.png)
Smarter Search. Lower Spend.
Cut infrastructure, cloud, and operational costs by deploying efficient AI-powered search and retrieval systems — without compromising performance.
Why Cost Optimization Matters
Search systems are resource-intensive.
- Bloated infrastructureBloated infrastructure
- Expensive full-text indexing
- Inefficient retrieval pipelines
- High latency = high churn
At scale, these costs compound. Our AI-native approach helps reduce them — while improving user experience.
Results We Deliver
Built on Intelligent Components
Semantic Caching
Avoid duplicate or similar queries using embedding awareness
Tiered Indexing:
Store high-priority data in fast memory; cold data in cost-efficient stores
Personalized Retrieval:
Show fewer, more relevant results — reduce server calls and client load
Token & Bandwidth Reduction:
Compress query chains and memory usage with optimized LLM prompting
What We Optimize
🔄 Retrieval Efficiency
Replace redundant full-index searches with vector and hybrid retrieval that finds relevant data faster, with fewer compute cycles.
📉 Infrastructure Footprint
Use embedding-based search that reduces dependency on large clusters and expensive query execution models.
⚡️ Faster Results, Lower Latency
Optimize for low-latency inference at the edge, reducing the need for heavy backend calls and central compute.
📊 Smarter Caching & Ranking
Only re-rank what’s needed. Use session-level caching, adaptive ranking models, and context filtering to reduce processing per query.
How It Works
01. Audit
your current search stack: engines, ranking, and infra cost
02. Deploy
hybrid retrieval with vector + sparse indexes
03. Configure
smart ranking pipelines that reduce compute per query
04. Optimize
inference, caching, and preloading for performance
05. Measure
improvements in cost per query, latency, and user engagement
News & articles
Lorem ipsum dolor sit amet consectetur. Vitae augue id eleifend vitae at urna nisi commodo eu. Ultricies hendrerit sed nisl dolor ac at vitae in rutrum ut risus.
Frequently Asked Questions
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?
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 is the ingestion engine built on?
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.
What formats and protocols are supported for ingestion?
We support Kafka, REST, Parquet, GraphQL, JDBC, flat files, and streaming event logs. You can also ingest directly from our proxy-extracted data streams.
Can I use LLMs in my pipeline?
Yes. Our architecture supports Retrieval-Augmented Generation (RAG), agentic workflows, and transformation agents using custom or embedded LLMs.
What programming languages are supported?
You can connect Search.co to your stack via Python, Node.js, Go, .NET, Ruby, and more. We offer client libraries and REST/GraphQL APIs.
Is this suitable for real-time data?
Absolutely. The pipeline is built for both batch and real-time processing with millisecond-latency for dashboards, alerts, or APIs.
Can I deploy on my own infrastructure?
Yes. The ingestion engine is containerized and deployable on Kubernetes or Docker. For proxy routing, we handle the IP infrastructure on our end.
Is there bandwidth or request throttling?
No. We offer plans with unlimited bandwidth and support high-throughput scraping across geographies and endpoints.
Does Search.co integrate with BI tools?
Yes. You can pipe clean data into Looker, Tableau, Power BI, or any SQL-based BI tool via JDBC or GraphQL.
Can I use Search.co for SEO tracking and SERP scraping?
Yes. You can monitor rankings, ads, featured snippets, and competitor content at scale using rotating residential or mobile proxies.
Is this good for brand monitoring or product scraping?
Yes. You can monitor counterfeit listings, reseller pricing, product reviews, and inventory across platforms—completely anonymously.
Does Search.co support RAG and retrieval for AI agents?
Yes. You can combine vector search with live ingestion to power agentic AI, personalized recommendations, and context-aware chat.
What industries do you serve?
We work with companies across SaaS, fintech, healthcare, e-commerce, legal, and media—basically any org that needs external data in real time.
Is the platform secure and compliant?
Yes. We follow best practices in data encryption, access control, and logging. Ingestion pipelines are deployable to HIPAA-, SOC 2-, or GDPR-compliant environments.
Will proxies leak my identity or IP?
No. All proxies are fully anonymized and support rotating headers, user agents, and advanced fingerprinting resistance.