AI Construction Estimating: 10 Software Tools Compared

At a glance

In production: at OCBM, three agents run on plan sets with every quantity tied to a source drawing; at John Cockerill, piping takeoff takes 80 to 90% less time and the fabrication bill of materials comes out ten times faster. Both are deployments by Mirage Metrics engineers inside the contractor's own tools.

See AI quantity takeoff for contractors: quantities read from your plan sets, each traced to its source sheet.

AI estimating software reads a plan set, extracts quantities and turns them into priced line items that an estimator reviews, instead of tracing takeoffs by hand. The ten tools compared below, Togal.AI, STACK, ProEst, Mirage Metrics, Kreo, Buildxact, Autodesk Construction Cloud Takeoff, DESTINI Estimator, Procore and PlanSwift, differ on one thing more than price: whether they hand you an auditable takeoff or a final number. This comparison covers what each one does well, where it breaks, what the pricing models look like, and what a rollout actually involves.

Why Construction Estimating Needs Dedicated AI Tools

Manual takeoffs from plan sets are slow, error-prone, and lock estimators into repetitive tasks instead of value judgment. AI-powered takeoff software automates quantity extraction from drawings and documents, letting teams focus on pricing logic, risk assessment, and bid strategy.

The competitive pressure to bid faster without sacrificing accuracy has made AI estimating tools table stakes for GCs and subs managing 50+ bids per year. Yet most tools treat estimation as a black box: they output a final number rather than structured, reviewable line items that match how estimators actually work.

How Estimating Works Without AI

Traditional estimating starts with an estimator opening a PDF or DWG set, identifying scope boundaries, and pulling dimensions by hand with annotation tools, CAD markup, or a spreadsheet. Each measured element, whether rebar length, wall area, concrete volume, or linear feet of MEP runs, gets a quantity, a unit cost from a historical database or a published reference such as RSMeans, and a line item total. On a small project with a stable design that works well enough.

Three bottlenecks appear as soon as the package grows. A bid set running to hundreds of sheets needs either several estimators or several days. Every revision forces a partial or full re-takeoff, with the risk of overlaps and omissions each time. And subcontractor bids arrive carrying their own takeoffs, so catching a quantity discrepancy means checking line by line before anything is committed.

What AI Changes in the Workflow

An AI estimating agent reads the plan set end to end, identifies drawing types and scope, and extracts structured quantities: linear measurements, surface areas, volumes, material callouts, and the spatial relationships between them. It needs no CAD model, no manual digitization, and no rule written for every building system. The output is a takeoff matrix organized by assembly and material, not a single number at the bottom of a page.

What actually moves is where estimator time goes. Instead of extracting data, the estimator reviews quantities for reasonableness, adjusts for site conditions, applies regional labor rates, and makes the trade-off calls. When a revision set lands, the agent re-reads it and reports only what changed, so re-costing covers the affected line items rather than the whole package. The judgment, the bid leveling and the sign-off stay exactly where they were.

What a reviewable output looks like

Three properties separate an auditable takeoff from a wall of numbers. Every quantity should link back to the sheet it came from, so a reviewer can click a line and land on the drawing. Each line should carry a confidence indicator, and the tool should say what share of the set it actually measured, because the sheets it skipped are where the surprises hide.

The fourth property is how gaps are reported. Scope the model could not measure should come back as a labeled allowance, never as a zero, since a zero reads as scope that does not exist. Extraction quality also tracks plan quality: clean CAD PDFs hold up well, while hand-drawn sheets and low-resolution scans need manual correction and should be tested before you commit to a vendor.

The 10 AI Estimating and Takeoff Tools Compared

The tools below cover four different jobs: pure quantity extraction, takeoff plus costing, estimating inside a wider project platform, and delivered estimating work. Read each entry against your own bottleneck rather than against a feature list.

Togal.AI, best for cloud-native takeoff automation

Togal.AI (togal.ai) ingests PDFs and plan images, extracts quantities via computer vision, and returns organized takeoff sheets mapped to cost databases. It removes the step-by-step manual scaling and counting that dominates traditional takeoff work.

Best for mid-market GCs and specialty subs (mechanical, electrical, structural) who upload many plan sets per quarter and want a cloud-first workflow with little training overhead. Limitations: results depend on plan image quality, and complex custom details or non-standard drawing layouts may require manual correction. Integration with an existing ERP or project management system is still manual in most cases.

