Search.co

Accelerate AI with Retrieval-Augmented Generation (RAG)

Build smarter, more accurate LLM applications using Search.co’s expert RAG pipelines — combining AI models with real-time data retrieval for context-aware outputs.

1B+

Vectors

<100ms

Query time

13

Models

What is Retrieval-Augmented Generation (RAG)?

RAG is a framework that enhances large language models (LLMs) by connecting them to live data sources. Instead of relying solely on static training data, RAG systems retrieve relevant, up-to-date information at the time of the query — producing grounded, factual, and context-aware responses. At Search.co, we help you implement, scale, and optimize RAG systems using vector databases, embeddings, high-speed proxies, and structured retrieval workflows.

Tech Stack We Support

Vector DBs

Pinecone, Weaviate, Qdrant, Milvus

LLMs:

OpenAI, Claude, Mistral, Ollama, LangChain

Frameworks:

LangChain, LlamaIndex, Haystack

Data Sources:

Web scraping, APIs, internal knowledge bases

Use RAG For:

Internal Knowledge Assistants

Train AI on internal docs + real-time systems

Customer Support Automation

Build smarter bots with current data access

Legal & Compliance Research

Retrieve factual documents and summarize with LLMs

Healthcare & Scientific AI

Ensure generative outputs are grounded in real, peer-reviewed sources

Enterprise Search Engines

Semantic search combined with conversational AI

Why Choose Search.co for AI/RAG?

🔍 Custom Retrieval Pipelines

We design retrieval layers tailored to your domain, data types, and latency needs.

📦 Vector Database Integration

Embed and index high-dimensional data with tools like Pinecone, Weaviate, or Qdrant.

🌐 Data Collection at Scale

Use our proxies to scrape and feed accurate, real-time knowledge into your AI system.

🧠 LLM Compatibility

Our RAG stack works with OpenAI, Mistral, Cohere, Claude, and open-source models.

📈 Production-Ready Architecture

From prototype to scale, we help you launch AI tools with robust RAG workflows.

How It Works

01. Embed Your Documents

Convert unstructured text into vector embeddings stored in a vector DB.

02. Add Real-Time Retrieval

Fetch relevant results from your database or external sources via proxies or APIs.

03. RAG Pipeline Inference

Pass the retrieved data to an LLM for grounded, intelligent response generation.

04. Deploy & Iterate

Test, improve, and scale your RAG system with our support.

What they say about us

Syncs effortlessly with major programming languages

HTML

CSS

Phyton

News & articles

Oil & Gas Services Statistics Market Research Report

Gobal medical-technology (MedTech) market is estimated at ~US$668.2 billion in 2024, growing to ~US$694.7 billion

Frequently asked questions for enterprise search

Search.co is a unified platform for data extraction and ingestion. We provide high-performance proxy networks to collect data from anywhere on the web, and real-time AI-native pipelines to transform that data into actionable insights using SQL and LLM-powered logic.

Search.co is built for developers, data teams, growth marketers, AI researchers, and businesses that need structured, real-time data from external sources—without building and maintaining complex scraping or ingestion stacks.

We support a full range of proxies including residential, datacenter (IPv4 & IPv6), mobile (static & rotating), SOCKS5, and unlimited bandwidth proxies.

Yes. You can configure automatic rotation logic based on time, session, or custom rules to avoid IP bans and CAPTCHAs.

Oil & Gas Services Statistics Market Research Report

Gobal medical-technology (MedTech) market is estimated at ~US$668.2 billion in 2024, growing to ~US$694.7 billion

HealthCare/MedTech Market Research Report

Frequently asked questions for enterprise search

What is Search.co?

Search.co is a unified platform for data extraction and ingestion. We provide high-performance proxy networks to collect data from anywhere on the web, and real-time AI-native pipelines to transform that data into actionable insights using SQL and LLM-powered logic.

What is Search.co for?

Search.co is built for developers, data teams, growth marketers, AI researchers, and businesses that need structured, real-time data from external sources—without building and maintaining complex scraping or ingestion stacks.

What types of proxies do you offer?

We support a full range of proxies including residential, datacenter (IPv4 & IPv6), mobile (static & rotating), SOCKS5, and unlimited bandwidth proxies.

Can I rotate proxies automatically?

Yes. You can configure automatic rotation logic based on time, session, or custom rules to avoid IP bans and CAPTCHAs.

What is the difference between residential, datacenter, and mobile proxies?

What is the ingestion engine built on?

Frequently Asked Questions

What is Search.co?

Frequently asked questions for enterprise search

What is Search.co for?

Search.co is a unified platform for data extraction and ingestion. We provide high-performance proxy networks to collect data from anywhere on the web, and real-time AI-native pipelines to transform that data into actionable insights using SQL and LLM-powered logic.

What types of proxies do you offer?

Search.co is built for developers, data teams, growth marketers, AI researchers, and businesses that need structured, real-time data from external sources—without building and maintaining complex scraping or ingestion stacks.

Can I rotate proxies automatically?

We support a full range of proxies including residential, datacenter (IPv4 & IPv6), mobile (static & rotating), SOCKS5, and unlimited bandwidth proxies.

What is the difference between residential, datacenter, and mobile proxies?

Yes. You can configure automatic rotation logic based on time, session, or custom rules to avoid IP bans and CAPTCHAs.

What is the ingestion engine built on?

Residential Proxies use real devices with ISP-assigned IPs. Ideal for stealth scraping. ‍ Datacenter Proxies are faster and more cost-efficient but easier to detect. ‍ Mobile Proxies offer maximum trust for mobile-app scraping or anti-fraud use cases.

What formats and protocols are supported for ingestion?

Our ingestion engine uses a SQL-first approach, built with Apache Flink, GraphQL, and DataSQRL under the hood. You define transformations in SQL or the SQRL language; we handle scaling, streaming, and deployment.

Can I use LLMs in my pipeline?

We support Kafka, REST, Parquet, GraphQL, JDBC, flat files, and streaming event logs. You can also ingest directly from our proxy-extracted data streams.

Get a research stack you don't have to babysit.

Talk to the team that built it. We'll walk you through your data flow end-to-end.