Key takeaways
- Enterprise AI visibility contracts typically run 12 months or longer, so the questions you ask before signing matter more than the demo.
- The biggest failure mode in AI vendor procurement is signing before anyone asked where your data actually goes, or how the vendor's own LLM routing works.
- Ask about data sources (API vs. real UI), model coverage, crawler logs, execution vs. monitoring, and traffic attribution. These are the areas where monitoring-only tools quietly fall short.
- Get pricing mechanics, prompt limits, and overage terms in writing. Per-response pricing models vary wildly between vendors.
- Run a structured pilot against a competitor before committing. A 60-day head-to-head on your own prompts tells you more than any sales deck.
Brandlight positions itself as an enterprise AI visibility operating system, and it is a credible one in several respects. But enterprise contracts are a different sport from a monthly SaaS subscription. You are committing a year of budget, your data, and your team's attention. The ten questions below are the ones I would want answered in writing before any signature, and most of them apply to any AI visibility vendor, not just Brandlight.
Why the questions matter more than the demo
A standard IT procurement checklist was built for deterministic software: features, uptime, support, price. AI visibility platforms break that checklist in a few specific ways. Their outputs are probabilistic, the same prompt can return different answers on different days, and many vendors route your prompts through third-party LLM APIs without saying so explicitly. Worqlo's 2026 enterprise AI RFP framework estimates that a standard IT RFP misses up to 60% of the risk-relevant questions for AI vendors, which is how you end up with a signed contract and a security team that never asked where the data goes.
There is also a category-specific trap. "AI visibility" sounds like one thing, but the market splits into prompt trackers (was my brand mentioned?) and full optimization platforms (why are we invisible, and what do we do about it?). Brandlight sits in the enterprise tier of this market, where contracts are bigger and the switching costs are higher. That makes the discovery questions below worth real money.
The ten questions
1. Do you monitor real user interfaces, or only APIs?
This is my favorite question in the whole list because the honest answer is rarer than you would think. Many visibility platforms run their prompts through model APIs, which are cheaper and faster but can return different answers than what actual users see. ChatGPT's user-facing answers, citations, and shopping recommendations can differ from its API outputs. A vendor measuring only APIs is measuring a proxy.
Ask Brandlight to state, in writing, whether it monitors the live interfaces of ChatGPT, Gemini, Google AI Overviews, AI Mode, Perplexity, and Claude, or API endpoints. If it is API-only, that is not automatically disqualifying, but you should know it before you pay enterprise rates.
2. Which models and surfaces do you cover, and how fast do you add new ones?
The AI search market in 2026 is fragmented. ChatGPT, Claude, Gemini, Perplexity, Grok, DeepSeek, Copilot, Mistral, Meta's models, Google AI Overviews, and AI Mode all behave differently, and they change monthly. Promptwatch's own crawler data shows how volatile this is: Meta's new web indexer went from roughly 2% to nearly 38% of tracked AI crawler requests between mid-July and August 2026, and ChatGPT started using the site: operator at scale on August 8, jumping from about 0.4% to roughly 17% of fanout queries overnight.
Ask for the current list of covered models, the date each was added, and the vendor's commitment for new surfaces. "We add models as they launch" is fine as a slogan, but you want the actual lag time. If a vendor took three months to add AI Mode, that is three months of blind spots on a surface your customers probably use.
3. How do you explain why we are or are not visible?
Mention tracking is table stakes. The enterprise question is diagnosis. If ChatGPT never cites your product pages, is it because your pages are not crawled, because they are crawled but not cited, because a competitor's comparison page dominates the prompt, or because your content simply does not answer the question?
The strongest signal here is AI crawler log data: which of your pages AI systems actually visit, what they read, and whether they hit errors. This is a capability most monitoring tools lack entirely, and it is the difference between guessing and knowing. Ask Brandlight whether it ingests your CDN or server logs (Cloudflare, Fastly, Vercel, and similar), and whether it can connect a crawl visit to a citation. If the answer is no, you are buying a scoreboard without a coach.
4. What can the platform actually do, beyond showing me dashboards?
This is where the market's biggest divide shows up. Some platforms stop at monitoring. Others close the loop: content gap analysis, briefs, automated content generation, CMS publishing, and prioritized action lists. If your team is going to spend a year inside this tool, you want the tool to reduce work, not just report on it.
Ask for a live walkthrough, not a slide, of the execution features. Can it generate a content brief grounded in the actual AI responses you are losing? Can it publish to your CMS? Is there a prioritized to-do list derived from the data, or does your team have to interpret dashboards manually?
5. Where does our data go, and who processes it?
The procurement question that most often gets skipped, and the one that causes the most pain later. Many AI vendors process your prompts and data through third-party LLM APIs (OpenAI, Anthropic, Google) without making this explicit in sales conversations. Ask Brandlight directly:
- Which LLM providers process our prompts and content?