STACK, best for photo and document-based takeoff

STACK (stack.build) uses mobile capture and AI to turn site photos, marked-up plans, and sketches into structured takeoff data. It works offline and syncs when connected, which suits field-based estimation and on-site verification.

Best for subcontractors and field supervisors who need to capture quantities on site or from client-provided image libraries, then hand off to the office for final costing. Limitations: the mobile-first design makes desktop workflows secondary, there are fewer integrations with traditional bid management platforms, and the output is typically a material list rather than a full cost estimate.

ProEst, best for detailed estimating with trade libraries

ProEst (proest.com) combines takeoff with built-in trade-specific databases and historical cost records. It is anchored on the idea that good estimates require both accurate quantities and reliable unit costs from your own cost history.

Best for established GCs and large subs with deep cost libraries, multi-year project history, and formal estimating departments that want one tool from takeoff through bid assembly. Limitations: a steeper learning curve and higher licensing cost than lighter cloud tools, and some deployments require on-premises or private-cloud hosting, which adds IT overhead.

Mirage Metrics, best for revision-heavy bids with human oversight

Mirage Metrics (miragemetrics.com) uses its Plan Reading Agent and Cost Estimation Agent within the Construction Intelligence platform to extract quantities, surfaces, linear measurements, and volumes directly from PDF and DWG plan sets, then generates structured cost data. Unlike self-serve SaaS tools, Mirage deploys via Forward Deployed Engineering: AI runs the takeoff work, your estimating team reviews and adjusts the output, and results are written into Procore, Autodesk Construction Cloud or Viewpoint through their APIs.

Best for general contractors and subcontractors on large, revision-heavy bid packages (20+ sheets, frequent addenda) who need the estimating team's judgment kept in the loop rather than a fully automated final number. It fits best when bids arrive with tight deadlines and plans change mid-bid. Limitations: it is not a self-serve SaaS tool and requires an implementation engagement with Mirage's engineering team. You do not use the software yourself: Mirage runs extraction, and you review and approve.

Kreo, best for real-time collaboration on takeoffs

Kreo (kreo.ai) ingests plans and generates takeoff tables in real time, allowing several estimators to work on the same takeoff at once and see quantity updates live. It emphasizes speed over perfection, on the view that 90% accuracy in 10 minutes beats 98% accuracy in 2 hours.

Best for fast-paced construction services firms, emergency response contractors, and bid teams working in parallel across many projects. Limitations: collaboration is confined to the web interface, exporting to your cost database or ERP still requires manual mapping, and quantity accuracy depends heavily on plan legibility.

Buildxact, best for full job costing with takeoff integration

Buildxact (buildxact.com) pairs AI-assisted takeoff with quoting, invoicing, and job costing in a single platform. The takeoff module feeds directly into budgeting and actuals tracking, giving cost-to-complete visibility from day one.

Best for residential builders, small to medium commercial contractors, and service-based subs that bid often and need traceability from estimate through final invoice. Limitations: it is not suited to very large multi-trade bids with hundreds of line items, the interface feels cramped on complex takeoffs, and the accounting and invoicing features are competent rather than best in class for large firms.

Autodesk Construction Cloud Takeoff, best for integrated Autodesk workflows

Autodesk Construction Cloud Takeoff (autodesk.com) sits within the Autodesk ecosystem, pulling plan data from Revit, BIM360, and uploaded PDFs. It is tightly bound to Autodesk's cost estimation and project management tools, which makes it the natural choice if you already run ACC.

Best for contractors deeply invested in Autodesk's BIM and project management suite who want single-vendor accountability across design, estimating, and field execution. Limitations: if Procore or Viewpoint is your primary system, Autodesk Takeoff becomes a secondary tool that requires data export, and per-user pricing adds up in large estimating departments.

DESTINI Estimator by Beck Technology, best for conceptual cost modeling

DESTINI Estimator (becktechnology.com) combines AI cost modeling with historical project data to generate parametric estimates early in design, before detailed takeoff begins. It learns from your firm's past bids and project results, building a cost model that flags scope creep and budget variance as the design develops.

Best for preconstruction teams and GCs that bid multiple project types and need early-stage (Class D) estimates, with the most value at firms holding 10 or more years of historical cost data. Limitations: it needs clean historical data to train effectively, since unreliable input produces unreliable parametric models, and it is not designed for specialty subcontractor pricing or trade-specific labor variance.

