Parallel Research

Multi-agent parallel processing for large-scale research, analysis, and production tasks

What is Parallel Research?

Parallel Research is Opulent's approach to tasks that involve processing many similar items — researching 100 companies, analyzing 50 documents, generating 30 pieces of content, or extracting data from 200 pages. Instead of a single agent working sequentially, Opulent deploys independent subagents that work simultaneously, each with its own full context window.

This architecture eliminates the quality degradation that causes most AI systems to produce generic, compressed results after the first 8–10 items.

The Context Window Problem

Traditional AI systems operate with a fixed context window. When asked to process many items sequentially:

Items 1–5: Detailed, thorough analysis with full context available
Items 10–20: Descriptions become shorter as context fills
Items 30+: Generic summaries, errors, and fabrications as earlier context is compressed

Research shows this "fabrication threshold" typically occurs around item 8–10 for most AI systems.

How Parallel Research Works

Opulent uses a fundamentally different architecture:

Task Decomposition — The orchestrating agent analyzes your request and breaks it into independent sub-tasks ("research company #1", "research company #2", etc.)
Parallel Agent Deployment — Each sub-task is assigned to a dedicated subagent with its own fresh context window
Independent Processing — Subagents work simultaneously, each conducting thorough research without competing for context space
Result Synthesis — The orchestrator collects all completed sub-tasks and assembles them into your requested format

Result: Item #200 receives the same depth of analysis as item #1, because each has its own dedicated agent and full context window.

Quick Start

Simple Request

"Research the top 20 enterprise SaaS companies and create a table
with their ARR, founding year, key differentiator, and pricing model"

Detailed Request

"Compare 100 B2B startup landing pages. For each, extract: company name,
target ICP, primary value proposition, pricing model, and trial CTA.
Organize in a sortable spreadsheet."

Creative Request

"Find 25 prominent venture capitalists. Generate a professional profile
for each including investment thesis, portfolio highlights, and contact
approach. Consistent format throughout."

Real Examples

Example 1: Researching 250 Enterprise Accounts

"Research these 250 accounts from our CRM. For each, find: recent funding,
headcount growth, tech stack, and any leadership changes in the past 6 months.
Output as a CSV ready to import back into Salesforce."

Output: Complete database with 250 detailed profiles, consistent quality across all entries.

Why This Works:

Each account gets a dedicated subagent with full context
No quality degradation from account #1 to #250
Structured output automatically formatted for CRM import

Example 2: Competitive Intelligence at Scale

"Analyze all 80 companies in this competitor list. For each, extract:
pricing page URL, pricing tiers, features per tier, free trial availability,
and enterprise contact method. Create a comparison matrix."

Output: Comprehensive competitive matrix with 80 rows, all fields populated.

Why This Works:

Parallel browsing across 80 sites simultaneously
Structured data extraction applied consistently
No AI fatigue or quality shortcuts on later items

Example 3: Lead Research at Scale

"Research these 200 prospects. For each, find: LinkedIn profile URL,
current title and company, years in role, company size, and a 1-sentence
personalization hook for outreach. Output as CSV."

Output: 200-row enriched lead list with personalization hooks.

Example 4: Batch Content Generation

"Generate 30 LinkedIn posts for our company — one for each topic in this list.
Each post should be 150–200 words, include a hook, value insight, and CTA.
Consistent brand voice throughout."

Output: 30 complete, distinct posts with consistent quality. No copy-paste between items.

Example 5: Academic Literature Review

"Review these 40 research papers on LLM reasoning. For each, extract:
methodology, key findings, sample size, limitations, and citation count.
Identify patterns and write a synthesis section."

Output: Structured literature database + a synthesized narrative across all 40 papers.

Use Cases by Category

Category	Example Tasks
Market Research	Compare 100 products, analyze competitor pricing, survey customer reviews
Academic Research	Literature review of 50 papers, compare methodologies, identify citation networks
Competitive Intelligence	Profile 50 competitors, track feature sets, monitor pricing changes
Lead Generation	Research 200 prospects, find contact info, generate personalization hooks
Content Creation	Write 30 blog outlines, generate 50 social posts, produce 20 product descriptions
Data Extraction	Scrape 100 websites, extract structured data, compile databases
Creative Production	Generate 30 custom images, edit 50 photos, create consistent brand assets
Investment Research	Analyze 40 startups, compare 30 funds, profile 50 portfolio companies
HR & Recruiting	Screen 100 resumes, research 50 candidates, benchmark 30 job descriptions

Parallel Research vs. Standard Agent

Aspect	Standard Agent	Opulent Parallel Research
Approach	Single agent, sequential processing	Parallel multi-agent orchestration
Speed	Hours until context saturation	Minutes regardless of scale
Scale	Degrades beyond 8–10 items	Scales to hundreds uniformly
Quality	Progressive degradation	Consistent quality at any scale
Output	Compressed summaries with detail loss	Complete reports and full datasets
Cost	Lower for small tasks	Optimized for large-scale tasks

When to Use Parallel Research

Perfect for:

Batch processing (50+ similar items)
Data extraction from 100+ pages or profiles
Content generation at scale (20+ distinct items)
Lead research (100+ prospects)
Competitive intelligence (30+ competitors)
Literature reviews (20+ papers)

Not ideal for:

Tasks with fewer than 10 items (use standard mode)
Real-time interactive research requiring your input between steps
Tasks with deep sequential dependencies (output of step A is input of step B)
Single deep-dive analysis (use standard agent mode)

Tips for Better Results

Be specific about structure:

❌ "Research these companies"
✅ "Create a table with columns: company name, ARR, HQ, CEO, founding year, and latest funding round"

Specify the scale upfront:

❌ "Analyze some of these companies"
✅ "Analyze all 100 companies in this spreadsheet"

Describe the desired output format:

❌ "Give me the results"
✅ "Organize in a CSV with headers matching our Salesforce import format"

Include evaluation criteria:

❌ "Compare these products"
✅ "Rate each on: pricing competitiveness, feature depth, ease of integration, and G2 rating"

Common Questions

How many items can Parallel Research handle? Tested reliably to 250+ items. Practical limits depend on task complexity and available concurrency.

How long does it take? Typically minutes for 50–100 items, regardless of per-item depth. Parallel execution means total time is roughly equal to the time for one item.

Can I refine results after? Yes. Ask for modifications: "Re-research items 40–60 with more detail" or "Add a funding date column for all rows".

Does it work for non-research tasks? Yes. Any task involving many independent items: image generation, content writing, data extraction, document analysis.

How do I trigger it? Simply describe your task at scale. Opulent automatically detects when parallel orchestration is the right approach.