Building supply companies process dozens of purchase orders daily from homebuilders, each formatted differently, often running 20-30 pages for a single project. BSGTX, a Texas building supply distributor, dedicates five full-time employees to manually extracting PO data from builders like Perry Homes across Houston, San Antonio, Austin, Dallas, and Fort Worth. AI document extraction eliminates this bottleneck by reading any PO format on the first attempt, extracting line items, quantities, SKUs, and pricing without templates or per-builder configuration.
Building supply distribution has a data entry problem that most people outside the industry do not appreciate. Every homebuilder sends purchase orders in a different format. Perry Homes formats their POs one way. Lennar formats theirs another. A custom builder in Fort Worth might send a handwritten order on a fax. A production builder in Houston sends a 30-page PDF with hundreds of line items spread across selection sheets, structural specs, and material lists.
Someone at the supply company has to open each PO, read through every page, extract the relevant data (item descriptions, quantities, SKUs, unit prices, delivery dates, job numbers), enter it into a spreadsheet, save it as CSV, and upload it to the company’s database or ERP system. Then the next PO arrives.
BSGTX, a building supply distributor operating across Texas, has five people doing this full-time. Five employees. Dedicated entirely to reading POs and typing numbers. At roughly 30 POs per day across their Houston, San Antonio, Austin, Dallas, and Fort Worth operations, and with selection documents running up to 30 pages each, the math is simple: manual PO extraction is one of the largest labor costs in their business.
Purchase order processing in building supply is uniquely painful for two reasons that compound each other.
The first is format variability. Every builder has their own PO format. Perry Homes, BSGTX’s largest client at approximately 10% of their total business, sends selection documents that can run 30 pages long. These documents specify every material selection for a home build: cabinet styles, countertop materials, flooring types, fixture specifications, trim packages. The data is there, but it is embedded in a format designed for the builder’s internal workflow, not for the supplier’s database.
Other builders use entirely different formats. Some send one-page POs with 10 line items. Some send spreadsheets. Some send PDFs generated from their project management software. A few still fax handwritten orders. The supply company needs to extract the same categories of data from all of them: what is being ordered, how much, at what price, for which job, and when it needs to be delivered.
The second reason is volume. Thirty POs per day does not sound overwhelming until you consider that each PO contains anywhere from 10 to 300+ line items, and each line item has 5-8 fields that need to be captured. At the high end, a single Perry Homes selection document with 30 pages might contain hundreds of individual material specifications. Across a full day, BSGTX’s team processes thousands of line items manually.
This combination, high format variability plus high line-item volume, is why template-based tools fail in construction supply. You would need a template for every builder’s PO format, and when that builder updates their format (which happens when they change project management software, update their specification sheets, or simply redesign their forms), you would need to rebuild the template.
BSGTX evaluated Supply Pro, a supply chain management tool, before looking at AI-based alternatives. The problem was pricing structure: Supply Pro’s per-job pricing model was cost-prohibitive at BSGTX’s volume. When you process 30+ POs daily across multiple metros, per-job pricing adds up fast.
But the deeper issue with template-based tools in construction is structural. A template-based extraction system maps specific coordinates on a document to specific data fields. “The invoice number is always in the top-right corner at position (x, y).” This works when every document follows the same layout. In construction supply, they never do.
Perry Homes’ selection documents have a completely different structure than their standard POs, which have a different structure than their change orders. And Perry Homes is one builder. BSGTX works with dozens of builders across five Texas metros. Each builder’s documents reflect their own internal systems, their own specification formats, their own ways of organizing material selections.
ACS Industries, a manufacturing company, experienced this failure mode directly. They were running a UiPath-based RPA workflow for PO processing at 400 POs per week. The system achieved reasonable accuracy on formats it was configured for, but had a 10% failure rate on POs with variable layouts. One in ten POs required manual rework because the bot extracted data from the wrong fields when the layout shifted. After switching to Lido, they achieved 99.5-100% accuracy across all vendor formats and saved 30 hours per week.
A 10% failure rate might sound acceptable in the abstract. In practice, it means that someone on the team still has to spot-check every PO to catch the ones that failed silently. The automation becomes a pre-processing step rather than a replacement for manual work.
BSGTX estimated that AI-based PO extraction could save them two full-time employees, approximately 80 hours per week of manual data entry. That is a conservative estimate based on their current volume. As they grow, the savings scale linearly because adding new builder accounts does not require adding new data entry staff.
But headcount is only part of the cost. Manual data entry in construction supply creates downstream costs that are harder to quantify.
Order errors are the most expensive downstream cost. When a human reads a 30-page selection document and types hundreds of line items into a spreadsheet, errors are inevitable. A misread quantity (50 instead of 500), a wrong SKU, a missed line item. Each error creates a cascade: wrong material is ordered, delivered to the job site, rejected, returned, and reordered. The cost of a single material error on a home build can run into thousands of dollars when you account for delivery costs, restocking fees, construction delays, and builder relationship damage.
Processing delays hurt competitiveness. If BSGTX receives a rush order at 3 PM and the data entry team is working through a backlog of morning POs, that rush order waits. In construction, timing matters. A delayed material delivery can idle an entire crew on a job site. Builders choose suppliers partly on responsiveness, and processing speed is a competitive differentiator.
