NeuralSearch is a deep-web traversal engine that crawls, links, and extracts context from across the internet — continuously, intelligently, in the background.
Pages Crawled
Context Accuracy
Deeper Than Standard
A background engine continuously crawls, prioritizing relevance, depth, and discovery — link by link, layer by layer.
You provide a topic or keyword. NeuralSearch seeds its crawler with targeted entry points — high-authority sources, academic portals, news archives.
Every page visited reveals new hyperlinks. The engine scores and queues them by contextual relevance — pruning noise, following signal.
Structured and unstructured content is extracted, parsed, and tagged — preserving relationships between entities, dates, and concepts.
Data flows into a knowledge graph — ready for AI ingestion, analysis, or export as structured JSON, CSV, or API responses.
Unlike surface-level scrapers, NeuralSearch follows multi-hop link chains — going 5, 10, even 20 layers deep into a topic's web of sources.
Understands content structure: headlines, summaries, data tables, author signals, and temporal context — not just raw text dumps.
Every page is scored for topical alignment before being processed — ensuring signal-to-noise ratio stays high across millions of documents.
Results are delivered as clean, normalized data structures — entities, relationships, timelines, and summaries ready for downstream use.
The engine runs continuously in the background — refreshing data, discovering new sources, and alerting on significant changes.
Monitor industry trends, product launches, pricing shifts, and emerging players — automatically, across thousands of sources simultaneously.
Track competitor content, hiring signals, product updates, and public sentiment. Know what your rivals are doing before they announce it.
Accelerate research by surfacing non-obvious connections between concepts, papers, and events across the entire public web.
NeuralSearch is built to be the data layer for AI-powered workflows. The structured output from each crawl can be piped directly into LLMs, vector databases, or custom analysis pipelines.
from neural_search import NeuralSearch from openai import OpenAI # Initialize crawler ns = NeuralSearch(topic="EV battery tech") ns.crawl(depth=8, sources=200) # Get structured context context = ns.get_context( format="structured", include=["entities", "timeline"] ) # Feed into LLM client = OpenAI() response = client.chat.completions.create( model="gpt-4", messages=[{ "role": "user", "content": f"Analyze: {context}" }] ) print(response.choices[0].message.content)
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