Procore, best for estimating inside a full project management platform

Procore (procore.com) is a construction project management platform with AI-assisted estimating and bid management built in. It connects estimating workflows to actual field costs, labor hours, and material consumption, creating a feedback loop that informs future estimates from your own project data.

Best for large GCs (500+ employees) that want a single platform spanning bidding, project execution, and financial close rather than a standalone estimating tool. Limitations: estimating is secondary to project management, so specialized takeoff tools extract quantities faster, and implementation with data migration typically takes months at mid-size firms.

PlanSwift, best for specialty subcontractor takeoff

PlanSwift (planswift.com) is on-premises takeoff software built for specialty subcontractors (mechanical, electrical, plumbing, roofing) that need trade-specific measurement tools and material lists. It includes templates for common scope items and automates quantity rollup for repetitive assemblies.

Best for specialty subcontractors and trade shops bidding 20 to 200 projects a year, where accuracy in material counts drives margin, with roofing, framing, and mechanical trades adopting fastest. Limitations: on-premises deployment needs IT maintenance, takeoff is manual measurement rather than computer vision, so gains come from templates and interface speed, and there is no built-in cost database, so exports go to Excel for manual pricing.

Which tool fits my firm's size and bid volume?

GC estimating at scale (100 or more simultaneous bids, 500+ employees) points toward Mirage Metrics or Procore, since both handle multi-team workflows and deadline tracking. Builders with clean drawing sets and a moderate bid volume, whatever their size, often get more from a self-serve tool such as Togal.AI, STACK or Buildxact for takeoff speed and cost lookup. Specialty subcontractors bidding 20 to 200 projects a year are best served by PlanSwift's trade-specific templates.

What AI Estimating Software Costs

Vendors price in three ways. Per-seat subscriptions dominate self-serve takeoff tools and platform modules, and they punish large estimating departments where occasional reviewers still need a login. Volume pricing charges by sheets, plan sets or projects processed, which suits teams with uneven bid flow. A services engagement, the model Mirage uses, prices the delivered work and the integration rather than the software.

The figure worth comparing is the total cost per bid, not the license line. Add the subscription, the integration work needed to get quantities into the system where you actually price them, and the estimator hours spent correcting output. Several self-serve vendors offer a trial without a credit card, which is the cheapest way to test output quality on your own plans before any of that arithmetic matters.

Does Togal.AI offer the best value for money?

It depends on whether your bottleneck is takeoff speed or bid assembly. Togal.AI is priced as self-serve SaaS and concentrates on fast quantity extraction from plan images, so the cost per takeoff is low for a team that uploads plan sets constantly and prices them in its own spreadsheets. It is less economical when quantities have to land in Procore, Viewpoint or an ERP without re-keying, because that mapping stays manual and gets paid in estimator hours.

Firms with mature cost libraries often get more from ProEst or Buildxact, where costing and bid assembly sit in the same tool. Firms with heavy addenda traffic pay less overall with a delivered model, since the re-read on each revision is the expensive part. Compare on cost per completed, reviewed bid rather than on subscription price.

Beyond Takeoff: Bidding, Job Costing and Should-Cost Models

Searches for AI estimating often mean four different workflows. Knowing which one you are buying prevents most of the disappointment that follows a pilot.

AI bidding and tendering software

Bid-side tools work after the takeoff: they compare subcontractor quotes against a common scope list, flag the lines a vendor did not price, and assemble the proposal in your own template. Machine learning helps by normalizing quotes that arrive in different formats and by matching each line to the right scope item. The scope gaps it surfaces are usually worth more than the leveling itself, because an unpriced line in a winning bid becomes your cost.

Job costing and subcontractor management for commercial contractors

A job management system with AI-driven estimating closes the loop between the bid and the actuals: committed costs against commitments, subcontractor compliance, change orders and pay applications. Procore and Buildxact both carry that loop, at very different scales. If subcontractor management is the priority, choose the platform first and connect a takeoff tool to it, because standalone estimating tools do not track commitments or retainage.

Should-cost models

Should-cost model software builds a price from the bottom up, material quantities, labor hours at a known productivity rate, equipment, overhead and margin, so you can challenge a quote line by line instead of accepting it. In construction it is the same arithmetic as a conceptual or parametric estimate, which is what DESTINI Estimator produces early in design. In manufacturing and procurement it is used to test supplier pricing before negotiation.

AI helps with the part that takes days: reading the drawing or the specification and producing the quantity base. The coefficients, the productivity assumptions and the overhead treatment stay with your team, and they are what makes a should-cost model defensible when the supplier pushes back.

