How Dabudai Extracts AI Links | Dabudai

Link Intelligence Methodology: How Dabudai Extracts and Interprets Links From AI Answers

Overview

Dabudai measures AI visibility using the links and placements users can see in AI answers. This is why we extract links from AI answers exactly as they appear in the provider interface.

In Dabudai, AI answer source links are the visible, clickable URLs shown inside the answer (including “Sources,” inline links, or cited references). We record brand position exactly as displayed in the provider interface. We do not deduplicate links. If the same page appears multiple times, every occurrence is counted.

Share of Voice is position-weighted: higher placement contributes more.

We support three core experiences: ChatGPT, Google AI Overviews, and Google AI Mode.

Customers select country + language, so outputs match the real market. We extract every visible link occurrence (your pages, other brands, and third-party sources) and analyze it at page level (no domain grouping). Because AI answers vary, we rely on aggregated data from repeated runs, not one-off checks.

Key definitions (foundation)

Link occurrence: one visible appearance of a clickable link in an AI answer; each appearance is counted, even if repeated.
Brand position: the placement of a brand in a list or structured answer, recorded as shown in the provider interface.
First-party link: a link to your own pages (your website URLs).
Other-brand link: a link to other brands’ pages, including competitors.
Third-party source: an external page URL linked or cited by the provider (tracked at page level, not grouped by domain).

Full set of canonical definitions → Link Definitions

Data fidelity rules (we record what users actually see)

Dabudai is built for accuracy and auditability. We record outputs as displayed in the provider interface. We do not alter placements or link occurrences.

Brand position is recorded as shown

We record brand position based on its placement in the AI answer. We do not re-rank, normalize, or reorder positions.

We do not deduplicate links

We do not deduplicate links inside a single answer. If the provider shows the same page multiple times, we record every occurrence. We report raw occurrences so teams can review the same repetition users see. This can affect page-level stats and Share of Voice.

Share of Voice is position-weighted (consistent weighting)

Share of Voice is position-weighted. Higher placement in an AI answer contributes more to SOV. Weighting is computed consistently by our backend across runs (no manual adjustments).

UTM removal (display-only cleaning)

We remove UTM parameters so teams can review clean URLs. This does not change the destination page the provider linked to.

We do not group by domain

We do not group links by domain. We keep page-level links exactly as returned in the AI answer. This preserves page-level winners instead of hiding them inside domain totals.

Quick rule table


Rule	What we do	Why it matters
Position as shown	Record brand placement exactly as displayed	Comparable placement over time
No link deduplication	Count every visible link occurrence	Matches what users see in the UI
Position-weighted SOV	Higher placement contributes more	Reflects real attention and impact
Remove UTMs	Clean display URLs only	Faster review without changing meaning
No domain grouping	Keep page-level links	Page-level insight stays accurate

Providers and locale controls (where results come from)

Different providers can return different answers and show links differently. Locale matters too: the same buyer question can produce different links and sources by country and language.

Primary providers

We measure across three core experiences:

ChatGPT
Google AI Overviews
Google AI Mode

Country + language selection (locale)

Customers select a country and language for analysis. We run measurements in that fixed locale so outputs match the target market. For clean comparisons over time, keep locale consistent.

What we extract from each answer (raw fields)

For every run, we store raw output so results are auditable and comparable over time. We capture the same elements users can see in the provider interface. Our AI response link detection process identifies every visible link in the answer and records it exactly as shown — including position and context.

Raw fields we capture (as shown)

From each AI answer, we record:

Provider (ChatGPT / Google AI Overviews / Google AI Mode)
Country + language (locale)
Timestamp
Buyer question ID
Full answer text
Position in the answer (brand placement, as displayed)
Every visible link occurrence (each appearance is recorded, no deduplication)
Link title (when shown)
Full URL (cleaned for readability by removing UTM parameters)
Domain (extracted from the full URL)
Visible sources (when displayed by the provider)

We also use AI context-aware link extraction to keep links tied to the correct citation context, even when providers change formatting, truncate URLs, or display sources differently across interfaces.

What we compute on the backend (derived metrics)

We then compute consistent metrics from the raw dataset, including:

Share of Voice (SOV)
Recommendation Rate (Top 5)
Aggregated views that summarize results for customers (across runs, topics, and buyer questions)

Share of Voice methodology (position-weighted, link-based)

Share of Voice (SOV) helps you compare AI visibility across brands. It is designed to reflect what users are most likely to notice in an AI answer.

What SOV represents in Dabudai

SOV is a position-weighted share based on how brands appear in AI answers. Higher placement contributes more than lower placement. We count every visible link occurrence, even if the same page appears multiple times (no deduplication).

Position-weighted SOV (text formula)

Assign a weight to each occurrence by position: w(r) = 1 / r, where r is the position (1 = highest).
Sum weights for a brand b across all its occurrences: W_b = Σ (1 / r_i).
Compute SOV as a share of total weight across all brands: SOV_b = W_b / Σ W_k.

Why position affects SOV

Top placements get more attention than lower placements. Position-weighting reflects this reality.

What influences SOV in your dataset

SOV can change based on:

Higher brand placements across answers
More visible link occurrences to your pages (including repeated appearances)
Stability across repeated runs (larger sample → stronger signal)
Locale differences (country + language)

Third-party source intelligence (external pages that shape answers)

AI answers often reference external pages. These pages can influence which claims and links appear in recommendations. Understanding them helps you increase the chance your pages get cited.

What we capture

We capture third-party links as shown in answers. We keep them at page level (we do not group by domain). We record every visible occurrence, even if a source page appears multiple times.

Practical examples (so it’s clear)

If review pages dominate “best” questions, improve your listings and publish one proof page that supports your main claim.
If media articles dominate a topic, pitch a data-backed angle and publish a methodology page that can be cited.

Sampling and aggregation (why we use trends, not one-offs)

AI answers can vary even when the prompt stays the same. That is normal. It is why one-off analysis is not reliable.

Our sampling approach

We run the same buyer questions multiple times per day and aggregate results across the full dataset. We use weekly windows to reduce noise and see stable trends. In general, a larger sample size produces a more valid signal.

Manual replication checklist (7 steps)

If you want to reproduce Dabudai manually, use this workflow. It is verifiable, but it is time-consuming.

Take 20 buyer questions that match your ICP.
Choose providers (ChatGPT, Google AI experiences).
Fix country + language for the week.
Use a stable output request format (for example: “Top 5 + links when shown”).
Run every question multiple times per day in each provider.
Record brand positions exactly as shown.
Copy every visible link occurrence (including repeats), across your pages, other brands, and third-party sources.

Repeat for 7 days, then calculate metrics using the full dataset.

FAQ

What counts as a “link” in Dabudai?

A link is a visible, clickable URL shown in the AI answer.

Why don’t you deduplicate links?

Because we want statistics to match what users see in the AI interface. Each visible occurrence is counted, even if the same page appears multiple times.