When people talk about the data gap in construction, they point to the enterprise layer. Large projects with fragmented documentation. General contractors managing dozens of subcontractors with no single source of truth. Developer portfolios with no visibility into delivery quality or cost variance.
That problem is real. It is also well-funded.
Procore is a $10B company. Autodesk Construction Cloud processes millions of project documents annually. PlanGrid, Fieldwire, Buildertrend: there is no shortage of capital or product effort directed at the top of the construction stack. The data layer for large-scale construction exists. It is expensive, often over-engineered for its actual users, and imperfect. But it exists.
The bottom of the stack is a different story.
Who Actually Builds Things
Canada has over 400,000 construction and renovation businesses. The vast majority run on 1 to 10 people. These are the plumbers replacing water lines in residential buildings. The electricians wiring new builds across the GTA. The renovation contractors turning basements into liveable space across every suburb in the country.
These are not informal operators. A trades business running C$150,000 to C$500,000 annually is managing real project complexity: scoping work from client descriptions, estimating materials and labour hours with meaningful cost variance, coordinating schedules across multiple active jobs, invoicing on completion, and chasing payment.
That is a real business. It just rarely has real infrastructure behind it.
Their operational data (quoting time, material costs, labour hours, job outcomes, payment cycles) lives in spreadsheets, WhatsApp threads, and closed PDF invoices. Unstructured. Unretained. Compounding into nothing.
Not because these operators are unsophisticated. Because nobody built the right tools for their scale.
What Happens When Data Does Not Compound
Every completed job contains information. What scope was quoted versus what was actually delivered. Which materials were estimated correctly and which were not. How long the job took versus how long it was priced to take. When the invoice was sent. When it was paid.
In an enterprise environment, that information feeds back into the next estimate. It trains project managers. It improves margin over time. The organisation learns.
In a trades micro-business, that information evaporates. The next quote is built from memory and instinct. The same underestimates recur. Margins stay thin not because the work is unprofitable but because the operational layer has no feedback mechanism.
This is not a discipline problem. It is an infrastructure problem.
Run your trade business on systems, not spreadsheets.
What We Already Learned Building This Once
Before Duuabl set out to solve this for Canada, we ran a construction marketplace in Estonia for three years. Over that time, we tracked EUR 123 million+ in B2B contract activity through structured, job-level workflows: replacing the spreadsheets, WhatsApp threads, and email chains with systems that captured what actually happened for every job (scope, cost, time, outcome).
The result was a specific observation: the data was there all along. It was just never structured at the source. A quote sent through a proper workflow has fields: trade type, scope, materials, labour, timeline, acceptance status, final invoiced amount versus quoted amount. That is not a data product. That is just a quote that was structured properly.
At one job, that is an invoice. At ten thousand jobs, it is something else entirely.
What the Data Becomes at Scale
At scale, structured job data starts doing things no single invoice ever could. A large enough set of actual-versus-quoted costs becomes a real pricing signal, something contractors, customers, and property managers currently have no shared reference point for. It becomes the input to a credit model banks cannot build today, because traditional lending has no visibility into a trades business’s job volume, completion rate, or payment cycle time. And it becomes a training set enterprise software has no path to, because Procore and Autodesk have deep data on large commercial projects and almost none on the C$40,000 kitchen remodel: that segment is too small for their sales motion and too fragmented for their product architecture.
That is the gap. It is also the asset.
The SaaS Is the Collection Mechanism. The Data Is the Asset.
This is why Duuabl exists, and why we are building it the way we are.
Duuabl is an operating system for trades micro-businesses: AI-assisted quoting, trade-specific workflows, integrated payments, a branded customer portal, and a peer referral network. Tradespeople use it because it saves them time on non-billable work and makes them look more professional to customers.
But every job processed through Duuabl is also a structured data point. Every quote accepted or declined. Every workflow completed. Every payment processed. Every referral made between verified peers.
That is not a side effect. It is the architecture.
The SaaS earns trust. It solves a real daily problem for real operators. It builds the relationship that makes retention possible. And as it scales, it builds the dataset that no competitor starting from scratch can replicate, because the data only exists inside a product that tradespeople actually use, at the scale and segment nobody else has prioritised.
The data layer for construction micro-businesses does not exist yet. We are building it from the bottom up.