ChatGPT ranks InstaLILY #1 nine times.
Gemini and Claude did not surface it once.
InstaLILY coined the category — “the world’s first AI Forward Deployed Engineer.” On ChatGPT it owns that category: mentioned on 17 of 20 buyer queries (85%), 9 of them at #1. Ask Gemini or Claude the exact same questions and InstaLILY is mentioned zero times — the forward-deployed slot goes to Palantir, field service to Aquant. Across all three platforms: 17 of 60 responses (28%).
The findings below come from Xtrusio, an AI visibility audit system built specifically for B2B buyer-intent testing. Every mention was verified by running 20 real prospect queries across ChatGPT, Claude, and Gemini.
Analysis reflects how AI platforms surface enterprise-AI and industrial-distribution vendors during discovery-phase research.
Every one of InstaLILY’s 17 mentions lives on a single platform.
On ChatGPT, InstaLILY dominates its own positioning — InstaQuote for RFQ-to-quote, InstaProspect for lead gen, Lily for building software inside SAP and NetSuite, the Small Data Center for on-prem. But that visibility is 100% concentrated: on Gemini and Claude, all 20 questions — several of them near-verbatim paraphrases of InstaLILY’s own site copy — return zero mentions. Most striking, the “AI Forward Deployed Engineer” query, a term InstaLILY coined, resolves to Palantir and OpenAI on both of the platforms where InstaLILY is absent.
Platform Scorecard
InstaLILY mention rate across AI platforms
AI Visibility Leaderboard
Who owns the AI conversation — total mentions across all platforms
AI Positioning Audit
20 buyer-intent queries — click any row to see the exact question
Each query was written from the perspective of a real decision-maker researching operational AI — a distributor automating quote-to-order, a rental operator running field service and dispatch, and an enterprise leader deploying AI into production. These are the buyers whose AI search results decide whether InstaLILY gets discovered.
Buyer Personas
3 verified LinkedIn decision-makers behind these 20 queries| # | Query Topic | Cluster | Claude | ChatGPT | Gemini |
|---|---|---|---|---|---|
| 1 | RFQ email / PDF → quote | Shared | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “Our distribution reps spend hours turning inbound RFQ emails and PDF attachments into quotes in our ERP. What AI tools can read those requests and draft accurate quotes automatically?” | |||||
| 2 | Competitor part# / SKU match | USP | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “When customers send purchase orders using competitor part numbers or vague descriptions like ‘same as last order,’ is there AI that can match them to the right SKU in our catalog and price the quote correctly?” | |||||
| 3 | Multi-channel order entry | Shared | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “What’s the best way to automate order entry for an industrial distributor so POs from email, fax, and web forms get into our ERP without manual typing?” | |||||
| 4 | CRM + PIM + quote platform | Competitor | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “We’re an industrial distributor looking for an AI platform that combines CRM, product data, and quote automation on one system. What options should we evaluate?” | |||||
| 5 | Cross-sell / win-back scan | USP | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “Is there AI that can scan a distributor’s entire customer base to surface untapped cross-sell and win-back revenue, and hand reps a prioritized list of actions each day?” | |||||
| 6 | Churn-risk / stalled quotes | Shared | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “What AI sales tools help B2B distribution reps identify churn-risk accounts and stalled quotes before they lose the business?” | |||||
| 7 | Lead gen + scoring in CRM | Shared | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “How can a wholesale distributor use AI to generate and score leads across its market and work them inside an existing CRM like Salesforce?” | |||||
| 8 | Codify rep playbook | USP | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “We want to codify our best reps’ playbook so every salesperson follows it. Can AI capture that expert selling logic and run it across hundreds of reps?” | |||||
| 9 | Field diagnosis + part ID | Shared | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “What AI can help field service technicians diagnose equipment faults and identify the right replacement part while they’re on-site?” | |||||
