Togal.AI ✓

Togal.AI is an AI-powered construction takeoff platform, based in Miami, Florida, that automates the detection, measurement, and comparison of quantities directly from building plans -- a workflow traditionally done by estimators manually clicking through drawings to count doors, measure walls, and tally square footage trade by trade. Togal's own claims put its detection accuracy at up to 98% and its takeoff speed at roughly 5x faster than manual methods, positioning it as an AI-first alternative to traditional takeoff software like PlanSwift or Bluebeam Revu's measurement tools.

Togal.CHAT is the platform's most distinctive feature: a conversational AI interface that lets estimators "talk" to construction plans directly, asking natural-language questions about a project rather than manually searching through drawing sets or symbol libraries -- a meaningfully different interaction model than clicking through layers and pages in a traditional takeoff tool.

One-click drawing-set comparison is a second genuinely practical feature: rather than manually cross-referencing a revised drawing set against a prior version to catch what changed, Togal quantifies those changes automatically, directly addressing a specific, well-understood pain point in fast-moving bid and change-order cycles where drawings get revised repeatedly.

The platform ships trade-specific versions -- specialized takeoff tooling for drywall, electrical, HVAC, plumbing, painting, landscaping, and general contracting -- rather than a single generic measurement tool retrofitted across every trade, letting subcontractors get a takeoff experience tuned to their specific scope rather than a one-size-fits-all interface.

Togal has also picked up a meaningful string of industry recognition alongside its customer growth, including awards from Startup City, Built Worlds, and a spot on PreConstruction Magazine's Top 50 list -- a pattern of repeated, independent industry recognition (rather than a single one-off award) that tends to correlate with sustained rather than one-time press-cycle attention.

Pricing runs $299/month per user on the Growth plan (billed annually), which includes unlimited automated takeoffs, unlimited AI chat prompts, and both internal and external team collaboration; a Business plan (4+ users) adds onboarding, a dedicated support representative, SSO, and volume discounts at custom pricing. Named customers include DPR Construction, Clark Construction, and Consigli Construction, alongside a substantial base of specialty trade contractors in painting, drywall, and flooring -- and the company has been recognized on PreConstruction Magazine's Top 50 list and by several other industry awards.

Editor’s takeBIM•STACK editor's take: Togal.AI earns its place among the more credible AI-estimating tools specifically because its core claims -- 98% detection accuracy, 5x speed -- are attached to a genuinely large, named customer base spanning major general contractors and a long tail of specialty trades, rather than resting on a single flagship case study. The honest tradeoff is cost: at $299/month per user, it's priced at the high end of this catalog's estimating tools, so firms should weigh that against the time savings on their own actual takeoff volume before committing.

Togal.AI's core takeoff workflow is designed to be considerably faster to learn than traditional manual takeoff software, since the AI handles the bulk of detection and measurement automatically rather than requiring an estimator to learn a complex manual measurement toolset first. Togal.CHAT further lowers the barrier for new users by letting them query plans in plain language rather than needing to master a symbol library or layer-management system upfront.

The trade-specific modules mean an estimator's ramp-up time depends partly on which version they're using -- someone working exclusively in the electrical or HVAC module has a narrower, more tailored tool to learn than someone using the general-contracting version across all trades. Teams adopting Togal.AI at scale should expect the platform's own onboarding and training support (included at the Business tier) to meaningfully shorten this curve versus self-directed learning alone.

Firms migrating an existing manual or semi-manual takeoff process to Togal.AI face a modest workflow-adjustment period beyond the software itself -- shifting an estimating team's habits from measuring drawings by hand to reviewing and verifying AI-generated quantities is a different skill (verification and exception-handling rather than manual measurement), and teams that build a habit of spot-checking AI output against known project types tend to build trust in the tool faster than teams that either blindly accept or over-scrutinize every result.