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We asked ChatGPT about 25 Indian B2B manufacturers. 18 were invisible.

Manufacturing has the worst AI visibility pass rate of any sector we've tested. Plant engineers and sourcing managers are already using AI to identify suppliers, benchmark capabilities, and shortlist vendors before a single RFQ goes out. Our study of 25 Indian industrial manufacturers found that 18 were completely invisible to these queries — and only 2 were both visible and accurately described. That is an 8% pass rate.

How we tested

We selected 25 companies spanning engineering, cables, electricals, auto-components, and industrial goods — including Thermax, Greaves Cotton, Kirloskar Brothers, Ceat, Polycab, Havells, Finolex Cables, APL Apollo Tubes, Ratnamani Metals, Bajaj Electricals, Dixon Technologies, V-Guard Industries, Amara Raja Batteries, Exide Industries, Sona BLW Precision, Bharat Forge, Endurance Technologies, Schaeffler India, a listed auto-components manufacturer, Kaynes Technology, Sansera Engineering, Craftsman Automation, KDDL, Tube Investments of India, and Indo Count Industries.

We ran three prompt types across ChatGPT (GPT-4o), Gemini Advanced, and Perplexity:

• Sourcing prompts — "top Indian manufacturers of industrial cables", "best Indian auto-component suppliers for Tier 1 OEM", "which Indian companies make precision engineered components for export?"
• Direct prompts — "what does [company] manufacture?", "is [company] a listed company / export-compliant supplier?"
• Capability prompts — "which Indian manufacturer has the capacity and certifications for [product] in [volume range]?"

What we found

• 18 of 25 companies never appeared in any sourcing query on any engine — despite many being listed companies, export-certified, and with multi-crore order books.
• Of the 7 that did appear, 5 had material errors — product categories confused with competitors, outdated capacity data, certifications described as pending when they've been held for years, and global subsidiaries described as the Indian entity.
• Only 2 of 25 were both visible and accurately described — an 8% pass rate, the lowest of any sector in our study series.
• AI answers for manufacturing sourcing queries systematically over-represented companies with strong English-language Wikipedia presence and active investor relations content — neither of which correlates with actual manufacturing capability.

What the invisible 18 had in common

1. B2B companies built digital presence for one audience: stock analysts. The companies with the most detailed digital footprints — annual reports, quarterly presentations, investor days — aimed everything at capital markets. That content doesn't answer the questions a sourcing manager asks, so AI can't use it for recommendation queries.
2. Product catalogues in PDF, not in crawlable HTML. Most manufacturers' product specification data exists as downloadable PDFs — organized for engineers but invisible to AI retrieval systems. A 200-page capability document that can't be crawled contributes zero to AI visibility.
3. No specification content written for discovery. A sourcing manager using AI asks a different question than one browsing a catalogue. AI visibility requires content that answers natural-language procurement questions — and manufacturing companies, almost universally, haven't written that content.
4. Certifications and compliance data buried in footers. ISO, IATF, AS9100, and export compliance certifications are major differentiators for industrial buyers — yet they're often listed as a string of acronyms in a footer, with no context an AI engine can interpret and cite.

What to do about it

Manufacturing brands need to build content for the buyer's natural-language question, not for the annual report reader. That means product capability pages in crawlable HTML, specification comparisons written as answers, certification pages with context, and case studies with named customer categories. We've built this for India's manufacturers — full system in our Complete GEO Guide.