| 10 | Technician onboarding / ramp | USP | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “Our technician onboarding takes months. Is there AI that codifies expert repair knowledge so new field reps ramp faster?” | |||||
| 11 | Field-service troubleshooting leader | Competitor | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “What’s the leading AI for field service troubleshooting and service intelligence in industrial equipment?” | |||||
| 12 | Cut cost-to-serve | Shared | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “How can a company cut the cost to serve a field-service call using AI-guided diagnosis and remote support?” | |||||
| 13 | Dispatch / delivery exceptions | Shared | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “What AI tools help logistics teams automate dispatch, handle delivery exceptions, and send proactive customer updates?” | |||||
| 14 | Fleet routing in seconds | USP | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “Is there AI that can cut routing and planning time for a distribution fleet from minutes per order down to seconds, inside our existing systems?” | |||||
| 15 | Supply-chain agents across systems | Shared | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “How are supply chain operations using AI agents to manage fragmented workflows across ERP, email, and carrier systems?” | |||||
| 16 | Builds software in SAP/NetSuite | USP | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “We don’t want another copilot or dashboard — we want AI that actually builds the custom software our operations need and runs it inside SAP and NetSuite. Does that exist?” | |||||
| 17 | Stays deployed / anti-drift | USP | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “Consultants build us custom systems then leave, and the software drifts out of date. Is there AI that stays deployed and keeps the software aligned as our business changes?” | |||||
| 18 | On-premise / edge AI | USP | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “We have sensitive operational data and need AI that can run on-premise or at the edge, not only in the public cloud. What are our options?” | |||||
| 19 | Enterprise FDE + governance | Competitor | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “What platforms let a large enterprise deploy AI into complex operational workflows with forward-deployed engineers and strong governance?” | |||||
| 20 | Live in weeks not quarters | Shared | ✗ | ✓ | ✗ |
Exact question asked across all AI platforms: “For an operationally complex enterprise, what’s the fastest way to get AI live in production workflows in weeks rather than a multi-quarter IT project?” | |||||
| TOTAL | 0/20 (0%) | 17/20 (85%) | 0/20 (0%) | ||
The Gemini & Claude Blackout
Two platforms, forty questions, zero mentions of InstaLILY
ChatGPT names InstaLILY on 17 of 20 queries. Feed the identical questions to Gemini and Claude and the count drops to 0 of 20 on each. This is not a ranking problem — it is total absence. On the questions below, both platforms return confident, detailed answers; InstaLILY simply is not in them.
“What platforms let a large enterprise deploy AI into complex operational workflows with forward-deployed engineers and strong governance?”
Query 19 • ChatGPT mentions InstaLILY • Gemini & Claude answer Palantir Foundry, C3 AI, and OpenAI’s forward-deployed team — the exact category InstaLILY branded itself around.
“We want AI that actually builds the custom software our operations need and runs it inside SAP and NetSuite. Does that exist?”
Query 16 • ChatGPT ranks Lily the closest match • Gemini points to SAP Build and NetSuite SuiteCloud; Claude routes to Palantir — InstaLILY’s core ‘builds software’ wedge, unseen.
“What AI tools can read inbound RFQ emails and PDFs and draft accurate quotes in our ERP automatically?”
Query 1 • ChatGPT recommends InstaQuote • Gemini answers Kavida.ai and WizCommerce; Claude answers Proton.ai and Conexiom — InstaLILY’s flagship quoting product, absent on both.
A term a company invents only pays off if AI platforms connect that term back to the company. Right now, on two of three tested platforms, they don’t. Buyers who ask Gemini or Claude the forward-deployed-engineer question — InstaLILY’s own framing — are handed Palantir, C3 AI, and OpenAI. The category is working; it is just working for someone else.
