a minor feature update based on our findings. Not every company can or should expose its engineering team this way. It requires mature developers who communicate well, set clear boundaries around response times, and provide explicit documentation of feature requests versus quick fixes. The payoff is huge, though: engineers build with real users in mind rather than abstract personas. Our transaction success rates and reliability metrics have improved since implementing this system. Direct customer-to-engineer communication shouldn't be a revolutionary concept. It should be standard for infrastructure companies that are really serious about building solutions.

When people, processes, and data are disconnected, we ship complexity to downstream teams. I’ve learned that the fastest path to custom solutions is to make configuration decisions early, with one place that holds the rules, options, and constraints across design, engineering, and manufacturing.     Look at what’s working in wind. A major OEM consolidated variability data into a single platform that spans DBOM, EBOM, and MBOM. They moved configuration upstream, validated buildable options before release, and handed off over 80 configuration parameters from sales to execution. The result was faster customer response, fewer ERP changes, and cleaner engineering change control.     The pattern is consistent. When configuration is scattered, lead times stretch and quality wobbles. When you build a common variability backbone, teams stop re-creating the same work, and changes like HSE actions or supplier shifts land reliably across every product variant.     Here’s the practice I use with engineering leaders in complex operations: define one variability model that the whole value chain trusts. Configure products early to prove feasibility and manufacturability. Tie change management to that model so updates apply across plants and systems without breaking schedules.     If you’re ready to reduce rework and respond faster, let’s compare notes on making configuration the calm center of custom work. 

There’s a role quietly shaping the future of enterprise AI: the forward-deployed engineer. AI breakthroughs are everywhere. But the real challenge? Deployment. 1. What is a Forward-Deployed Engineer? Not your typical software developer. An FDE embeds directly inside a business unit. They don’t build generic products; they build custom solutions for one specific client or team. They’re part engineer, part product thinker, part internal consultant. ⸻ 2. What do they actually do? Think of the FDE as a tactical operator. They: - Sit with end users to map real workflows - Identify friction, bottlenecks, and inefficiencies - Build full-stack solutions; from data pipelines to UIs - Integrate AI models into legacy systems - Iterate fast and deploy often They don’t hand off code; they own it until it works. ⸻ 3. Why does this role matter now? AI is no longer about proving what’s possible. It’s about making it practical. Enterprises don’t need another model demo. They need someone who can translate AI into business outcomes and that works inside their actual stack, with their actual people. FDEs make that leap happen. ⸻ 4. What makes a great FDE? Not just technical skill. You need: - Product sense: What’s worth automating? - Systems thinking: How does this process actually work? - Communication: Can you align engineers, users, and execs? - Grit: Can you navigate internal politics and outdated systems? ⸻ 5. Why this isn’t just “a dev with a customer-facing hat” This is a distinct mindset. It’s not about building the perfect system. It’s about building something that works here and now, under constraints, in production, at scale. FDEs don’t just deliver software. They deliver change. Learn more about this role in this recent blog from a16z: https://lnkd.in/gvRsiVim

Why Trek Health's Unique Approach to Product Demos is Changing the Game! In today's fast-paced digital age, product demos have become the norm for most companies. They're the go-to method for showcasing a product's features and capabilities. However, at Trek Health, we've taken a different route, one that's proving to be a game-changer in the industry. Here's a closer look at our unique approach: 1. **We Don't Start with a Demo** While most companies are eager to jump into a product demo, we at Trek Health take a step back. Instead of showcasing our product first, we ask our potential customers to walk us through their current workflow. This might seem counterintuitive, but it's a crucial step in understanding the real challenges our customers face. 2. **Understanding Pain Points is Key** By asking customers about their current workflow, we're able to identify the pain points they experience. This approach allows us to get a deeper understanding of the challenges they face daily. It's not just about what our product can do; it's about what our customers need it to do. 3. **Customized Solutions for Real Problems** Once we've identified the pain points, we configure a workflow in our RCM platform tailored to address those specific challenges. This isn't a one-size-fits-all solution; it's a customized approach that ensures our product aligns with the unique needs of each customer. 4. **Proposing a Solution that Resonates** After configuring a tailored workflow, we then propose a solution to our customer. This isn't just any solution; it's one that's been crafted with their specific challenges in mind. By the time we present our customized product, it's not just a demo; it's a solution to a problem they've been grappling with. 5. **The Importance of Customer Involvement** If you build a product solution without truly understanding customers' pain points, you will not go very far. By involving our customers from the get-go, we ensure that our solution is not just innovative but also relevant and impactful. **In Conclusion** The traditional product demo has its place, but at Trek Health, we believe in a more holistic approach. By understanding our customers' challenges first, we're able to offer solutions that truly resonate. It's not just about showcasing what our product can do; it's about showcasing what our product can do for *you*. So, the next time you're considering a solution for your RCM needs, remember that the best solutions are those that are crafted with your specific challenges in mind. At Trek Health, that's precisely what we aim to deliver. --- Connect with us to learn more about how Trek Health is revolutionizing the way we approach RCM challenges for mental health practices.

