01
Multi-model measurement
Tracked prompts are analyzed across ChatGPT, Claude, Gemini, Perplexity, and related AI-search surfaces to show presence, position, sentiment, and change over time.
AI / Brand Intelligence / Case study
Lantern
From AI answers to actions brands can take.
An AI-search intelligence platform that shows ecommerce teams where models recommend their brand, why competitors appear, and what to improve next.
The brief / 01
Build the product around the work it needs to change.
- Product
- AI search intelligence platform
- Users
- Ecommerce & marketing teams
- Coverage
- Major AI answer platforms
- Focus
- Visibility, citations & recommendations
Challenge
As product discovery moves into AI assistants, ecommerce teams need to know whether their brand appears in buyer answers, how it is described, which sources shape the response, and where competitors have an advantage.
Response
Lantern runs tracked buyer questions across major AI platforms, extracts brand mentions and citations, scores presence and sentiment, forecasts visibility, and turns identified gaps into prioritized recommendations for content, product data, and off-site signals.
The product / 02
One system. Built around real users and operations.
02
Citation intelligence
Teams can see which owned, earned, marketplace, and competitor sources influence answers and where stronger evidence is needed.
03
Forecasts & recommendations
Predicted visibility and scenario analysis connect observed gaps to ranked actions a marketing or ecommerce team can evaluate and deliver.
Outcome / 03
What the product makes possible.
01
AI discovery becomes measurable
Teams gain a repeatable view of where a brand is present or absent across the buyer questions and AI providers they track.
02
Competitive gaps become visible
Brand mentions, position, sentiment, and citation sources can be compared with configured competitors at prompt level.
03
Analysis leads to action
The platform connects measurement to prioritized product-data, content, citation, and off-site improvements rather than stopping at a score.
Capabilities / 04
Work delivered across the product.
- AI product strategy
- Data platform engineering
- Analytics product design
- AI evaluation systems
- Forecasting and recommendations
- Agent and tool integration
Let's build / 05