Nfluence is evaluating Adeptmind as a potential sell-side mandate. The central question every buyer will ask: if Adeptmind builds on top of OpenAI and Google, what stops those providers from going direct to retailers and eliminating the platform entirely? Crossover's Q of AI Assessment answers that question with independent primary evidence before your process begins.
Toronto-based B2B SaaS company applying LLM-powered search, merchandising, and product discovery to mid-market and enterprise retail - bridging e-commerce and physical shopping center operators. The platform's AI layer runs on third-party foundation models, creating a specific and predictable objection pattern in any M&A process.
Open with AI classification badge and dual scorecard. Frame as independently validated by third-party customer research. Lead with the strongest resilience metric: Data Lock-In score.
Feature customer-validated metrics - daily adoption rate and specific ROI figures from retail customers. Not generic "AI-powered platform" claims. Buyers see through the latter immediately.
Cite the competitive differentiation score. Include customer quotes on why direct LLM APIs failed as substitutes. Use verbatim customer language - it carries more weight than any management slide.
"Won't ChatGPT make this obsolete?" Counter with Data Lock-In score and customer quotes about failed replacement tests. Cite specific switching timelines in customer language.
"Keeping pace with AI-native startups?" Acknowledge innovation velocity score transparently. Pivot to roadmap credibility and structural moats startups cannot replicate without proprietary retail data.
"Why pay a premium?" AI Fortress positioning. Only 15–20% of software companies achieve High Capability + High Resilience. Customer-validated, not self-reported, and citable in IC memos.
This is the central question for the sell-side process. Adeptmind is powered by OpenAI, Google, and other foundation model providers - the same providers building ChatGPT Shopping, Google AI Overviews, and native commerce discovery features. The risk is that these providers go direct to retailers, disintermediating Adeptmind's orchestration layer.
The assessment interviews retailers currently using Adeptmind and asks directly: have they evaluated native LLM APIs as a substitute? What did they find? What would they gain or lose by switching? What is the time-and-cost barrier to replacing Adeptmind with a direct OpenAI or Vertex API integration?
Switching cost is the primary valuation lever for any B2B SaaS sell-side process. If Adeptmind is integrated at the catalog level, the PIM, the merchandising workflow, and has fine-tuned behavioral models on proprietary product data, switching is an 18-month project and buyers will price that stickiness into the multiple.
The assessment maps integration architecture from the customer's perspective: which systems does Adeptmind touch, how much customization has been applied, what would a transition project actually require in time and headcount, and whether the customer has ever evaluated alternatives and decided against switching.
Adeptmind's customer list includes Cadillac Fairview, Bayer Properties, CBL Properties, Hammerson, and Mall of America - all shopping center operators, not pure-play e-commerce. This segment is structurally underserved by traditional AI search vendors and harder for foundation model providers to address without proprietary physical retail data.
The assessment distinguishes between Adeptmind's pure-play e-commerce customers and its shopping center operator customers - scoring satisfaction, integration depth, and substitutability separately by segment. This surfaces whether the phygital angle is a genuine differentiation driver with pricing power, or a secondary market that complicates the story.
Ulta Beauty was an investor in Adeptmind's Series A, a customer, and is widely reported to have acquired the underlying technology to power their "digital store of the future" personalized search. If the Ulta relationship represents a disproportionate share of revenue - or if IP was effectively transferred - the remaining independent business may have a materially different profile than the external-facing company suggests.
The assessment's customer sourcing process will surface how many active independent customers Adeptmind has, their concentration by revenue tier, and their stated satisfaction and retention intent - without relying on management-provided lists. This creates a ground-truth view of the customer base that is independent of whatever Adeptmind discloses in preliminary diligence.
The most actionable output for Nfluence is not just the data - it's the narrative frame the data enables. If Adeptmind's customers confirm that the platform cannot be replaced by a direct LLM API without significant workflow disruption, the CIM narrative shifts from defensive to affirmative:
From: "We build on LLMs but here's why that's okay."
To: "Adeptmind is the critical context and orchestration layer that makes LLMs commercially useful in retail - infrastructure that foundation model providers cannot displace without the retailer's proprietary catalog data and 18+ months of behavioral intent training."
Priced for sell-side engagement economics. Delivered in 14–21 days. Structured for CIM integration from day one.
The average cost to produce a full Q of AI Assessment is ~$18K–$20K in research and labor. Comparable engagements with PE clients are priced at ~$30K–$50K at full commercial rates. For our banking partners, we price near cost - because the relationship compounds.
| Factor | Crossover Q of AI Assessment | DIY / In-Process Research |
|---|---|---|
| Timeline | ✓ 14–21 days from kickoff | ✗ 6–12 weeks minimum |
| Independence | ✓ Third-party - no vendor list | ✗ Often relies on vendor contacts |
| AI Disintermediation Framework | ✓ Proprietary Q of AI rating | ✗ No standardized framework |
| IC-Grade Structure | ✓ Dual scorecard + verbatim attribution | ✗ Typically anecdotal |
| CIM-Ready Output | ✓ Built for M&A context from day one | ✗ Requires translation into CIM format |
| Buyer Objection Guide | ✓ AI objection handling built in | ✗ Not included |