Scaling constraints limit growth. BSGTX operates across five Texas metros. Growing into a new market or onboarding a new high-volume builder means more POs, which means more data entry staff. Manual processing creates a linear relationship between business growth and headcount. AI extraction breaks that relationship. The system processes the 31st daily PO as fast as the first, without overtime or hiring.
AI-first document extraction reads POs the way an experienced data entry clerk would, but faster and without fatigue. The system looks at a Perry Homes selection document, understands that it contains material specifications organized by room and category, and extracts item descriptions, quantities, SKUs, and pricing from wherever they appear on the page.
The same system processes a one-page PO from a custom builder, a multi-page spreadsheet-style PO from a production builder, and a faxed order from a small contractor. No template configuration. No per-builder setup. The AI understands document structure contextually.
For BSGTX, the workflow changes from: receive PO, open document, read pages, type data into Excel, save as CSV, upload to database. It becomes: receive PO, run through Lido, review extracted data, export to database. The five-person data entry team shifts from full-time manual entry to exception handling and quality review.
ACS Industries saw this transformation at 400 POs per week. Their previous UiPath workflow required dedicated staff to handle the 10% of POs that failed extraction. With AI-first extraction, the failure rate dropped close to zero, and the team that previously managed exceptions now focuses on vendor relationship management and procurement optimization. The tool saved them from hiring an additional FTE, and they saved 30 hours per week of processing time.
For construction supply companies processing vendor invoices alongside POs, the same extraction system handles both document types. Invoices from material suppliers, freight carriers, and subcontractors are processed through the same pipeline, with PO-to-invoice matching validating that billed quantities and prices match what was ordered.
Implementation for construction supply companies follows a straightforward pattern. Start with your highest-volume builder. For BSGTX, that would be Perry Homes (10% of total business). Upload a batch of recent POs, define the fields to extract (line items, quantities, SKUs, unit prices, job numbers, delivery dates), and validate the output against manually entered data.
Once accuracy is confirmed on the primary builder’s format, expand to additional builders. Because AI extraction does not use templates, adding a new builder’s PO format requires no configuration. The system reads the new format on the first document.
The output format matches your existing workflow. If your team currently exports to CSV for database upload, Lido outputs CSV. If you need Excel for review before upload, Lido outputs Excel. The extraction step changes. Everything downstream stays the same.
For companies evaluating the ROI: calculate your current cost of PO data entry (headcount * loaded cost per employee), add the cost of order errors caused by manual entry, and compare against the cost of AI extraction. BSGTX’s estimate of two FTEs saved at 80 hours per week gives a clear picture of the math. For most building supply companies processing 20+ POs daily, the payback period is measured in weeks.
Lido is an AI document processing platform that extracts structured data from purchase orders, invoices, and other construction documents. We work with building supply distributors, manufacturers, and contractors to eliminate manual data entry from document-dependent workflows.
Yes. AI-first extraction handles multi-page documents natively. Perry Homes selection documents running 30 pages with hundreds of material specifications are processed in their entirety. The system extracts line items across page breaks, handles tables that span multiple pages, and maintains the relationship between item descriptions, quantities, and pricing regardless of document length.
No. AI-first extraction tools like Lido read documents contextually rather than matching coordinates on templates. A Perry Homes PO, a Lennar PO, and a handwritten order from a custom builder are all processed without separate configurations. This is the primary advantage over template-based tools like Supply Pro or UiPath, which require per-format setup and break when formats change.
ACS Industries, processing 400 POs per week, achieved 99.5-100% accuracy with Lido after experiencing a 10% failure rate with their previous UiPath-based workflow. AI extraction eliminates common manual entry errors like misread quantities, transposed SKU digits, and missed line items. On clean, typed POs, accuracy is consistently above 99%. On degraded inputs like faxed or scanned orders, accuracy depends on input quality but typically exceeds 95%.
BSGTX estimated savings of two full-time employees (approximately 80 hours per week) from automating PO extraction. At an average loaded cost of $40,000-$50,000 per data entry employee, that represents $80,000-$100,000 in annual labor savings. Additional savings come from reduced order errors, faster order processing (which improves builder retention), and scaling into new markets without proportional headcount increases. ACS Industries saved 30 hours per week and avoided hiring an additional FTE.
Yes. The same AI extraction system processes purchase orders, vendor invoices, delivery receipts, and other construction documents. For building supply companies, this enables end-to-end automation: PO data is extracted when orders arrive, and invoice data is extracted when bills come in, with automatic matching to verify that billed quantities and prices align with what was ordered. This catches discrepancies before payment rather than during month-end reconciliation.
Time to first extraction is under five minutes. Upload a PO, define the fields you need (line items, quantities, SKUs, pricing), and the system extracts immediately. Production-ready workflows, including integration with your database or ERP import process, typically take days rather than months. Because no templates need to be built, adding new builder formats requires zero setup time. BSGTX’s current workflow of exporting to CSV for database upload is directly supported by Lido’s export options.
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