BOQ Intelligence Engine
Reads BOQs, classifies construction items, groups procurement packages, detects procurement opportunities, and prepares RFQ structures.
Construction AI Framework
Specialized AI Engines Built for Construction Decisions.
Quotah embeds AI across the workflow from project upload to procurement decision support. The platform augments engineers and procurement teams by organizing construction information, accelerating analysis, reducing operational risk, and keeping final commercial decisions under professional control.
The Problem
Generic AI Is Not Enough for Construction Procurement.
Construction procurement involves materials, specifications, regional markets, supplier capabilities, subcontractor capacity, project characteristics, and commercial risk. Quotah uses specialized AI domains rather than a single generic model so each workflow receives construction-specific intelligence.
BOQsDocumentsExcelManual ReviewIndividual ExperienceSlow and fragmented understanding
What Quotah Studies
Specialized Engines Working as One Intelligence Framework.
Each intelligence engine performs a specific operational function while sharing information with the wider Quotah intelligence network.
Cost Intelligence Engine
Analyzes project documents, interprets BOQs, estimates costs, compares similar historical items, and uses market pricing intelligence for defensible estimates.
RFQ Generation Engine
Converts studied project requirements into structured RFQ logic with scope, quantities, technical context, deadlines, and response requirements.
Matching Intelligence Engine
Matches suppliers and subcontractors using products, capabilities, coverage, readiness, technical fit, pricing behavior, and response history.
Quotation Intelligence Engine
Compares supplier quotations and subcontractor proposals against project context, technical requirements, commercial gaps, and procurement risk.
Procurement Intelligence Engine
Supports approval readiness, purchase order direction, decision notes, history, and reusable organizational knowledge.
AI Architecture
Specialized intelligence domains, not one generic model.
The blueprint defines the AI framework as modular construction intelligence. Each engine is optimized for a specific construction decision, and each domain can improve independently as more verified project, quotation, procurement, and market data enters the network.
Decision Intelligence
Combines project information, supplier performance, subcontractor capability, quotation behavior, risk signals, and approval context to support clearer decisions.
Market Intelligence
Uses comparable items, regional pricing, recent quotations, procurement history, project type, specification logic, and quantity relationships to improve cost context.
Organizational Learning
Preserves validated project study logic, procurement decisions, supplier history, subcontractor history, and decision notes as reusable institutional knowledge.
Future Expansion
The architecture can extend into predictive analytics, workflow intelligence, enterprise automation, supplier performance analytics, integrations, and executive dashboards.
How the AI Framework Works
From Project Files to Compounding Intelligence.
Every project, RFQ, quotation, proposal, and purchase order strengthens the intelligence network for future recommendations.
Stage 01
Create a controlled project workspace.
The study starts with project context, team readiness, and the commercial frame that will carry into every later output.
BOQ Intelligence Engine
Understand What the Project Actually Contains.
Quotah moves beyond reading spreadsheet rows. It creates BOQ and BOM-level understanding around items, quantities, scope relationships, classifications, procurement packages, and project structure.
ItemsQuantitiesScopesClassificationsRelationshipsPackages
Cost Intelligence Engine
See the Cost Structure Behind the Tender Total.
The platform creates visibility into cost components and major cost drivers so teams can understand the commercial logic behind the project.
RFQ Generation Engine
Connect Cost to Market Reality and Execution Logic.
Project cost cannot be understood through static libraries alone. Quotah considers similar items, specifications, regional market conditions, recent quotations, quantity relationships, productivity assumptions, and execution logic.
Matching Intelligence Engine
Know What the Project Requires to Be Delivered and Procured.
Project analysis translates scope into materials, labor, equipment, procurement requirements, supplier/subcontractor categories, subcontractor needs, and sourcing priorities. This is the bridge from Project Study to Procurement.
Quotation Intelligence Engine
Surface Risk Before It Becomes a Commercial Problem.
Quotah highlights supported risks such as scope gaps, drawing conflicts, unusual cost drivers, missing information, execution assumptions, supplier dependency, subcontractor dependency, and procurement exposure. AI flags remain subject to professional validation.
Procurement Intelligence Engine
Turn Detailed Analysis into Decision-Ready Project Understanding.
Senior management can review project structure, major cost drivers, critical resources, key assumptions, major risks, and procurement priorities without reading thousands of BOQ lines.
Real Project Study Output
From Thousands of Project Inputs to One Connected Intelligence View.
A product-proof view of the study output, organized by the tabs a team expects to review.
Human-Centered AI Governance
AI Supports the Work. Professionals Own the Decisions.
Quotah is designed to augment engineers and procurement professionals, not replace them. Teams review, validate, adjust, and approve assumptions and outputs before acting on them.
AI Supports
Structuring, classification, estimation support, RFQ preparation, matching, risk flags, quotation comparison, and connected report generation.
Professionals Control
Validation, adjustment, approval, commercial judgment, technical judgment, supplier selection, subcontractor selection, and final decisions.
Key Differentiator
The Intelligence Does Not End with a Report. It Drives Procurement.
The intelligence created during Project Study continues into procurement, reducing the disconnect between understanding what the project needs, selecting the right participants, comparing responses, and buying what the project requires.
Project IntelligenceResource RequirementsProcurement PackagesSupplier & Subcontractor MatchingRFQsSupplier Quotations & Subcontractor ProposalsComparisonDecision
See How Intelligence Drives ProcurementWho Uses It
Who Uses the Project Study
Different teams use the same intelligence output for different decisions, without separating tendering, commercial, procurement, supplier, and subcontractor context.
Tendering Teams
Understand scope and commercial structure.
Estimators & Quantity Surveyors
Review BOQ and cost logic.
Procurement Teams
Understand requirements and sourcing priorities.
Commercial Managers
Review cost drivers and risks.
Management
Use executive intelligence for decisions.
Why It Is Different
More Than BOQ Analysis. More Than Cost Estimation.
Quotah does not simply calculate a project. It builds a structured understanding of the project before commercial and procurement decisions are made.
Multi-Layer Project Understanding
Cost, productivity, resources, market pricing, supplier readiness, subcontractor capability, and risk are studied together.
BOQ-Level Intelligence
Analysis begins from actual project scope and quantities.
Study-to-Procurement Continuity
Intelligence continues into RFQs, matching, quotation comparison, approval readiness, and procurement instead of ending in a report.
AI with Professional Control
Users validate the logic and remain responsible for decisions.
Sample Project Study
A sample project journey based on validated project logic.
Input documents move into project size and complexity review, study process, intelligence produced, risks identified, procurement requirements generated, and decisions supported. No invented savings, speed improvements, accuracy percentages, or ROI claims are used.