- Is our data used for training, by you or by your providers?
- Where is data stored, and under which jurisdiction?
- What happens to our data at contract end?
Get plain-language answers, not marketing language. If the vendor cannot answer the training-data question clearly, that is a red flag regardless of how good the product is.
6. How is pricing structured, and what happens when we exceed limits?
AI visibility pricing is usually built around response volumes, prompt counts, or tracked sites, and the mechanics matter more than the headline number. Ask for:
- The unit of billing (responses, prompts, sites, seats)
- Overage rates, and whether they are capped
- What happens to your data and history if you downgrade
- Annual vs. monthly terms, and any multi-year discount strings
For context, the market runs from free tiers (10 prompts, one model) to several hundred dollars a month for mid-market plans, with enterprise deals negotiated individually. Brandlight's pricing is not public in the same way, which is normal for enterprise sales but is exactly why this question belongs in writing. A vendor that quotes per-response pricing should tell you your expected monthly response volume and the cost if you double it.
7. What does onboarding actually look like, and how long until we see real data?
Ask the vendor to name a comparable customer that went live, how long it took, and what broke after launch. Vendors that have run real enterprise deployments can answer this in detail. Vendors that have not will talk about their process in general terms.
The specifics you want: who does prompt set-up (you or them), how long until first reports, whether historical data is backfilled or starts from day one, and what the vendor needs from your team (log access, CMS credentials, analytics connections). If the honest answer is "four weeks of your team's time," you want to know that now, not in month two.
8. How do you measure business impact, not just mentions?
A mention is not revenue. The enterprise-grade question is whether the platform can connect AI visibility to actual traffic and conversions from AI platforms to your site. Ask whether Brandlight tracks AI-driven visitors separately from generic referral traffic, whether it attributes conversions, and how it handles the analytics integration (Google Analytics, server-side, or its own tracking).
If the platform can only report share of voice and sentiment, it will be hard to defend the budget at renewal. The CMO conversation eventually lands on "what did this revenue?" and your tool should have an answer.
9. What does support and SLA look like at our tier?
Enterprise contracts should come with enterprise support terms. Ask for the SLA in the contract itself, not in a support page: response times, escalation path, a named contact or CSM, and what counts as an outage. Also ask what happens when a model changes its behavior overnight (this happens regularly in 2026) and your data goes weird. Does the vendor notify you, or do you discover it in a board deck?
10. What are the exit terms?
Nobody asks this one and everybody should. What is the notice period? Do you keep your historical data after termination, and in what format? Is there a wind-down period? What is the refund position on unused months if the vendor fails to deliver onboarding milestones? Given that switching AI visibility vendors means rebuilding prompt sets and losing historical trend lines, the exit terms are your only leverage once you have signed. Negotiate them while you still have leverage, which is before signature.
How Brandlight's answers should be scored
Score each answer against this simple rubric:
| Question area | Strong answer | Weak answer |
|---|---|---|
| Data source | Real UI monitoring across major surfaces, stated in writing | "We use APIs" or vague "proprietary methods" |
| Model coverage | Current list with add dates, commitment to new surfaces | "All major models" with no specifics |
| Diagnosis | Crawler logs, citation paths, page-level analysis | Mention counts and sentiment only |
| Execution | Content gap analysis, briefs, CMS publishing, action lists | Dashboards only |
| Data handling | Named LLM providers, no training use, clear retention | Marketing language, no specifics |
| Pricing | Unit billing, capped overages, written examples | "It depends on your needs" |
| Impact | AI traffic and conversion attribution | Share of voice only |
| Exit | Data export, notice terms in contract | Silence, or "standard terms" |
Run a pilot against a competitor before you sign
The single best procurement move in this category is a 60-day head-to-head. Pick your 50 most commercially important prompts, give the same set to Brandlight and one or two competitors, and compare the outputs on your actual business, not on demo data. Platforms like Promptwatch offer trial periods and free tiers that make this comparison cheap to run, and a structured pilot will surface more in two months than any RFP document will in six.
If you want to see the broader field before shortlisting, the GEO software directory at bestgeosoftware.com covers the full category, and our comparison of GEO and AI visibility platforms breaks down feature-by-feature differences across the major vendors.
The bottom line
Brandlight may well be the right choice for your enterprise. But the enterprise AI visibility market is barely two years old, vendors are still building their platforms in public, and contract terms are being written faster than procurement teams can read them. The ten questions above are not hostile questions. They are the questions a vendor with a mature product and a real deployment history will answer quickly and in detail. If any of them get a vague answer, a redirect to the roadmap, or a "we can discuss that after signature," you have learned something important about the contract you were about to sign.