How Nebius Works: Building AI Infrastructure the AI-Native Way There's a certain irony when companies building the future of AI still operate like it's 2015 — annual planning cycles, rigid org charts, decisions bottlenecked at every layer. At Nebius, we think differently. As a company building GPU-native cloud infrastructure for the AI era, we believe our operating model should reflect the same principles we enable for our customers: speed, adaptability, and intelligent resource allocation. The traditional enterprise pyramid optimizes for control and predictability. Information flows up, decisions flow down, and latency compounds at every layer. We operate more like a dynamic graph. The right people connect directly around the problem — with context already shared — and move. This doesn't mean chaos or lack of structure. It means: Strategy as guardrails, not frozen plans. We set clear objectives, boundaries, and success metrics — then give teams freedom to adapt as the market shifts. In AI infrastructure, where customer needs and competitive dynamics evolve weekly, a rigid annual plan is a liability. Stable structures, dynamic execution. Reporting lines provide career development and accountability. But the actual work happens in cross-functional teams that form around a problem, ship, and dissolve — sometimes in days. When a strategic partner needs a custom integration, we don't wait for the next planning cycle. We assemble the right engineers, product leads, and partner managers, solve it, and move on. Capabilities over titles. "Who has the context and can make this call right now?" matters more than org chart position. Why This Matters for AI Infrastructure We're not just philosophically committed to this model. It's a competitive necessity. Hyperscalers operate at massive scale, but that scale comes with organizational mass. Decisions take quarters. Custom solutions require escalations. Enterprise customers wait. We win by being faster, more adaptive, and closer to the problem. When a customer needs GPU capacity configured for a specific AI workload, we don't hand them a form and a 6-week timeline. We connect the right people, solve it, and deliver. Our operating model is our product differentiation — because agility isn't just a value we talk about. It's how we ship. Dynamic networks of humans (and increasingly, AI agents) replacing fixed hierarchies. Continuous realignment instead of periodic reorgs. Managing by enterprise graph, not org chart. We're not claiming we've perfected it. But we're intentionally building a company that moves the way AI-native companies need to move — because that's exactly what we're enabling for our customers. #Nebius cc: Roman Chernin Arkady Volozh Daniel Bounds

European manufacturers face a brutal reality: they're competing against Asian rivals with 10x their engineering resources. A Director of Advanced Engineering at a major European OEM told me: "We're simply too expensive within the R&D process. Asian competitors have the power of 10 number of employees. We have to find other methods to compete." The mandate is clear. Get better products to market faster and cheaper. But R&D organizations are hamstrung by three poor options: 🚧 Build in-house: Multiple vendor evaluations. Eventually you realize no single vendor offers everything. Your mechanical engineers know what they want, but they're not software developers. Projects stall. 🚧 Deploy a heavy PLM system: Spend €10M+ on Siemens or Dassault. Bend your processes to theirs, or pay another €10M to customize. Either way, you're locked into their environment. 🚧 Hire consultants: They propose "nearly the same" solutions and build "exactly what you said" without real domain expertise. Everything gets complex. Complexity comes with costs and delays. None of these paths solve the fundamental opportunity: European manufacturers have 6 million software developers but 17.5 million engineers and scientists. That's a 3:1 ratio sitting unused. What if the solution isn't hiring more software developers? What if it's empowering the engineers you already have to write more software? The Hidden Workforce Your R&D engineers already know your workflows. They understand test rigs, simulations, data structures. They know how to package wind tunnel data for CFD. They know which parameters matter when correlating physical test with simulations. They just can't build the software to join it all because they were trained in mechanical engineering, not software engineering. A Quix customer described the breakthrough from this approach: "When engineers saw the platform, their eyes lit up. They started to build things on their own from the first day." The alternative path: a data platform that empowers engineers to build solutions themselves. Not by turning them into software developers, but by giving them tools they can use. Python? They already use it. Validating models with test data? They already do it. Automating workflows? They can write scripts. This unlocks the largest untapped resource in European manufacturing: the engineering workforce itself. The Competitive Advantage When you empower the engineers who understand the problems, transformation accelerates. Not because the technology is revolutionary, but because you've removed the translation layer. Management wants it faster? Engineers build on day one. Need it tailored? Engineers configure it themselves. Worried about vendor lock-in? Engineers own their implementations. Seventeen and a half million engineers across Europe. Most spending their days manually wrangling data, waiting for IT projects, or working around systems that don't fit. That's not a talent shortage. That's an empowerment gap.