Product Overview
What It Is
Jobotics is an AI-powered answer and action engine built specifically for trade and specialty contractors. It aggregates data from a contractor's fragmented technology stack, covering project management platforms, ERP and accounting systems, safety tools and CRM, into a single intelligence layer. Users interrogate that data in plain English and trigger automated workflows, from generating risk dashboards and Excel reports to reviewing contracts and producing labor hour estimates.
The Problem It Solves
Current Approach
Trade and specialty contractors, primarily mechanical, electrical and plumbing firms, manage their businesses across a patchwork of disconnected systems: Procore or Autodesk Build for project management, Vista or a similar ERP for job costing, dedicated safety platforms, and CRM tools. Extracting meaningful insight across them is manual and time-consuming. Project managers jump between tools to compile reports, spending hours on work that should take minutes. Executives and senior leadership lack a real-time consolidated view of project health, so risks surface too late. Field workers cannot get quick answers about material deliveries, installation specs or schedules without escalating. Finance teams struggle to reconcile job costs across multiple platforms with no single source of truth. Contract review gets delegated to external counsel because of volume and complexity, making it slow and expensive. Material take-offs and labor hour estimations are done manually, page by page, copied and pasted out of PDFs, taking hours to days. And job closeout, packaging all project documentation for handoff, is a laborious multi-hour process prone to error.
How It Changes
Jobotics sits as an AI orchestration layer above a contractor's existing tools rather than replacing them. By connecting to current systems through API or MCP integrations, it pulls data into a unified memory layer and exposes it through a conversational interface, automated workflows, and AI-generated outputs including dashboards, spreadsheets, slide decks and contract redlines.
Value Proposition
Speed, consolidation and proactive risk detection.
Area of Focus
Trade and specialty contractors with a mechanical, electrical and plumbing focus, operating in high-growth construction verticals such as data centers, EV and battery manufacturing plants, and automotive factories. This is a subcontractor platform rather than a general contractor one. The minimum viable size is around $20M to $50M in annual revenue, with the current sweet spot at $350M to $700M or more, and 100 to 300 or more employees operating across multiple states or divisions.
Key Features
AI-Generated Risk Dashboards and Reports
Pain: Leadership has no consolidated view of project health. Data is fragmented across tools and risks surface too late.
A single-screen project dashboard generated on the fly, aggregating data from all connected systems. It displays an overall risk score, cost performance index, schedule performance and safety incident tracking, with correlation analysis across dimensions such as safety events mapped against cost performance trends. No human-built templates are used: the AI orchestrates the data and determines the layout. Output is a dashboard plus spreadsheets and slides, all generated in minutes from a single natural language prompt.
AI Contract Review
Pain: Contractors pass contracts to external counsel, which is slow, up to a week of turnaround, and expensive.
Users upload a contract PDF and receive a redlined document within minutes, with risk items flagged high, medium or low and mapped to specific clauses and paragraphs. The AI is fine-tuned on the client's own historical contracts, so it learns firm-specific preferences such as payment terms and turnaround clauses rather than applying generic legal logic. That reduces legal spend to reviewing the extracted key terms only.
Material Take-Off
Pain: Manual take-offs require page-by-page review and error-prone copying out of PDFs, taking hours or days.
Users upload 2D drawings, specifications or bid documents as PDF, DWG or a BIM model. The AI extracts all materials and generates a structured bill of materials in a spreadsheet, with quantities, descriptions, source references and a summary tab. The system cites where each match was found in the MCAA database, providing an audit trail for validation, and human-in-the-loop validation is built in.
Labor Hour Estimation
Pain: Labor estimation for mechanical, electrical and plumbing trades is highly specialized and typically done manually by experienced estimators.
Given a material list or specification, the AI generates a labor hour estimate by referencing MCAA and other trade labor databases. Output is a multi-tab spreadsheet showing estimated hours per section, matched against the relevant database entry for each line item, which accelerates the first pass of the pre-bid workflow.
Used By
Target Customer Types
Sylvan
$700M revenue, 200 employees
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Andy J. Egan Co.
$240M revenue, 200 employees
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Operating Locations
US-focused, with at least one client operating in Canada and Mexico.
Implementation & Onboarding
Jobotics uses a forward deployed engineer model. Step one, consultation: assess the client's current tools, data and workflows. Step two, an on-site assessment over three days: embed with the client team on site, map use cases, data sources and integration requirements, and begin integration in place. Step three, product build and deployment: complete integration and customization, typically within a few additional weeks. Step four, ongoing iteration. The claimed implementation timeline is weeks rather than months, against the 9 to 12 months typical of a full Procore or Autodesk implementation.
Security & Privacy
Integrations
ROI & Value
Time saving
Measured before and after per workflow. Job closeout reduced from around four hours to three minutes, a contract first draft from days to minutes, and material take-off from hours or days to minutes.
Cost saving
Reduced legal spend on contract review, and reduced administrative overhead.
Revenue recovery
One client recovered $40M in accounts receivable after the platform surfaced $60M in overdue balances fragmented across projects that leadership had no visibility of.
Founding Team
Robert Dong
Founder and CEO
A serial AI entrepreneur and product leader. Robert has spent over a decade building AI-driven products at major technology companies including Meta, TikTok and Cruise, working at the intersection of AI infrastructure, product development and large-scale platforms.
LinkedIn
Skye Zhou
Co-Founder
An AI founder with experience across three AI startups and deep expertise in construction technology. An MBA alumnus of UC Berkeley and EDHEC, and a founder alumnus of Berkeley SkyDeck's Pad-13 incubator.
LinkedInFunding
Total Raised
Undisclosed
Investors