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Wide Research

Deploy parallel AI agents to research, compare, and benchmark dozens of items at once. Structured results in minutes, not days.

How it works

Step 1

Decompose

Describe what you want researched. The agent parses your request into a list of independent items and confirms a structured schema with you.

Step 2

Delegate

Sub-agents fan out in parallel — each with its own fresh context and web access. No quality degradation on item #50.

Step 3

Collect

Results stream back as sub-agents finish. Failures are noted, not fatal — the orchestrator proceeds with what succeeds.

Step 4

Synthesize

Everything is saved as a structured collection and you get a summary of key findings, patterns, and outliers.

The context overload problem

Ask a chatbot to analyze 50 companies. The first 5 get detailed write-ups. By #20, descriptions get suspiciously brief. By #50, you’re reading generic filler. This happens because traditional AI has a fixed context window — as it processes more items, previous context fills up the space and quality drops.

Wide Research solves this by giving each item its own dedicated sub-agent with fresh context, independent web access, and no knowledge of what other agents are doing. No accumulation, no degradation.

How it compares

ManualAI chatbotonroi
ApproachOne item at a timeSequential in one contextParallel multi-agent
SpeedDays to weeksHours, degrades over timeMinutes at any scale
Quality at scaleVaries with fatigueDrops after ~10 itemsConsistent across all items
Output formatNotes and bookmarksCompressed summariesStructured collection

Capabilities

  • Fresh context per item — no degradation at scale
  • Live web search for up-to-date pricing, docs, and news
  • Structured collection output you can filter and export
  • Graceful partial failures — one item failing doesn’t block the rest
  • Automatic batching for large item sets (10+ items)
  • For small sets (<5 items), researches directly without sub-agent overhead

Frequently asked questions

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