AI-Based Image Data Extractor and BOM Generator for a Chennai Automotive Supplier
AI-Based Image Data Extractor and BOM Generator for a Chennai Automotive Supplier
Palpx takes enterprise AI from prototype to production, principals who have done it at scale, not associates learning on your budget.
Palpx takes enterprise AI from prototype to production, principals who have done it at scale, not associates learning on your budget.
Nova·BOM · Automotive · Computer Vision + Generative AI
Nova·BOM · Automotive · Computer Vision + Generative AI
Nova·BOM · Automotive · Computer Vision + Generative AI
Nova·BOM · Automotive · Computer Vision + Generative AI
Client
Leading Oil & Gas Company
Industry
TAGS
Computer Vision · Generative AI · Automotive · Document AI
Technologies
Custom Visual Models · OCR · LangChain · Conversational AI · Python · PLM Integration · Nova Platform
Engagement
Lumen™️ → Forge™️
Client Overview
A Chennai-based manufacturer of automotive component parts supplying OEMs and tiered suppliers. The client managed complex component diagrams and large volumes of legacy engineering images — hand-drawn and digitally scanned — across their PLM workflow.
Results:
90%
extraction accuracy on production diagrams
70%
manual effort for BOM extraction
60–80%
time to generate validated BOMs
The Challenge
Complex, hand-drawn and legacy component diagrams required manual review to extract bill of materials (BOM) and part details. The process was slow, error-prone and created downstream delays in procurement and assembly planning. Engineers were spending days on BOM extraction that should take hours — and the error rate was creating rework loops that delayed production schedules.
The Solution
Palpx deployed Nova — a visual-AI image extraction pipeline — to convert engineering diagrams into structured data and enable natural-language queries, automated summaries and BOM generation. Three-phase engagement:
Phase 1 — audited diagram types, image quality and existing extraction workflows, curated labeled dataset, defined success metrics.
Phase 2 — tuned state-of-the-art visual models for component recognition and text/label extraction, built extraction pipeline converting detected elements into structured BOM entries, implemented validation rules and human-in-the-loop checks for edge cases. Phase 3 — integrated Nova output with the client's in-house PLM systems, enabled conversational AI queries so engineers could ask about parts, counts and dependencies in natural language.
Client
Leading Oil & Gas Company
Industry
TAGS
Computer Vision · Generative AI · Automotive · Document AI
Technologies
Custom Visual Models · OCR · LangChain · Conversational AI · Python · PLM Integration · Nova Platform
Engagement
Lumen™️ → Forge™️
Client Overview
A Chennai-based manufacturer of automotive component parts supplying OEMs and tiered suppliers. The client managed complex component diagrams and large volumes of legacy engineering images — hand-drawn and digitally scanned — across their PLM workflow.
Results:
90%
extraction accuracy on production diagrams
70%
manual effort for BOM extraction
60–80%
time to generate validated BOMs
The Challenges
Complex, hand-drawn and legacy component diagrams required manual review to extract bill of materials (BOM) and part details. The process was slow, error-prone and created downstream delays in procurement and assembly planning. Engineers were spending days on BOM extraction that should take hours — and the error rate was creating rework loops that delayed production schedules.
The Solution
Palpx deployed Nova — a visual-AI image extraction pipeline — to convert engineering diagrams into structured data and enable natural-language queries, automated summaries and BOM generation. Three-phase engagement:
Phase 1 — audited diagram types, image quality and existing extraction workflows, curated labeled dataset, defined success metrics.
Phase 2 — tuned state-of-the-art visual models for component recognition and text/label extraction, built extraction pipeline converting detected elements into structured BOM entries, implemented validation rules and human-in-the-loop checks for edge cases. Phase 3 — integrated Nova output with the client's in-house PLM systems, enabled conversational AI queries so engineers could ask about parts, counts and dependencies in natural language.
Nova·BOM · Automotive · Computer Vision + Generative AI
Client
Leading Oil & Gas Company
Industry
TAGS
Computer Vision · Generative AI · Automotive · Document AI
Technologies
Custom Visual Models · OCR · LangChain · Conversational AI · Python · PLM Integration · Nova Platform
Engagement
Lumen™️ → Forge™️
Client Overview
A Chennai-based manufacturer of automotive component parts supplying OEMs and tiered suppliers. The client managed complex component diagrams and large volumes of legacy engineering images — hand-drawn and digitally scanned — across their PLM workflow.
Results:
90%
extraction accuracy on production diagrams
70%
manual effort for BOM extraction
60–80%
time to generate validated BOMs
The Challenges
Complex, hand-drawn and legacy component diagrams required manual review to extract bill of materials (BOM) and part details. The process was slow, error-prone and created downstream delays in procurement and assembly planning. Engineers were spending days on BOM extraction that should take hours — and the error rate was creating rework loops that delayed production schedules.
The Solution
Palpx deployed Nova — a visual-AI image extraction pipeline — to convert engineering diagrams into structured data and enable natural-language queries, automated summaries and BOM generation. Three-phase engagement:
Phase 1 — audited diagram types, image quality and existing extraction workflows, curated labeled dataset, defined success metrics.
Phase 2 — tuned state-of-the-art visual models for component recognition and text/label extraction, built extraction pipeline converting detected elements into structured BOM entries, implemented validation rules and human-in-the-loop checks for edge cases. Phase 3 — integrated Nova output with the client's in-house PLM systems, enabled conversational AI queries so engineers could ask about parts, counts and dependencies in natural language.