Clearstory built its business automating change orders, the out-of-contract work that quietly drives a huge share of project cost risk. But the real story Cameron Page told on Bricks & Bytes is what sits underneath that workflow: roughly 1.3 million change orders, now categorized by AI, flowing through a platform that sees around $3.5 billion in change orders a month. That dataset is the asset. It is the kind of thing nobody can clone quickly, and it is exactly what makes the difference between a useful tool and a defensible company. Here is why the data, not the software, is the moat.
Every general contractor knows the change order nightmare. You have 30 subcontractors on a project, each sitting on dozens or even hundreds of change order requests, and your only window into your real cost exposure is an email you send every couple of weeks asking everyone to send over their change order log. It comes back in 30 different formats. Somebody cross-checks every line by hand and types it into the financial software. That, as Cameron Page put it on the podcast, is how the industry understands its cost risk. By hand. In email.
Clearstory, the company Page founded in 2018 after a decade in commercial construction, exists to kill that process. Think of it as bill.com for change orders: instead of emailing logs around, every sub posts the change order request on one shared network, and both parties get a real-time view of what is outstanding. GCs often write it straight into their subcontracts, so posting on Clearstory becomes the only valid way to submit.
That workflow is the product people pay for. But on the show, the most interesting thing Page said had nothing to do with the workflow. It was about what the company has quietly accumulated underneath it. Because once you become the place where change orders live, you end up sitting on something far more valuable than a faster approval cycle.
Software workflows can be copied. A multi-year, industry-spanning, AI-categorized dataset of real change orders cannot, at least not quickly. Clearstory’s defensibility is shifting from what its tool does to what its data knows.
Why change orders are the perfect place to own the data
The highest-stakes paperwork in construction
Change orders are not a side process. As a GC, Page noted, pretty much everything centers around change order exposure. It is the number one variable cost driver on a project. When Clearstory raised its Series B, its lead investor framed the change order process as representing 10 to 20 percent of industry-wide construction spend, a slice that until recently was almost entirely devoid of technology.
That combination, enormous financial stakes plus near-total absence of software, is exactly where a data moat can form. Every change order carries a story: an owner wanting the job done faster, a design change, an unforeseen condition. The cost gets recorded, but the context, the why, usually lives in email threads, phone calls, and meeting notes. When a project manager leaves, that history walks out the door with them, and whoever runs closeout becomes, in Page’s words, a forensic accountant trying to recreate the financial history of the project.
Capture that process at scale and you do not just speed up approvals. You build the only structured record of why projects actually cost what they cost. That is the raw material almost nobody else in construction has in clean, comparable form.
When you come in at the end of the job to do closeout, you’re like a forensic accountant. You’re trying to go back in time and recreate the financial history of the project.Cameron Page, CEO of Clearstory, on Bricks & Bytes
From paperwork to a moat
What 1.3 million categorized change orders unlocks
Here is where it gets interesting. Page explained that most of the valuable information in a change order normally gets stripped out the moment it is logged. You give it a title, attach it to a cost code and a subcontractor, enter a dollar value, and that is it. Worse, plenty of change orders never even make it into the financial system because they get rejected first, taking their context with them. The fact that a change went through five revisions before anyone logged it is meaningful data, and it usually vanishes.
One of Clearstory’s first AI features attacked exactly this. It automatically assigns a reason category to every change order request a customer sends or receives, using pre-built categories the customer can customize. Page said the database now holds around 1.3 million change orders, and they are all categorized, which is the part that turns a pile of records into an actual asset. Categorized data is queryable data. Uncategorized data is just storage.
What that unlocks goes well beyond reporting inside the app. With categorized history, the platform can say that on an average hospital project of a given size, you tend to carry a certain amount of overtime, trade damage, or potential extra cost. That can feed directly into the allowances a contractor needs in a guaranteed maximum price, and it can be round-tripped back into pre-construction tools to inform the next estimate. The change order data, in other words, becomes a forecasting engine.