Quoting for field service and small trade jobs

Not every quote starts from a plan set. Service contractors, remodelers and specialty installers often work from a spoken or typed scope description, a few site photos, or a phone-based measurement, and a lighter class of tools drafts a line-item quote from that input. These tools apply regional labor burden and a saved material cost basis, then export a branded proposal for signature.

They are the wrong choice for a multi-trade commercial bid, where the quantities have to come off the drawings and survive an audit. They are the right choice when the job is priced during the visit and the alternative is a handwritten number.

Estimating time, not just cost

Labor hours and durations come out of the same quantity base: hours per unit multiplied by quantity gives crew hours, which then feeds the schedule. Tools differ in whether they expose the productivity coefficient behind each line or bury it inside a blended unit rate. Expose it, because productivity is the assumption most specific to your crews and the one most often wrong when it comes from a national reference.

Using AI Estimating Outside the United States

Most of these tools were built around US practice: CSI MasterFormat coding, imperial units, dollars, and rate references drawn from North American data. A contractor in the Netherlands, Belgium or Germany works with a bill of quantities structured to a local standard, metric units, euros, VAT handling, and labor rates set by collective agreements. The quantity extraction transfers without difficulty, since a drawing is a drawing; the pricing layer usually does not.

Two approaches work outside the US. Use the AI for takeoff only and keep pricing in the system your team already trusts, or calibrate the cost model on your own closed projects instead of any published reference. The second is what Mirage's engineers set up on site: the agents run inside your environment and connect to the ERP that already holds your cost history, so the output arrives in your classification, your currency and your units.

How to Choose the Right Tool

The two criteria that matter most are system integration and output transparency. First, verify that the tool natively syncs with your plan and project management backbone, such as Procore, Autodesk ACC, Viewpoint, or your ERP. If it forces you to re-upload plans or copy quantities by hand into your existing system, you have added work rather than cut it. Second, confirm that the output is reviewable, structured data, with quantities, units and cost assumptions an estimator can open, adjust and approve.

If you manage revision-heavy, large bids and need AI to speed up quantity extraction while keeping human judgment in the loop, Mirage Metrics' Forward Deployed model removes the burden of learning new software and forces no manual re-entry into your cost systems. If you prefer self-serve SaaS and want to own the whole workflow, STACK, Togal.AI, or Kreo move faster upfront but demand more internal discipline around data quality and exports. For firms already in the Autodesk ecosystem or running substantial BIM workflows, Autodesk Takeoff removes vendor switching costs; ProEst and Buildxact suit teams with mature cost libraries and multi-phase bid cycles.

Test it on a bid you already priced

The fastest honest evaluation is to run a past plan set, one your team already estimated and ideally already built, and compare line by line against the known answer. You get three things from that: the quantity variance on standard assemblies, the list of items the tool missed entirely, and an hour count for the review itself. Do it on a set with addenda, so you also see how the tool handles a revision instead of only a clean first issue.

What a Rollout Involves

Most deployments run in three phases. The first is data preparation: exporting closed project data from the ERP, normalizing labor and material cost categories, and finding out how much of it is actually usable. The second is a pilot on a handful of recent pursuits, comparing AI quantities against estimates your team already produced, which is also how estimators build trust in the output. The third is production, where new pursuits run through the workflow by default.

Two things predict how smoothly that goes. Firms already running an integrated ERP such as Viewpoint, CMiC, Procore, or SAP with clean cost history can start at the pilot. Firms whose cost history sits across spreadsheets spend the first phase consolidating it. Calibrating against your own completed projects, rather than published national averages, is what makes the output trustworthy on the line items that carry the most risk, such as complex formwork or non-standard electrical loads.

Where the Return Comes From

The direct saving is estimator hours per bid, since reviewing a takeoff takes a fraction of the time producing one did. The second effect is throughput: the same team carries more pursuits in a quarter, which matters more than the labor saving at firms currently turning bids down for lack of capacity. The third is fewer quantity errors reaching the contract, which is where estimating mistakes get expensive.

Read published figures carefully, because most are measured on standard assemblies rather than the edge cases that cause disputes. One vendor publishes 50% faster bid turnaround, and from a deployment on our own side, John Cockerill cut piping takeoff time by 80 to 90 percent after putting plan reading in front of its estimators. Ask any vendor for a comparable before and after on a project resembling yours, measured after estimator review rather than before it.