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We asked ChatGPT about 20 Indian logistics companies. 14 were invisible.

Procurement managers, supply chain heads, and D2C founders are increasingly using AI to shortlist logistics partners before issuing RFQs. "Which 3PL is best for ecommerce fulfilment in Tier 2 cities?" "Which courier has the best first-attempt delivery rates in India?" Our study of 20 Indian logistics companies found that 14 were completely absent from these conversations — handing an enormous competitive advantage to the 6 that are visible.

How we tested

We selected 20 companies spanning express courier, ecommerce logistics, B2B freight, and contract logistics — including Blue Dart, Delhivery, Ecom Express, DTDC, XpressBees, Ekart Logistics, Gati, Safexpress, TCI Express, Mahindra Logistics, TVS Supply Chain Solutions, Rivigo, Shadowfax, Borzo, Trackon Couriers, Loadshare Networks, BlackBuck, Porter, ElasticRun, and WareIQ.

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

• Procurement prompts — "best courier partner for D2C brand in India", "which 3PL handles Tier 2 and Tier 3 city delivery well?", "top logistics companies for B2B freight in India"
• Direct prompts — "what services does [company] offer?", "is [company] good for last-mile delivery?"
• Comparison prompts — "[company A] vs [company B] for ecommerce fulfilment — which is better for returns management?"

What we found

• 14 of 20 companies never appeared in any procurement query on any engine — despite many operating networks that cover 95%+ of India's pin codes.
• Of the 6 that did appear, 4 had material errors — coverage areas described inaccurately, merged or acquired companies described as independent, pricing models outdated by multiple rate revisions, and service capabilities conflated between competitors.
• Only 2 of 20 were consistently recommended and accurately described — a 10% pass rate in a category where a wrong recommendation can cost a shipper crores annually in SLA failures.
• AI answers for logistics queries defaulted heavily to brands with strong Wikipedia presence and extensive third-party coverage — operational scale had almost no correlation with AI visibility.

What the invisible 14 had in common

1. B2B companies built their digital presence for lead generation, not discovery. Most logistics companies' websites are essentially contact forms and rate calculators — designed to convert someone who already knows them, not to be found by someone who doesn't. AI engines can't recommend a company that has no content explaining what it does and who it serves.
2. Service area and capability data exists only in sales decks. Coverage data, SLA benchmarks, and vertical specializations — the information a procurement head needs — lives in password-protected portals and PDFs sent to prospects. It's not crawlable, not citable, and invisible to AI.
3. No owned thought leadership or original market data. A logistics company that publishes quarterly data on Indian ecommerce return rates, or sector-specific reports on Tier 2 demand, creates something AI engines can cite and attribute. Almost none of the 20 companies in our study did this.
4. Industry association presence isn't AI-visible. Several brands had strong CII/FICCI profiles and trade publication mentions — but in formats that AI retrieval systems don't prioritize over structured web content.

What to do about it

Logistics brands need to build public-facing content that explains their capability, coverage, and differentiators — not for SEO, but for AI citation. Original market reports, structured capability pages, and earned coverage in logistics and ecommerce media are the starting points. Full playbook in our Complete GEO Guide.