T

Togal AI

AI-powered construction estimating software

Business Development Representative

Full-Time
No salary listed
Junior
Remote in USA
Remote
Sales & Account ManagementSales Development Representative

About the job

Requirements
  • At least 2 years of experience in outbound sales, business development, or a sales development representative or business development representative role.
  • Experience in construction and a deep understanding of the construction industry.
  • Proven cold-calling experience, including the ability to make more than 100 dials per day.
  • Strong presentation, negotiation, and closing skills.
  • Ability to engage technical and non-technical audiences effectively.
  • Experience using customer relationship management and sales engagement tools.
Responsibilities
  • Make cold calls, send emails, and conduct LinkedIn outreach daily to generate qualified leads.
  • Run customized product demonstrations and presentations for prospects.
  • Drive pipeline growth by targeting Spanish- and English-speaking prospects across the United States.
  • Exceed activity metrics, including calls, meetings, and demonstrations, as well as revenue targets.
  • Build relationships with contractors, estimators, and construction executives to open new opportunities.
  • Serve as a trusted advisor by demonstrating product knowledge and industry insight.
  • Provide frontline feedback on messaging, objections, and market trends to improve the sales playbook.
  • Represent the company at industry events, trade shows, and networking opportunities.
Desired Qualifications
  • Experience in construction technology or software as a service.
  • Startup experience.

About the company

T

Togal AI

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Website

Togal.AI provides AI-powered construction estimating software that uses deep learning to automate quantity takeoffs, measuring materials and labor for a project. The SaaS platform analyzes drawings and documents to generate quantity takeoffs and cost estimates, which users review and adjust. It differentiates itself by blending seamless integration with existing construction management tools and automating takeoffs without forcing disruptive workflow changes. Its goal is to improve estimation accuracy and efficiency so estimators can focus on strategic tasks and firms can bid and deliver projects more effectively.

Company Size

51-200

Company Stage

Seed

Total Funding

$5.4M

Headquarters

Miami, Florida

Founded

2019

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Simplify's Take

What believers are saying

  • Refresh Miami reported September 29, 2026, 95% of code now uses AI assistance.
  • Built In listed Togal.AI at 40 Miami employees on October 4, 2026.
  • Assemblies lets contractors migrate PlanSwift and On-Screen Takeoff libraries, lowering switching friction.

What critics are saying

  • Legacy rivals PlanSwift and On-Screen Takeoff already own estimator workflows and libraries.
  • Trade-specific feature races commoditize Togal.AI unless drywall expands fast beyond 2026.
  • Autodesk or Procore bundle AI takeoff features and crush stand-alone pricing.

What makes Togal AI unique

  • Togal.AI spans 97 of the 400 largest U.S. contractors and 32 countries.
  • Its 32 million labeled plans create a dataset moat competitors cannot copy quickly.
  • Assemblies, launched August 24, 2026, embeds trade-specific drywall logic into estimating.

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Benefits

  • Health Insurance
  • Dental Insurance
  • Vision Insurance
  • Unlimited Paid Time Off
  • Remote Work Options

Growth & Insights and Company News

Headcount

6 month growth

7%

1 year growth

1%

2 year growth

6%
GroundBreak Communications, LLC
Oct 7th, 2026
Can ChatGPT do construction takeoffs? New data says not yet.

