How do I set up a question research API for building topic briefs around featured snippets and shopping results?

Here’s a practical way to set up a question research API for building topic briefs aimed at featured snippets and shopping results.

1) Define the goal

Your API should take a seed topic or product and return:

• Questions people ask
• SERP intent signals
• Featured snippet opportunities
• Shopping/product intent opportunities
• Clusters and brief-ready summaries

Example input:

{
  "query": "best espresso machine",
  "market": "us",
  "language": "en"
}

Example output:

{
  "topic": "best espresso machine",
  "intent": "commercial investigation",
  "questions": [...],
  "featured_snippet_targets": [...],
  "shopping_targets": [...],
  "brief": {...}
}

---

2) Use data sources

To research questions well, combine multiple sources:

A. SERP data

Use a SERP provider or search API to fetch:

• Top organic results
• Featured snippet presence
• People Also Ask questions
• Shopping results
• Product grids / merchant listings
• Related searches

Useful fields:

• has_featured_snippet
• paa_questions
• shopping_cards
• top_titles
• ranked_domains

B. Keyword/question sources

Pull from:

• Autocomplete suggestions
• People Also Ask
• Related searches
• Question databases
• Forums/Q&A sites if permitted

C. Product/catalog data for shopping briefs

For shopping-oriented topics, also ingest:

• Product feeds
• Merchant catalog
• Review/rating data
• Price ranges
• Brand/model attributes

---

3) Design the API endpoints

A simple structure:

POST /research

Main endpoint. Accepts a seed query and returns research data.

Request:

{
  "query": "best espresso machine",
  "market": "us",
  "language": "en",
  "device": "desktop",
  "depth": "medium"
}

Response:

{
  "query": "best espresso machine",
  "intent": "commercial investigation",
  "serp_features": {
    "featured_snippet": true,
    "shopping_results": true,
    "paa_count": 8
  },
  "questions": [
    {
      "question": "What is the best espresso machine for home use?",
      "type": "comparison",
      "priority": 0.92
    }
  ],
  "snippet_targets": [
    {
      "question": "What is the best espresso machine for home use?",
      "answer_format": "list",
      "recommended_answer_length": "40-60 words"
    }
  ],
  "shopping_targets": [
    {
      "angle": "best under $500",
      "attributes": ["price", "pressure", "milk frother", "size"]
    }
  ]
}

Optional endpoints

• POST /cluster — group questions by intent
• POST /brief — generate a topic brief from research
• GET /serp/:query — raw SERP snapshot
• GET /questions/:query — question suggestions only

---

4) Build the research pipeline

A good pipeline is:

Step 1: Query normalization

Clean and normalize the seed:

• lowercase
• remove punctuation
• detect product/topic type
• identify modifiers: “best”, “cheap”, “for beginners”, “near me”

Step 2: SERP fetch

Call your SERP/search provider and extract:

• featured snippet text
• PAA questions
• shopping block presence
• organic titles/meta
• related searches

Step 3: Question expansion

Generate more question variants:

• what is
• how to
• best
• vs
• for [use case]
• under [budget]
• near me
• alternatives
• problems/solutions

Step 4: Intent classification

Classify each query into:

• informational
• commercial investigation
• transactional
• navigational

For shopping briefs, prioritize commercial and transactional terms.

Step 5: Clustering

Group by:

• buying stage
• product attributes
• pain points
• use cases
• comparison questions

Step 6: Brief generation

Produce a structured brief:

• search intent
• main questions
• snippet opportunities
• shopping angles
• recommended headings
• supporting entities and attributes

---

5) Featured snippet logic

To target featured snippets, identify queries where:

• the SERP already has snippets
• the query is question-based
• the answer can be formatted as:
  • paragraph
  • list
  • table
  • steps

Useful outputs:

• snippet type
• ideal word count
• exact question wording
• suggested answer structure

Example:

{
  "question": "How do espresso machines work?",
  "snippet_type": "steps",
  "answer_guidance": "Use 4-6 short steps with a clear opening sentence."
}

---

6) Shopping result logic

For shopping results, look for:

• product intent keywords
• price modifiers
• brand/model mentions
• spec comparisons
• high purchase urgency

Extract shopping attributes:

• price
• brand
• model
• size
• material
• rating
• shipping
• availability
• warranty

Then generate product-oriented brief sections:

• top buying considerations
• must-have specs
• common comparison points
• category filters
• “best for” subsegments

---

7) Suggested tech stack

A common stack:

• API: FastAPI, Express, or Django REST
• Queue: Celery, BullMQ, or Sidekiq
• Storage: Postgres for research records, Redis for caching
• Search/SERP provider: third-party API
• NLP: keyword expansion + embeddings + lightweight classifier
• LLM: optional for brief summarization and clustering

---

8) Data model

Store results in tables like:

research_jobs

• id
• query
• market
• language
• status
• created_at

serp_snapshots

• job_id
• feature_type
• raw_json
• fetched_at

questions

• job_id
• question
• source
• intent
• cluster
• priority

briefs

• job_id
• title
• summary
• outline_json

---

9) Scoring system

Score each question/opportunity using:

• search volume
• SERP feature presence
• intent match
• content gap
• business value
• snippet suitability
• shopping conversion potential

Example:

{
  "question": "What is the best espresso machine under $500?",
  "score": 0.96,
  "reason": ["shopping intent", "price modifier", "comparison query", "snippet-friendly"]
}

---

10) Example brief structure

A generated brief could include:

• Primary topic
• Target intent
• Featured snippet questions
• Shopping-related questions
• Key attributes to cover
• Recommended headings
• Entities/brands/models
• FAQ section
• Content gaps vs competitors

---

11) Practical implementation pattern

If you want a simple MVP:

1. Accept seed query
2. Fetch SERP/PAA/shopping results
3. Normalize and expand questions
4. Classify intent
5. Rank opportunities
6. Return JSON brief

That alone is enough to build useful topic briefs.

---

12) Example MVP API response

{
  "query": "best running shoes",
  "intent": "commercial investigation",
  "questions": [
    "What are the best running shoes for flat feet?",
    "Which running shoes are best for beginners?",
    "How do I choose running shoes?"
  ],
  "featured_snippet_opportunities": [
    {
      "question": "How do I choose running shoes?",
      "format": "bulleted list"
    }
  ],
  "shopping_opportunities": [
    {
      "question": "What are the best running shoes under $100?",
      "attributes": ["price", "cushioning", "arch support", "brand"]
    }
  ],
  "brief_outline": [
    "What to look for in running shoes",
    "Best running shoes by use case",
    "Budget options",
    "FAQ"
  ]
}

If you want, I can also give you:

1. a FastAPI example implementation,
2. a database schema, or
3. a prompt/spec for generating the topic briefs.