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AI Automation & Agents

Cost-aware multi-engine AI search for GEO

A custom search and synthesis engine that combines several search APIs, live scraping and routed LLMs to deliver GEO insights, such as how a brand appears in AI answers, at a lower cost per query.

  • Sector Search technology, Digital marketing
  • Client Generative engine optimisation (GEO) company
Google, Bing and Brave results merged for each query
Multi-engine
OpenAI, Claude, Gemini and DeepSeek, routed per task
Multi-model
Answers synthesised with their sources
Cited
The starting point

The challenge

Tracking how brands appear in AI search means running many searches and model calls for every query. Paying premium rates for each query and analysis run adds up fast, and the client wanted the same quality of insight at a lower cost.

The solution

What we built

  • Built a FastAPI service that analyses each query and checks a Redis semantic cache first, so repeated and similar queries return instantly.

  • Routed searches through SerpAPI across Google, Bing and Brave, then merged and de-duplicated the results.

  • Scraped the top sources in real time with Crawl4AI, including pages that need a headless browser to render.

  • Cleaned, chunked and ranked the content by relevance, keeping source attribution for citations.

  • Routed each task to a suitable model across OpenAI, Claude, Gemini and DeepSeek through OpenRouter, with fallbacks and retries.

  • Synthesised answers across documents with cited sources, and reported GEO measures such as brand visibility, citation coverage and source ranking.

Architecture

How the system works

AI search engine architecture: query analysis with a semantic cache, multi-engine search routing, real-time scraping, content cleaning and ranking, multi-LLM routing with cost control, and response formatting with GEO analytics
Engineering

How it was engineered

  • Controlled cost at every layer: a semantic cache, task-based model selection and token-aware routing.

  • Kept the model layer LLM-agnostic, so models can be swapped without touching the pipeline.

Scope

What was delivered

01

Query analysis and semantic cache

02

Multi-engine search routing

03

Real-time scraping with Crawl4AI

04

Content ranking with attribution

05

Multi-LLM routing with cost control

06

Cited synthesis and GEO analytics

Technology

Stack

FastAPILangChainSerpAPICrawl4AIRedisOpenRouterOpenAIClaudeGeminiDeepSeek

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