AI Discoverability
Help AI systems find your content and understand your business.
See how Indexwell helps web teams improve access to their content, strengthen the information AI systems use, and understand how their business appears across AI search. Each service connects findings to priorities, guidance, and validation.
Access & Delivery
Check whether the intended systems can reach your pages and receive the information your team means to publish.
AI Crawler Access & Content Controls
Define which AI systems can access your content and how that access aligns with your business preferences.
Why it matters
Search discovery, model training, and user-requested visits serve different purposes. Blocking all AI traffic can take public content out of the search experiences where you want it to appear.
Our approach
We compare your intended policy with crawler rules, indexing directives, CDN restrictions, and platform inclusion settings. You receive a record of what is allowed, what is blocked, and the changes needed to bring those settings into line with your decisions.
Content Retrieval & Rendering
Check what search crawlers and AI retrieval systems actually receive from important pages.
Why it matters
A page that looks complete in a browser can still return missing text, blocked resources, errors, or conflicting versions of information to another client.
Our approach
We inspect server responses, rendered pages, and extracted text, then compare them with available crawl and log evidence. Findings identify affected templates, missing information, and delivery failures, with developer requirements and post-change checks. Bot activity is treated as access evidence, separate from answer inclusion.
Content & Sources
Understand the questions, competing sources, and content decisions that shape your opportunity in AI search.
Audience Questions & Search Scenarios
Map the questions people ask while learning about a problem, comparing options, and deciding whether your business fits.
Why it matters
Tracking a few generic prompts can miss the specific needs, constraints, and decisions that matter to your customers.
Our approach
We combine search data with the questions your sales, support, and subject experts hear. The result is a prioritized set of topics and realistic scenarios, with the audience, intent, and relevant pages documented for research and monitoring. The research sample is defined so your team knows what it represents.
AI Source & Competitor Research
Examine which websites and pages AI answers cite for your priority topics, and where your competitors enter the conversation.
Why it matters
The useful comparison is the cited material: what it covers, how it presents evidence, and whether your business appears accurately within it.
Our approach
We inspect a defined sample of answers and verify their linked sources. Findings separate your own content gaps from external opportunities, such as outdated profiles, missing partner information, or relevant industry coverage. You receive a source map and recommended next steps for the teams responsible.
Content Coverage & Evidence
Identify the information your audience needs that is missing, outdated, or poorly supported across your current site.
Why it matters
Your pages need useful answers and credible material worth referencing. Original expertise, clear explanations, and supporting evidence give people more reason to rely on your content.
Our approach
We map priority questions to existing pages and assess factual depth, original expertise, supporting evidence, and duplication. Your team receives a prioritized plan for what to create, expand, refresh, or consolidate, with briefs and evidence requirements for the people producing the content.
Page Structure & Answer Clarity
Make existing information easier to follow and accurately interpret, including when an individual section is read on its own.
Why it matters
Vague labels, unexplained claims, and qualifications separated from the facts they limit can make otherwise useful content hard to understand.
Our approach
We examine headings, definitions, comparisons, tables, examples, and the relationships between claims and sources. Recommendations show where to clarify language, keep essential context together, or change a template. Your team gets annotated examples and reusable guidance for relevant page types.
Brand & Product Information
Improve the accuracy and consistency of the business and product information available to people and AI systems.
Brand Information & Accuracy
Find and address outdated or incorrect descriptions of your business, products, services, and people in AI answers and the pages they cite.
Why it matters
A mention can still be unhelpful if it uses an old brand name, describes the wrong offer, or attributes a feature you do not provide.
Our approach
We compare sampled answers with facts confirmed by your team, then inspect relevant website pages, documentation, and profiles you control. You receive an issue log with source URLs, proposed corrections, and owners. Follow-up checks track whether the sources are corrected and how answers change.
Product Data for AI Shopping
Help ecommerce teams prepare consistent product information for supported shopping experiences in AI search.
Why it matters
Missing variants, stale availability, and conflicting prices can leave platforms with an incomplete or inaccurate picture of what you sell.
Our approach
We compare product pages and feeds against the requirements of relevant platforms, including Google Merchant Center and eligible ChatGPT product-discovery integrations. Your team receives field mappings, data-quality findings, and update checks for identifiers, attributes, price, availability, and merchant policies, with eligibility requirements made clear.
Measurement & Validation
Define what can be observed, connect the useful signals, and verify the work your team ships.
Mentions & Citation Tracking
Establish a repeatable view of where your brand appears and which pages are cited across selected AI search platforms.
Why it matters
Brand mentions, links, and impressions describe different types of visibility. A useful baseline makes those distinctions and shows what the data covers.
Our approach
We define the topics, prompt sample, platforms, and reporting cadence, then combine tracked answers with available first-party evidence. Reporting separates brand mentions, cited URLs, Google’s AI impressions, and Bing’s citation data, with source definitions and coverage limits alongside the trends.
Referral Traffic & Business Outcomes
Understand what identifiable visits from AI platforms do after people reach your site.
Why it matters
Landing pages, engagement, leads, and purchases show different stages of a visit. Useful reporting follows the stages your team can actually measure.
Our approach
We review referral classification, campaign parameters, landing pages, and configured conversion events. Your team gets reporting definitions, tracking requirements, and an analysis of identifiable visits and outcomes. We document attribution gaps so reported traffic is understood in the context of the data available.
Testing & Change Validation
Check whether implemented changes work as intended, and test which improvements deserve a wider rollout.
Why it matters
AI responses can vary even when your website has not changed. Recording the change and comparing results consistently helps your team make better decisions.
Our approach
We define the hypothesis, target pages, baseline, and evaluation criteria. After your team ships the change, we validate the technical or content requirements and repeat relevant observations. Where feasible, comparisons use unchanged pages or other controls; findings distinguish confirmed fixes from uncertain effects on visibility.
Technical SEO supports this work. Explore the wider service library for crawl access, indexation, rendering, structured data, and site performance.
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