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PAILABS CivicQuant
CivicQuant is an AI-powered construction quantity estimation and structural planning platform developed by PAILABS to transform architectural drawings into actionable engineering and cost insights.
The platform enables contractors, estimators, engineers, and construction teams to upload a 2D architectural blueprint or floor plan and automatically extract structural information required for project estimation.
CivicQuant analyzes the uploaded drawing and helps generate structural components and quantities covering areas such as columns, foundations, beams, slabs, reinforcement, concrete, masonry, formwork, and other construction materials.
Users can review and configure engineering parameters—including structural dimensions, concrete grades, reinforcement specifications, slab and beam configurations, material choices, labour rates, wastage, and contractor mark-ups—to reflect actual project requirements.
The platform then calculates a consolidated structural project estimate, including material quantities, labour requirements, and projected construction costs. It can also provide an itemized Bill of Quantities (BOQ) that can be used for estimation, tendering, procurement planning, and contractor quotations.
By automating traditionally manual quantity take-off and estimation activities, CivicQuant aims to help construction companies reduce estimation effort, improve consistency, respond to tenders faster, and evaluate more project opportunities without proportionally increasing the estimation team.
From Blueprint to BOQ — Faster, Smarter Construction Estimation with AI.
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What if a 2D architectural drawing could be transformed into a detailed quantity takeoff and project cost estimate—with far less manual effort?
Manual takeoffs are often time-consuming, difficult to scale and prone to costly errors. At PAILABS, we are developing an AI-powered platform designed to bridge the gap between architectural drawings and accurate, dynamic construction estimates.
In this demonstration, PAILABS Structural analyses a 2D blueprint, extracts room dimensions, generates structural components, applies material specifications and produces:
✅ Detailed quantity takeoffs
✅ Interactive structural visualizations
✅ Material-specific calculations
✅ Local vendor-based cost estimates
✅ Comprehensive project reports
The goal is simple: help civil engineers, architects, estimators and construction companies improve accuracy, accelerate bidding and make better-informed project decisions.
We would love to connect with industry professionals and discuss potential use cases, pilot opportunities and practical challenges this platform could solve.
I'd flag any disagreement between a printed dimension and a measurement inferred from the drawing's scale before it reaches the takeoff. Does your workflow send those conflicts to an estimator for review?
midcode 2.0 is out. The biggest update so far.
the idea in the long run is to edit all frontend and backend visually + agentically
New interface
More integrations Vue, Astro, Angular, Shopify and 15 more stacks, edited on the canvas
Databases: your tables and SQL
Start projects from scratch, and a marketplace of starters
midcode.app
AI Rent & Arrears Follow-Up Automation: Property Management Dashboard
A working demo of an arrears management dashboard for property managers. It detects overdue tenants, ranks who to follow up first, and drafts safe, factual tenant messages from stored data.
Chasing late rent is slow and inconsistent. Managers often don't know whom to contact first, and messages are written from scratch each time. I built a demo system that turns rent and payment data into a clear daily workflow:
Rent data → arrears detection → priority → follow-up message → escalation → resolution
What it does:
Dashboard: total rent due, collected, outstanding, overdue tenants, high-risk accounts and follow-ups due today.
Arrears queue: every overdue tenant with property, amount, days overdue, priority and a suggested action.
AI Arrears Prioritization: automatically ranks who the manager should contact first, based on days overdue, amount owed and past payment behaviour.
Account analysis: shows the amount outstanding, days overdue, payment history, communication log and a recommended next action for any tenant.
Tenant message generation: drafts a polite reminder, follow-up or escalation message matched to the account's stage.
Manual stage control: the manager moves each account through Reminder, Follow-up, Escalation and Resolved.
Tech stack: Python, Flask, SQLite, HTML, CSS, JavaScript, with optional local AI through Ollama and a rule-based fallback. It needs no paid APIs and runs fully offline.
Demo data: 5 properties, 20 units and tenants, six months of payment history and prior follow-up records, so the dashboard looks like a live system.
Honest framing: I'd label it a "working demo" or "prototype" rather than a production system. It uses seeded sample data and has no real SMS or email sending or login yet. Those would be the natural next steps for a client.