Can ChatGPT do construction takeoffs? New data says not yet. by GroundBreak Carolinas on October 7, 2026 New benchmark tests how accurately AI can read blueprints, measure spaces and produce the numbers contractors use to bid. Leading AI models, including ChatGPT and Claude, scored just 2% to 34% accuracy on a new benchmark that tests whether AI can read real construction blueprints. Called Construction's Last Exam (CLE), the benchmark from Miami-based Togal.AI puts AI models through 33 real-world construction tasks. The models have to read plans, measure spaces and materials, and produce the numbers contractors use to build bids and takeoffs.For example, a model is given a set of plans and asked to measure every balcony in a building. Its answers are then checked against the correct measurements to produce an accuracy score. AI Challenges in Construction Most AI progress over the past few years has happened in language, chatbots, writing, and code. Vision is a harder problem, and blueprints are some of the hardest images to read: dense, technical documents packed with symbols, scales and notations that even advanced AI models still struggle to interpret with the precision it takes to actually build something. That gap is a quiet bottleneck behind cost overruns, slower timelines and, at scale, the U.S. housing shortage. It also raises a less obvious risk: those same plans carry sensitive project and client information, and most AI tools aren't built to safeguard it. "When you hand your blueprints to a piece of software, you're handing over everything about a project. Most AI tools can't tell you where that data goes or who sees it, and in this industry, that's not a small thing," said Patrick E. Murphy, founder and CEO of Togal.AI. "Frontier models still struggle to read a blueprint accurately, let alone protect it." Since 2019, Togal.AI has built proprietary machine-vision models specifically to understand construction drawings, training them on its own proprietary dataset of tens of millions of construction plans. That approach is fundamentally different from general-purpose large language models like ChatGPT and Claude. A Growing Trend As AI moves into data-heavy industries, a pattern is emerging: build a benchmark to test how an industry-built AI stacks up against general-purpose tools like ChatGPT and Claude. Other high-profile examples include Humanity's Last Exam, which tests AI against expert-level academic questions; SWE-bench, which measures whether AI can solve real-world software engineering problems; and ARC-AGI, which tests advanced reasoning and problem-solving. CLE is the first benchmark built specifically to measure AI performance in construction. About Togal.AI: Based in Miami, Togal.AI builds proprietary AI technology specifically for the construction industry. Trained on a proprietary dataset of tens of millions of construction plans, the company has built one of the industry's largest labeled proprietary construction datasets. Its patented AI helps contractors estimate, review and understand construction documents faster and more accurately. Togal.AI is SOC 2 certified, reflecting its commitment to rigorous data security and privacy standards for construction information. Following three consecutive years of roughly 300% growth, Togal.AI now serves more than 10,000 users across 30 countries, including 100 of the largest in the USA. The company's mission is to make construction faster, more accurate and more efficient, starting with the very first estimate.

Associated Press
Oct 6th, 2026
Togal.AI launches Construction's Last Exam to benchmark AI on 33 real-world building tasks

Togal.AI has launched Construction's Last Exam, the first AI benchmark designed specifically for the construction industry. The benchmark tests AI models on 33 real-world construction tasks, including reading blueprints, measuring spaces and materials, and producing numbers for bids and takeoffs. The company, which has been building proprietary machine-vision models since 2019, trains its AI on a proprietary dataset of tens of millions of construction plans. This approach differs from general-purpose large language models like ChatGPT and Claude, which still struggle to read blueprints with the precision required for actual construction. Togal.AI is based in Miami and now serves more than 10,000 users across 30 countries, including 100 of the largest contractors in the US. The company reported roughly 300% growth for three consecutive years.

Arizona Corporation Commission
Sep 1st, 2026
The next wave of Construction AI? Going deeper into individual trades.

The next wave of Construction AI? Going deeper into individual trades. Togal.AI Launches Assemblies, Starting With One of Construction's Most Complex Trades As AI adoption in construction grows, the industry is entering a new era: more specialized AI tools built to solve the unique workflow challenges of individual trades. Miami-based Togal.AI, the AI-powered construction estimating platform, is taking that approach with the launch of Assemblies, a new feature designed to automate one of the most detailed and time-consuming parts of drywall estimating. "Construction isn't one industry with one workflow. It's thousands of specialized contractors, suppliers and vendors, each with their own expertise," said Patrick Murphy, Founder and CEO of Togal.AI. "Drywall is where we're starting, but the bigger opportunity is to take AI deeper into those individual workflows and give each trade technology built around how they actually work." Traditionally, drywall estimators measure walls from construction drawings, then move those quantities into spreadsheets or other programs to calculate everything needed to build them, from studs and drywall to insulation, screws, labor and equipment. Assemblies brings that entire process into Togal.AI. The result is fewer manual handoffs, less re-entry of data and a faster path from construction drawings to a bid-ready scope. "Drywall is a perfect example of why construction estimating is so much more complicated than simply measuring a set of plans," Murphy said. "An estimator doesn't just need to know how many linear feet of wall there are. They need to know what goes inside that wall, how it's built and what it will take to actually price and construct it. Assemblies lets contractors take their own estimating knowledge and apply it much faster." "For contractors, estimating capacity directly affects how much work they can pursue," Murphy added. "If you can dramatically reduce the time spent building each estimate, the same team can bid more projects and focus on value engineering." Togal.AI chose drywall as the first trade for Assemblies because of the complexity and level of detail required to estimate it accurately. Users can create their own assemblies or start with Togal.AI's industry-standard drywall templates, which can be customized to match a contractor's existing estimating practices. Contractors moving from legacy estimating platforms such as PlanSwift or On-Screen Takeoff can also migrate their existing build libraries, allowing them to retain formulas and estimating rules developed over years of work. Completed calculations can be exported with formula columns and cost codes and organized by factors such as material and stud height, helping contractors carry takeoff information into estimating and procurement workflows. The launch marks the beginning of a broader strategy for Togal.AI: building increasingly specialized estimating capabilities for individual construction trades rather than treating every scope of work the same. About Togal.AI: Based in Miami, Florida, Togal.AI is one of the fastest-growing construction technologies, with thousands of users from some of the world's largest contractors. Named by the South Florida Business Journal as one of the fastest growing companies, Togal also won The Big 5 Global Construction Pitch Competition in Dubai, AGC Innovation of the year and Miami's eMergeAmericas Startup competition judged by Shark Tank's Kevin O'Leary. The company was featured on the EntrepreneurTV show Elevator Pitch, selected for Venture Atlanta, and has made BuiltWorlds' Pre-Construction 50 List four years in a row, which names the hottest construction-tech companies in the world.

