The AI Search
Baseline Report.

Lumen Field Coffee earned a B-minus on its April readiness audit, up six points from March. The brand is well established on Reddit and within espresso enthusiast subreddits, but it is still missing from a third of the AI answers customers are actually asking. The single biggest lift available this sprint is publishing a dedicated answer page for espresso coffee subscriptions and adding Article and FAQ schema across the cold-brew hub.

On a brand-on-brand basis, Lumen Field is now competitive: branded prompts return clean, accurate answers with positive sentiment in 71% of cases on ChatGPT and 64% on Perplexity. The work this quarter is on category prompts — the queries customers run before they ever type the brand name. Today, Lumen Field is mentioned in just over half of these and cited in only 38%, with the gap concentrated in espresso, sustainability and "best of" buying guides where the answer page either does not exist or buries the answer below 1,200 words of brand storytelling.

Three moves close the largest share of that gap inside one sprint: ship a dedicated espresso answer page, add Article and FAQ schema across the cold-brew hub, and pitch Sprudge for a sourcing-story feature. Modelled together, those three close approximately +18 readiness points and lift recommendation rate by 4–6 points on Perplexity.

Each pillar scores between 0 and 100 with two axes of confidence: signal strength (how much evidence Ampli has) and signal stability (how consistent that signal is week to week). The rollup is weighted toward retrieval-side pillars (Answer Coverage, Citation Capture, Retrievability) because those are the strongest predictors of AI mention and citation outcomes in our model.

Engine-by-engine breakdown of mention, citation, recommendation and positive-sentiment rates across the audited prompt set. ChatGPT and Perplexity are the strongest surfaces for the brand today; Claude is the weakest and the only engine where Lumen Field is below its category average.

A representative answer captured during the audit. Every score in this report traces back to verbatim outputs like this one — the exact prompt, the exact response, the exact citations, all stored as evidence and downloadable from the workspace.

The full prompt set sits in the workspace; this section samples eight high-traffic prompts spanning branded and category intents. Use it to read the audit at the row level — what was asked, who showed up, who got cited and which competitors were named.

AI engines build answers by stitching cited sources together. The brands that own those sources own the narrative. This is the citation share for category prompts in Lumen Field's space — where you appear, where you do not, and which sources are doing the heavy lifting for competitors.

Every audited prompt is matched to the page on the site that should answer it, and that page is scored on structural fit — how cleanly an AI engine can extract a direct answer. Gaps and weak fits are the highest-leverage content investments this quarter.

Findings are grouped into 12 categories. Each finding is graded for severity, owned by a specific team, and traces back to one or more affected URLs. The five categories below account for 78% of the priority work.

Gemini and Claude weight authorship and freshness signals heavily when arbitrating between two roughly equal sources. Lumen Field's foundations are sound but a quarter of the journal corpus is missing bylines and a third of pillar pages have not been updated in 18 months. Both are addressable in-sprint.

Reddit drives 28% of citations in this category and AI engines lean on it heavily for recommendations. Lumen Field has earned a strong, organic presence in r/coffee and r/Coffee_Roasters but is being out-talked roughly 3:1 by Trade Coffee in raw mention volume. Sentiment is positive (64%) and the conversation is brand-led rather than promotional.

Eight category competitors scored on the same twelve pillars and the same prompt set. Trade Coffee leads the category overall, driven by deeper publisher relationships. Stumptown leads on espresso. Lumen Field leads on cold brew but lags on espresso and sustainability.

The action plan above rolls up into three sequenced sprints. Each sprint balances quick wins with one larger investment, and ends with a checkpoint where the score is re-tested against the same prompt set.

Targets reflect the modelled outcome of executing the action plan in sequence. They are not guarantees — AI engines are non-deterministic and category dynamics shift weekly — but they are calibrated against three months of historical movement on the same prompt set.

• AI engines are non-deterministic. Identical prompts return different answers run-to-run. Targets reflect medians across three audit runs; week-to-week movement of ±3 points is expected and not a regression.
• Editorial outreach is not guaranteed. The Sprudge feature is modelled as in-flight, not landed. If the pitch slips a sprint, citation rate gains shift to Sprint 33 but do not disappear.
• Category dynamics shift. Trade Coffee shipped a new sustainability landing page in late March. If they sustain the editorial push, share-of-voice gains may compress by 1–2 points.
• Sample sizing on niche prompts. Espresso prompts have a smaller sample than cold-brew. Confidence on espresso-specific moves will rise as the answer page accrues citation evidence.

This audit ran 64 prompts across ChatGPT, Gemini, Claude and Perplexity, captured 354 third-party citations, crawled 187 pages on lumenfield.coffee, scored 12 readiness pillars and reconciled the prompt set with three months of Search Console click data.

Audit confidence is the weighted average of signal strength and signal stability across the 12 pillars. A score of 86% means the underlying signals are well-evidenced and consistent across the reporting window. Pillars with confidence below 75% are flagged individually in the workspace.

The same overview the workspace opens to. Score, grade, two-axis confidence, twelve pillars at a glance and the highest-priority findings of the run.

Run-by-run prompt visibility across ChatGPT, Gemini, Claude and Perplexity, plus AI Overview inclusion from the SERPAPI feed and a twelve-week trend view. Toggle between sub-tabs to inspect the raw data or the engine-level rollup.

A single 0–100 score blended from mention rate, prominence, recommendation rate and citation rate across the audited prompt set. Track the brand against tracked competitors and watch the line move as recommendations ship.

Every captured citation, grouped by source domain, with engine breakdown, sentiment and share-of-voice. The heatmap surfaces concentration risk — where one or two sources are doing all the work.

Traditional ranking position, SERP-feature ownership and SoV trend lines for the tracked keyword set. We layer this with AI visibility to separate "ranking but not cited" from "cited but not ranking" — two different problems with two different fixes.

The full prompt → page assignment grid with structural fit, schema status and answer extractability per row. The narrative section above samples the worst offenders; this is the complete map.

Every finding from the audit, grouped by category, severity-graded and ownable. Each row links back to the rule that generated it and the affected URLs so a developer can jump straight to the fix.

The action plan as a Kanban — critical, high, medium and low columns — with effort, owner and projected impact on each card. Drag-to-reorder and assign in the workspace; here it is the read-only print equivalent.

Author byline coverage, content freshness distribution, trust-page completeness (contact, address, business identity) and structured-data conformance. The signals Gemini and Claude lean on hardest when arbitrating between two equally-relevant sources.

Subreddit footprint, sentiment split, AI-citation rate from community sources and head-to-head share of voice with the competitor set. Reddit is over a quarter of all citations in this category, so it is treated as a first-class surface.

The full sprint roadmap — sequenced work, owners, estimated effort, projected pillar impact and the score-checkpoint at the end of each window. The same plan the workspace will track against week-by-week.

The competitor set scored on the identical prompt sweep and pillar rubric. Use it to calibrate "good" — readiness only matters relative to the competitors fighting for the same answer real-estate.