The Modern Scaling Problem

Scaling is one of those stupid words that is somehow both overused and vaguely defined. Let’s break it down into something tangible and understandable.?

At its core, scaling means turning your bundle of day-to-day tasks?—?those little microprocesses you manage yourself?—?into something that can run without your constant attention.?

It means choosing what to automate and what to hand off to other people, and then learning to manage those automations and people instead of doing everything yourself. You’re spreading out the workload in a sustainable, repeatable way.

When you start a business or kick off a new project, you typically handle a lot of the details yourself. You create numerous microprocesses: drafting proposals, updating spreadsheets, managing supplier contacts, or sending out reminders. You also generate scattered bits of data?—?pricing notes, delivery schedules, time logs?—?stored in various places, often just in your head. These microprocesses and their associated data are tightly bound to your personal involvement. That’s fine at a small scale, but it becomes a bottleneck as you try to grow. Your time and energy don’t magically multiply. Eventually, you need to assign these processes to dedicated people or automations, and that’s what scaling is all about.

Think of it like building a machine out of many parts that used to be manually cranked by you. Scaling is attaching motors where your hands used to be, replacing guesswork with documented instructions, and ensuring the entire apparatus can operate smoothly with a team or with a set of automated workflows.

The Components of?Scaling

• Microprocesses: The tiny tasks you do daily?—?ordering supplies, sending reminders, updating project statuses.
• Distributed Data: Pricing notes here, order confirmations there, contract details in an email somewhere else.
• Tacit Knowledge: Stuff only you know, like special instructions for a key supplier, or how to handle an unexpected request.

Scaling is about writing these things down, turning them into structured processes, and deciding whether a human should run them or a piece of software should. Without that structure, attempts to scale collapse under their own weight.

A Case Study with Bridgewater and?oneXerp

I’m going to talk about how we tackled these challenges with Bridgewater Studio and our product oneXerp. Bridgewater started off like many companies?—?creative, resourceful, and patching together solutions with the tools at hand.?

Then they hit an inflection point where they needed to scale, but their existing processes weren’t allowing them to move fast enough. That’s when we partnered up with Eric Cup & Bridgewater to build oneXerp.?

Step 1: Scaling Ordering & Logistics

Before we implemented oneXerp, Bridgewater used Monday.com to manage ordering and logistics. This was creative and, honestly, pretty ingenious. Monday.com served as a flexible canvas for tracking requests, purchases, receiving deliveries, and coordinating transport. It worked really well when they had a small team and a manageable number of orders.

But it didn’t scale beyond a certain threshold. As more employees got involved and as the volume of orders increased, the Monday.com setup became hard to maintain. Processes got cluttered, data scattered, and the overhead of coordinating everything through a repurposed PM tool ballooned. It was time for a dedicated business system?—?something that could translate those microprocesses into a robust, scalable workflow.

With oneXerp, we documented their ordering and logistics processes, integrated them into a centralized system, and replaced guesswork with clearly defined steps and responsibilities. This moved Bridgewater from a personal, handcrafted solution to a structured process that could grow without becoming unwieldy.

Step 2: Introducing Time Tracking at the Project?Level

Another critical gap at Bridgewater was time tracking. Previously, they didn’t track time at the project level at all. Without this data, Eric had no way of knowing the actual labor costs per project. And since Bridgewater works on a lot of projects, often moving through them quickly, missing labor cost data meant missing a huge piece of the profitability puzzle.

By building time tracking directly into oneXerp, we gave them the ability to log hours per project and per task. Now, instead of lump-sum labor estimates or vague feelings about where time was spent, they have concrete data. This data feeds into their cost calculations, allowing them to see exactly how much labor goes into each project. That means more accurate forecasting, pricing, and resource allocation?—?essential ingredients for scaling.

Step 3: Evolving from an Excel-Based Estimation Tool

Before oneXerp, Eric had built a remarkably advanced estimation tool in Excel. To be clear, it was far more functional than many off-the-shelf estimation solutions on the market. Excel, flexible and powerful, served as a stage for Eric’s estimating ingenuity. But Excel, while brilliant in his hands, is less ideal for collaboration and large-scale use. It’s easy for one person to maintain a complex Excel model, but harder for a growing team to leverage it without confusion or version conflicts.

Our job was to take the best features of this Excel estimation tool?—?features that reflected deep business logic and domain expertise?—?and bring them into oneXerp. We retained the clever formulas and logic that made Eric’s Excel tool shine, but we wrapped them in a system that supports multiple users, adds collaboration features, and polishes the UI. By evolving beyond Excel, we gave Bridgewater a scalable estimation platform. Now they can price projects consistently, and multiple team members can interact with the data without stepping on each other’s toes.

The Result: A Real-Time P&L

The grand payoff of scaling these processes?—?ordering & logistics, time tracking, and job estimation?—?is the ability to produce a real-time P&L (Profit & Loss) at the project level. With centralized data and well-defined processes, Bridgewater can see their profitability in near real-time, adjust strategies on the fly, and confidently pursue larger or more complex projects. That’s what scaling really means: you’ve built a machine that doesn’t rely on one person’s memory or a patchwork of tools. You have a system (oneXerp in this case) that can flex and grow without collapsing under the weight of complexity.

Final Musings

Scaling means turning tacit knowledge and personal routines into explicit, documented systems. It means choosing tools and platforms that can handle the influx of data and interactions without devolving into chaos. For Bridgewater, scaling wasn’t just about getting bigger; it was about getting smarter, shifting from personal heroics to sustainable processes.

If you find yourself juggling too many details in your head, managing tasks across random channels, and relying on personal hacks to keep things running, it might be time to formalize and scale. The reward is not just efficiency, but a platform for growth that can handle whatever challenge comes next.

About the Author

Sam Hilsman is the CEO of CloudFruit? and BotOracle. If you’re interested in investing in BotOracle or oneXerp, or if you’d like to become a developer ambassador for BotOracle, visit www.botoracle.com/dev-ambassadors.