WCONline
Aug 24th, 2026
Togal.AI automates drywall estimating with AI.

Togal.AI automates drywall estimating with AI. Togal.AI launches Assemblies to automate detailed drywall takeoffs and streamline estimating workflows. August 24, 2026 Togal.AI has launched a new estimating feature aimed directly at drywall contractors, using artificial intelligence to automate some of the detailed calculations that traditionally take estimators from a wall takeoff to a bid-ready scope. The Miami-based construction technology company introduced Assemblies, a new capability within its AI-powered estimating platform. Drywall is the first trade targeted by the feature, reflecting both the complexity of estimating wall assemblies and Togal.AI's broader strategy of developing AI tools around the workflows of individual construction trades. Looking for a reprint of this article? From high-res PDFs to custom plaques, order your copy today! JOIN TODAY To unlock your recommendations. Already have an account? Sign In

Design and Build UK
Aug 19th, 2026
The next wave of construction AI? Going deeper into individual trades.

The next wave of construction AI? Going deeper into individual trades. AsAI adoption in construction grows, the industry is entering a new era: more specialized AI tools built to solve the unique workflow challenges of individual trades. Miami-based Togal.AI, the AI-powered construction estimating platform, is taking that approach with the launch of Assemblies, a new feature designed to automate one of the most detailed and time-consuming [...] Aug 19, 2026. AsAI adoption in construction grows, the industry is entering a new era: more specialized AI tools built to solve the unique workflow challenges of individual trades. Miami-based Togal.AI, the AI-powered construction estimating platform, is taking that approach with the launch of Assemblies, a new feature designed to automate one of the most detailed and time-consuming parts of drywall estimating. "Construction isn't one industry with one workflow. It's thousands of specialized contractors, suppliers and vendors, each with their own expertise," said Patrick Murphy, Founder and CEO of Togal.AI. "Drywall is where we're starting, but the bigger opportunity is to take AI deeper into those individual workflows and give each trade technology built around how they actually work." Traditionally, drywall estimators measure walls from construction drawings, then move those quantities into spreadsheets or other programs to calculate everything needed to build them, from studs and drywall to insulation, screws, labor and equipment. Assemblies brings that entire process into Togal.AI. The result is fewer manual handoffs, less re-entry of data and a faster path from construction drawings to a bid-ready scope. "Drywall is a perfect example of why construction estimating is so much more complicated than simply measuring a set of plans," Murphy said. "An estimator doesn't just need to know how many linear feet of wall there are. They need to know what goes inside that wall, how it's built and what it will take to actually price and construct it. Assemblies lets contractors take their own estimating knowledge and apply it much faster." "For contractors, estimating capacity directly affects how much work they can pursue," Murphy added. "If you can dramatically reduce the time spent building each estimate, the same team can bid more projects and focus on value engineering." Togal.AI chose drywall as the first trade for Assemblies because of the complexity and level of detail required to estimate it accurately. Users can create their own assemblies or start with Togal.AI's industry-standard drywall templates, which can be customized to match a contractor's existing estimating practices. Contractors moving from legacy estimating platforms such as PlanSwift or On-Screen Takeoff can also migrate their existing build libraries, allowing them to retain formulas and estimating rules developed over years of work. Completed calculations can be exported with formula columns and cost codes and organized by factors such as material and stud height, helping contractors carry takeoff information into estimating and procurement workflows. The launch marks the beginning of a broader strategy for Togal.AI: building increasingly specialized estimating capabilities for individual construction trades rather than treating every scope of work the same.