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EPC
Engineering
AI/ML

Case Study: Engineering Insights

Redefining Engineering Efficiency with AI for Engineering, Procurement, and Construction (EPC) Firms.

The Problem
In high-stakes EPC projects, failures stem from deep structural flaws in engineering workflows.
  • Manual BoQ calculations leading to errors.
  • Siloed CAD/PDF drawings causing visibility issues.
  • Lack of design validation tools increasing risk.
  • Tribal knowledge silos resulting in insight loss.
  • Poor planning-data sync causing disruptions.

These flaws lead to costly rework, safety risks, and reduced stakeholder trust.

The Solution
An end-to-end engineering intelligence platform.
  • Drawing Intelligence: Turns unstructured PDFs into structured, machine-readable geometry and specs.
  • Smart BoQ Generation: Automates BoQ with traceability and instant error-flagging.
  • Compliance Validator: Checks drawing data against project and regulatory specs.
  • Knowledge Hub: Transforms one-off insights into scalable institutional knowledge.
  • Review Collaboration: Enables teams to work in sync on validated inputs.
Operational Flow & Efficiency Uplift
A workflow that previously took 3–4 days across departments now concludes in under 60 minutes.
  1. 1

    Input Phase

    Upload structural, architectural, or civil drawings in PDF or scanned format.

  2. 2

    Interpretation Engine

    AI models extract key data such as dimensions, reinforcement types, zones, and materials.

  3. 3

    Validation Pipeline

    Automated checks flag inconsistencies in codes, material specs, or missing elements.

  4. 4

    BoQ Generator

    Structured BoQ output created, auditable and ready for procurement.

  5. 5

    Feedback Layer

    Engineers review, annotate, and approve outputs—creating a continuous learning loop.

  6. 6

    Export Options

    Download in Excel/PDF or push directly into ERP, BIM, or tender systems.

AI Stack & Technical Innovation
  • Vision AI

    Vertex AI OCR with engineering-specific training to parse legacy PDFs and drawings.

  • LLM Parsing Agents

    Custom-tuned language models that understand drawing annotations, layer logic, and specifications.

  • Retrieval-Augmented Generation (RAG)

    Dense + sparse embedding architecture for BoQ knowledge queries.

  • Validator Engine

    Combines learned rules and deterministic logic to enforce project-specific codes and constraints.

  • Self-Learning Feedback Loop

    Incorporates engineer inputs to refine prediction and flagging accuracy over time.

Business Impact
Efficiency, Compliance & Confidence
KPIBeforeAfter
Time to Generate BoQ3–4 days<1 hour
Design Review Time2+ hours/drawing15–30 minutes
Procurement Planning ErrorsFrequentReduced by 85%
Compliance RiskHighAuto-flagged & transparent
Engineering Knowledge LossHigh (tribal)Captured & reusable
70% faster design-to-procurement
90% reduction in missed flags
Improved tender confidence
Deployment Status & Market Momentum
  • Pilot Success: Completed with Tier-1 EPC firm on metro rail infrastructure.
  • Phase II Scaling: Active deployments in road, bridge, and urban utility projects.
  • Regional Footprint: Live in Bangladesh, India, with rollouts in Middle East and Southeast Asia.
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