Why Simply Asking a Question Is Not Enough

Most people ask AI "How is brand X?" and accept the answer as an analysis result. But this approach has serious shortcomings:

A single question produces a superficial answer
The model's current "mood" affects the result
Multiple perspectives are missing
Numerical scoring becomes inconsistent

AURA solves this problem with a two-stage approach: Miner + Analyst

Stage 1: Miner (Data Mining)

In the first stage, 5 different questions are asked to 9 different AI models. These questions are specially designed "miner prompts":

5 Miner Prompt Categories

Category Authority: "What are the best brands in this sector?" - Measures your brand's position in the industry
Entity Check: "Do you know brand X?" - Tests whether AI recognizes your brand
Competitor Sentiment: "Compare brand X with brand Y" - Measures your perception relative to competitors
Brand Perception: "What do you think about brand X?" - Determines positive/negative perception
Market Position: "List the top 5 brands in this area" - Determines your ranking and position

Total: 45 Miner Calls

9 models x 5 prompts = 45 separate API calls. Each model answers each question from its own perspective. This creates a broad data pool instead of depending on a single model.

All miner calls run in parallel, so 45 calls complete in approximately 50-60 seconds.

Stage 2: Analyst (Synthesis and Scoring)

After collecting 45 miner responses, all data is sent to a single analyst model (Claude Haiku 4.5). This model:

Reads and analyzes all 45 responses
Applies a consistent scoring standard (0-100)
Identifies contradictions between models
Detects hallucinations (false information)
Calculates your position in the Top 5 list
Creates a SWOT analysis
Generates strategic recommendations

Why a Single Analyst?

Each AI model has different scoring standards. GPT-4o might give generous scores while Llama might be more conservative. By using a single analyst model, we obtain consistent and comparable scores.

Stage 3: Executive Summary

Finally, the analysis results are transformed into an easy-to-read executive summary. This summary includes:

Overall situation assessment
Key strengths highlighted
Areas requiring immediate attention
Priority action recommendations

AURA Analysis by the Numbers

Metric	Value
Total API calls	47 (45 miner + 1 analyst + 1 summary)
AI models used	9 different LLMs
Miner prompt count	5 different question categories
Average analysis time	~2 minutes
Scores generated	5 metrics (Visibility, Accuracy, Sentiment, Competition, Growth)

Thanks to this comprehensive approach, AURA produces much more reliable and actionable results than a superficial estimate.

Miner + Analyst: AURA's Two-Stage Analysis Approach