PILARS vs Beam AI:
AI Takeoff Compared

What each tool is

Beam AI is an AI estimating platform focused on automating the manual quantity extraction step — feeding it your plans and getting measured quantities back without a human clicking through every page. It targets estimators, subcontractors, and GCs who are looking to reclaim hours in the preconstruction phase.

PILARS takes PDF blueprints and reads them into quantities and bills of quantities (BOQs) across a range of trades. The aim is the same — remove the hand-measuring grunt work — but PILARS packages the output as a structured BOQ, ready to price rather than just measure. Both sit squarely in the preconstruction stack and compete for the same estimator workflow.

The practical question isn't which one sounds better in a demo; it's which one covers the trades you actually bid, produces output in the format your process needs, and costs what you can justify per job.

Time savings claims

Beam AI reports that users save 15 to 20 hours per week (Beam AI, 2026). That figure is consistent with the broader industry pattern: an 8 to 16 hour manual takeoff can drop to 15 to 60 minutes with AI-assisted extraction (appintent, 2026). The range is wide because it depends heavily on plan quality, trade complexity, and how much human review the first-pass output actually needs.

Time saved is the primary ROI lever for either tool. But time saved is only valuable if you redirect those hours into more bids submitted — or if your existing estimators are stretched so thin that the bottleneck is genuinely hours, not pipeline or business development. An estimator who saves 12 hours a week but bids the same number of jobs as before hasn't captured the value.

When evaluating either platform, run a real set from your backlog and clock the review time yourself. The vendor-quoted hours are directionally correct but the number that matters is your number on your plan quality.

Takeoff-to-BOQ workflow

A BOQ — bill of quantities — is the bridge between raw takeoff measurements and a priced tender package. It organises measured quantities into line items, typically by CSI division or trade section, so your pricing team (or the same estimator) can apply unit rates without having to cross-reference separate documents. Without a clean BOQ output, a takeoff tool saves half the work and leaves the other half on the table.

When comparing both tools, look for clean Excel or CSV export so quantities flow into your estimating spreadsheet or software without manual transcription. CSI division tagging matters if you're coordinating multi-trade packages or handing off to a sub. Equally important is addenda handling: when the architect revises the drawings mid-bid, you need to be able to re-run the affected sections and have revised quantities replace the old ones without rebuilding the entire BOQ from scratch.

• Clean Excel/CSV export — quantities should land in your spreadsheet without re-keying
• CSI division tagging — important for multi-trade coordination and handoffs
• Addenda re-export — revised quantities should flow without a full redo

Pricing model

PILARS prices at $100 per trade per plan, with no per-seat fees. If your team has four estimators and you run ten takeoffs a month covering two trades each, the cost is the same whether one person runs all ten or four people split them. The bill scales with the work, not with headcount.

Category per-seat tools — across AI estimating and broader takeoff software — run from $1,700 to $3,500 per seat per year (MeltPlan, 2026). At that range, a team of three estimators costs $5,100 to $10,500 annually before a single takeoff is run. For a firm bidding in volume with a stable team, per-seat may pencil out. For a smaller shop, or one that adds temporary estimating capacity on busy quarters, per-trade pricing is typically cheaper and more predictable.

The question to ask before signing either contract: does headcount or bid volume drive your cost over the next 12 months? If you plan to grow the team, per-seat costs compound. If you plan to grow bid volume with the same team, per-trade costs scale proportionally.

How to choose

Start with trade coverage. Make a list of the five trades you most commonly takeoff and confirm each tool covers them with reasonable first-pass accuracy. A tool that does electrical and plumbing well but stumbles on mechanical is only useful for part of your bid portfolio. Coverage gaps mean you're maintaining two workflows, which erases some of the time savings.

Test on your own plan quality before committing. Send a real set — ideally a recently completed bid you know the answer to — and compare the AI output against your numbers. First-pass accuracy varies by drawing style, scan resolution, and how atypically the architect has organised the sheets. A demo on a clean sample set tells you less than a trial on a messy real job.

Finally, compare pricing against your actual headcount and how it's likely to change. And ask both vendors for their data and training policy: does your plan data get used to train the model? Is it stored, and for how long? For work under NDA or in sensitive sectors, this matters as much as the price.

• Match trade coverage to the trades you actually bid, not just the headline list
• Test first-pass accuracy on your own plan quality, not a vendor-supplied sample
• Compare pricing model against your current and projected headcount
• Confirm each vendor's data retention and model training policy

Questions estimators actually ask