AI Topic Authority Map
Which product lines AI knows — and where the map goes dark
| Topic Territory | Who AI Credits | InstaLILY Coverage |
|---|---|---|
| Quote-to-Order (RFQ, SKU match, order entry) | InstaLILY on ChatGPT; Proton.ai, Conexiom, WizCommerce elsewhere | ChatGPT only |
| Sales Enablement (cross-sell, lead gen, playbook) | InstaLILY on ChatGPT; Salesforce, ZoomInfo, Gong elsewhere | ChatGPT only |
| Field Service (diagnosis, onboarding, cost-to-serve) | Aquant across all platforms; Augmentir, ServiceMax | Partial — ChatGPT Q9–10 only |
| Logistics & Routing (dispatch, fleet, supply-chain agents) | DispatchTrack, Bringg, Locus, Project44 | Partial — ChatGPT routing only |
| Forward-Deployed Engineering (build, anti-drift, on-prem, governance) | InstaLILY on ChatGPT; Palantir, C3 AI, OpenAI elsewhere | ChatGPT only |
Product Line × Platform Heatmap
Percentage of queries in each product line where InstaLILY was mentioned, per platform.
Methodology
How this audit was conducted
Audit Parameters
Recorded for reproducibility. Figures reflect the conditions under which these specific responses were generated — AI answers can shift with model, session, browsing state, and location.
| Test window | August 2026 |
| Platforms & modes | ChatGPT — 3 independent sessions (including a web-search–augmented run); Gemini — Custom Gem + standard conversational; Claude — reference-guide + standard conversational |
| Session type | Logged-in consumer sessions; web browsing / search enabled where the platform supports it |
| Model versions | Latest generally-available consumer models at time of testing. Exact build identifiers are not exposed in the consumer interfaces, so versions are recorded as “current release.” |
| Location | Runs originated from an India-based session — reflected in Gemini surfacing local (Lucknow) vendors on the fleet-routing query. Geography can influence results and is disclosed here for that reason. |
| Repetition | Seven testing sessions were run in total: ChatGPT ×3, Gemini ×2, and Claude ×2. One 20-query session per platform was selected as the locked scored baseline (60 scored responses). The remaining four sessions were used only to check run-to-run volatility and were excluded from the headline denominator. |
| Mention & ranking rule | A “mention” = InstaLILY named as a recommended vendor or answer in the response. “Rank” = its ordinal position within that response’s list. Composite = responses containing an InstaLILY mention ÷ 60 total. ChatGPT is locked on its most recent full session. |
Recommendations
A staged plan to close the two-platform blackout
- Audit and strengthen the existing product pages (InstaQuote, InstaProspect, Lily, Small Data Center) around the exact buyer-language gaps this audit surfaced — sharpening category associations, proof, comparison language, internal linking, and third-party corroboration rather than creating duplicate pages.
- Add Organization and Product structured data to strengthen machine-readable entity and product clarity.
- Prioritize earned mentions on the third-party sources AI answers frequently draw from — industry press, distribution/MRO trade sites, and analyst roundups.
- Build category-defining content that ties “AI Forward Deployed Engineer” unmistakably to InstaLILY, with named customer outcomes in distribution and rental.
- Ship comparison pages against Palantir, C3 AI, and copilots that draw the ‘builds & stays deployed’ distinction in explicit, quotable language.
- The Small Data Center page already covers cloud, on-prem, and edge, and Lily already positions on staying deployed — reinforce these existing pages with proof and third-party validation so Queries 17–18 attach to InstaLILY where incumbents currently win.
- Existing field-service and logistics coverage isn’t translating into consistent recommendation visibility on the three universal blind spots — field-service leadership (Q11), cost-to-serve (Q12), and dispatch (Q13). Strengthen those pages with buyer-specific outcomes, customer evidence, and third-party validation.
- Publish outcome-led case studies in field service and logistics to establish authority in the lines currently owned by point solutions.
- Re-run this Xtrusio audit quarterly to track Gemini and Claude recovery from 0% and confirm ChatGPT holds at 85%+.
This audit is a snapshot. AI answers shift as models and retrieval layers update — and InstaLILY’s single-platform concentration makes that volatility a real risk. Ongoing Xtrusio tracking monitors mention rates across ChatGPT, Claude, and Gemini so you see recovery on the two blind platforms — and any slippage on the one that carries you — the moment it happens.
Let’s put InstaLILY on all three platforms.
85% on ChatGPT proves the positioning works. Now make Gemini and Claude say it too.
