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Togal.AI

Togal.AI, Inc.

AI Detection
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PRICING:
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about

Togal.AI is cloud takeoff software that uses computer vision to detect, measure, count, compare, and label project spaces on architectural plans in seconds. Upload drawings, auto-name sheets, and let the engine find areas, walls, and objects, then review, adjust, and export. Collaboration helps estimators work together, while TogalGPT adds natural-language search over plans and specs. Faster takeoffs mean more bids and fewer hours tracing PDFs by hand across preconstruction teams.

Features

1

AI detection, measuring & labeling

Upload PDFs and let Togal.AI detect project spaces automatically—rooms, areas, and linear elements—then measure and label them to AEC conventions. Computer vision accelerates the first pass so estimators can review boundaries, adjust classifications, and lock quantities before export. The result is speed with control: fewer clicks to get credible areas and lengths, while keeping the ability to fine-tune edge cases that matter for pricing, alternates, and scope notes in preconstruction cycles.

2

Counts, comparisons & revisions

Count fixtures and objects with AI assistance and compare drawing revisions to surface changes quickly. Visual diffs highlight added or removed elements, and side-by-side views keep context as you validate updates. This lowers the risk of missed scope and speeds alternates pricing. Estimators can accept, reject, or refine detections, then rerun comparisons without starting over—useful during late design shifts when deadlines are tight and every click matters for accuracy and throughput.

3

Cloud collaboration & auto-naming

Work in a browser with shared projects, comments, and version history so teams coordinate without swapping files. Auto-naming cleans up sheet titles on upload, making large sets navigable and searchable fast. Role-based access enables parallel takeoffs by discipline or area. Centralized storage reduces duplicate work and preserves a traceable record of quantities and edits for later review—useful for audits and handoffs between estimators, PMs, and precon leaders.

4

TogalGPT: ask plans in plain language

Use natural-language queries to find details across plans, specs, and related docs. Ask about room sizes, finishes, counts, or scope notes, then jump to the relevant sheet and location. This shortens the path from question to answer and reduces context switching between documents. Estimators spend more time validating quantities and less time hunting references—especially on complex projects with many addenda and late updates. It’s a quicker way to surface facts while keeping takeoffs moving.

5

Exports, QA & integrations

Export quantities and annotations to downstream estimating tools or share reports with stakeholders. Togal’s workflow emphasizes review: lock areas, tag assumptions, and keep a record of changes so QA is easier when numbers move. Integrations and open formats help teams fold Togal into existing precon stacks without rip-and-replace. The platform’s focus on speed plus auditability makes it practical to scale takeoffs across portfolios while maintaining consistency from bid to bid.

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Recomended For

Estimating teams, GCs, and specialty contractors that need faster, consistent quantity takeoffs on PDF plans. Ideal for firms juggling alternates, late design changes, or multi-building projects where collaboration and version control matter. Also fits owners’ reps and precon leaders who want traceable quantities and quick answers to scope questions without deep CAD skills. Good for education, healthcare, retail, and multifamily portfolios with repeating spaces.

What it solved

Replaces manual tracing and spreadsheet handoffs with automated detection, measuring, and collaboration. It fixes slow first-pass takeoffs and reduces missed scope after revisions by flagging changes and preserving a reviewable record of edits. Teams click less, turn alternates faster, and spend more time on pricing and risk. Natural-language search shortens hunts for details, increasing confidence in quantities while keeping workloads predictable ahead of bid deadlines.

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