parallel-research

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