- Scale takes years. 1.3 million change orders are the byproduct of being the default network across thousands of companies, not something a competitor spins up in a quarter.
- Breadth matters. Page said Clearstory works with a large share of the biggest GCs plus every trade vertical, down to small painting and cleaning firms, so the data spans the whole market, not one slice.
- Context is captured, not lost. By living between companies, the platform keeps the revision history and reason codes that normally disappear into inboxes.
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Clearstory is now building AI agents on top of each core workflow, aiming to cut change order cycle time from weeks and months down to hours or minutes. Page described a change notification agent that helps decide who a design change should go to and roughly what it might cost, a pricing agent so subcontractors can respond almost instantly, and a review agent to check incoming change orders. The agents are useful on their own. They get a lot more useful trained on 1.3 million categorized examples.
That raised the natural question on the show: is this the kind of capability a Procore or an Autodesk would want to own? Page stayed focused on customer value rather than speculating, but the logic is hard to ignore. The platform already integrates with Procore, Autodesk, HCSS, and others, and change orders touch the whole industry. A tool with clear ROI for every stakeholder, sitting on a dataset nobody else has, is precisely the sort of thing the big platforms have been assembling through acquisition.
For founders watching, the lesson is cleaner than the M&A speculation. Page did not set out to build a data company. He set out to fix a workflow he hated as a project manager. The data moat was a byproduct of owning a high-stakes process end to end. That is usually how the durable ones get built.
| Layer | What it is | How easily copied | Defensibility | Who benefits |
|---|---|---|---|---|
| The workflow | Shared change order network between GCs and subs | Replicable over time | Low to medium | GCs and specialty contractors |
| The network | Thousands of companies posting on one platform | Hard, needs critical mass | Medium to high | Every party on a project |
| The data | ~1.3M categorized change orders with context | Very hard, years of scale | High | Estimators, pre-con, owners |
| The agents | AI tools trained on that categorized history | Hard to match without the data | High and compounding | Everyone in the workflow |
Clearstory is a change order communication platform for construction, founded by Cameron Page in 2018. Instead of GCs and subcontractors emailing change order logs back and forth in inconsistent formats, everyone posts requests on one shared network with real-time visibility into what is outstanding. It also handles time-and-material tickets, review workflows, and pricing. (Source)
They are the number one variable cost driver on most projects. Clearstory’s Series B investor described the change order process as representing 10 to 20 percent of industry-wide construction spend, an area that was almost entirely without dedicated technology. Poor change order tracking is a major source of cost overruns and disputes. (Source)
Workflows can be rebuilt by competitors. A multi-year dataset of around 1.3 million categorized change orders, spanning most of the largest GCs and every trade vertical, cannot be cloned quickly. On the podcast, Page explained that the categorization is what makes the data queryable and useful for forecasting cost on future projects. (Source)
An early feature auto-categorizes the reason for every change order. Page said the company is now building agents on each workflow: a change notification agent, a pricing agent so subs can respond fast, and a review agent. The goal is to compress change order cycle time from weeks and months to hours or minutes, with the agents trained on the platform’s categorized data. (Source)
Clearstory closed a $16 million Series B in 2024 led by Prudence, bringing total capital raised to around $35 million since launching in 2018. Investors include Industry Ventures, Jackson Square Ventures, Building Ventures, GS Futures, and Cloud Apps Capital Partners. (Source)
It is a fair question. Clearstory already integrates with Procore, Autodesk, and HCSS, and change orders touch nearly every project. A tool with clear ROI for all stakeholders, sitting on a unique categorized dataset, fits the profile of capabilities large platforms have acquired before. On the show, Page declined to speculate and emphasized delivering customer value instead. (Source)
Clearstory: About Us and company history
BusinessWire: Clearstory closes $16M Series B
Clearstory: Announcing our Series B
Yahoo Finance: Clearstory secures $16M Series B
Building Ventures: Our latest investment in Clearstory
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