parallel-research

name	parallel-research
description	AI-powered deep research using Parallel AI APIs for chat, research reports, entity discovery, and data enrichment. Use this skill when doing web research, competitive analysis, market research, generating research reports, finding companies matching criteria, or enriching existing data. Triggers on research requests, competitive intelligence, finding companies, or data enrichment tasks.

Parallel Research

Overview

Deep web research, competitive intelligence, entity discovery, and data enrichment using Parallel AI's specialized APIs.

Quick Decision Tree

What do you need?
│
├── Quick factual answer (3-5 seconds)
│   └── Chat API ($0.005/request)
│   └── Script: scripts/parallel_research.py chat "question"
│
├── Comprehensive research report (5min-2hr)
│   └── Deep Research API ($0.30/report for ultra)
│   └── Script: scripts/parallel_research.py research "topic"
│
├── Find entities matching criteria (companies, people)
│   └── FindAll API ($0.03 + $0.10/match)
│   └── Script: scripts/parallel_research.py findall "query"
│
└── Enrich existing data (add fields to records)
    └── Task API with schema ($0.025/record for core)
    └── Script: scripts/parallel_research.py enrich data.csv

Environment Setup

# Required in .env
PARALLEL_API_KEY=your_api_key_here

Get your API key: https://platform.parallel.ai/settings/api-keys

Common Usage

Quick Q&A

python scripts/parallel_research.py chat "What is Anthropic's latest funding round?"

Deep Research Report

python scripts/parallel_research.py research "Competitive landscape of AI code editors in 2025" --processor ultra

Find Companies

python scripts/parallel_research.py findall "AI code editor companies that raised funding in 2024-2025" --limit 50

Basic Research (Simplified)

python scripts/basic_research.py "Company Name"

Vendor Selection

python scripts/vendor_selection.py "CRM software" --requirements "enterprise,API,automation"

Processor Tiers

Processor	Cost/1K	Latency	Best For
lite	$5	10-60s	Basic metadata
base	$10	15-100s	Simple research
core	$25	1-5min	Cross-referenced research
pro	$100	2-10min	Exploratory research
ultra	$300	5-25min	Deep research (recommended)
ultra-fast	$300	2-10min	Speed + quality

Cost Estimates

Task	API	Cost
100 quick questions	Chat	$0.50
Market research report	Deep Research (ultra)	$0.30
Find 50 competitors	FindAll (core)	~$5.00
Enrich 100 leads	Task (core)	$2.50

Free Tier

20,000 requests free (combined across all APIs).

Security Notes

Credential Handling

• Store PARALLEL_API_KEY in .env file (never commit to git)
• Regenerate keys at https://platform.parallel.ai/settings/api-keys
• Never log or print API keys in script output
• Use environment variables, not hardcoded values

Data Privacy

• Research queries are sent to Parallel AI servers
• Research outputs may contain third-party company information
• Results are stored locally in .tmp/ directory
• Parallel AI may log queries for service improvement
• Avoid including sensitive internal data in research queries

Access Scopes

• API key provides full access to all research endpoints
• No granular permission scopes available
• Monitor usage and costs via Parallel AI dashboard

Compliance Considerations

• Data Sources: Research pulls from public web sources
• Citation: Always cite sources in research outputs
• Accuracy: AI-generated research should be verified
• Competitive Intel: Ensure competitive research complies with policies
• Third-Party Data: Respect intellectual property of sources
• PII in Results: Research results may contain company/individual PII
• Data Freshness: Verify currency of time-sensitive information

Troubleshooting

Common Issues

Issue: Processor timeout

Symptoms: Request times out or returns partial results Cause: Complex query requiring more processing time than allowed Solution:

• Use a faster processor tier (lite or base instead of ultra)
• Simplify the research query
• Break complex queries into multiple smaller requests
• Increase timeout in script if configurable

Issue: Credits exhausted

Symptoms: "Insufficient credits" or quota error Cause: Account credits depleted Solution:

• Check balance at https://platform.parallel.ai/dashboard
• Upgrade plan or purchase additional credits
• Use lower-cost processor tiers for less critical queries
• Monitor usage to avoid unexpected depletion

Issue: Invalid response format

Symptoms: JSON parsing error or unexpected response structure Cause: API returned error or malformed response Solution:

• Check query format matches API requirements
• Retry the request (may be transient issue)
• Verify API key is valid and active
• Review API documentation for expected response format

Issue: Empty or irrelevant results

Symptoms: Research returns no results or off-topic content Cause: Query too narrow, ambiguous, or poorly structured Solution:

• Broaden the search query
• Add context to clarify query intent
• Try different phrasing or keywords
• Use Chat API first to validate query understanding

Issue: API authentication failed

Symptoms: "Invalid API key" or 401 error Cause: API key expired, invalid, or not set Solution:

• Regenerate key at https://platform.parallel.ai/settings/api-keys
• Verify PARALLEL_API_KEY is set correctly in .env
• Check for leading/trailing whitespace in key
• Ensure key has not been revoked

Issue: Rate limited

Symptoms: 429 error or "rate limit exceeded" Cause: Too many concurrent requests Solution:

• Add delays between requests
• Reduce parallel request count
• Implement exponential backoff
• Contact support for higher rate limits if needed

Resources

• references/api-guide.md - Complete API documentation
• references/basic-research.md - Simple company research
• references/vendor-selection.md - Vendor comparison workflow

Integration Patterns

Research to Report

Skills: parallel-research → content-generation Use case: Create polished reports from research findings Flow:

1. Run deep research on topic/company
2. Generate structured research output
3. Format into branded document via content-generation

FindAll to CRM

Skills: parallel-research → attio-crm Use case: Populate CRM with discovered companies Flow:

1. Use FindAll to discover companies matching criteria
2. Enrich each company with additional data
3. Create/update company records in Attio CRM

Research to Sheets

Skills: parallel-research → google-workspace Use case: Build research database in Google Sheets Flow:

1. Run FindAll or batch research on multiple entities
2. Structure results as tabular data
3. Upload to Google Sheets for team collaboration