Multilipi Multilingual GEO Optimization System
Pioneered GEO (Generative Engine Optimization) at Multilipi - the first capability of its kind in the product - enabling clients to be cited by ChatGPT, Gemini, and Perplexity.
Project Snapshot
Role
Architect + Developer
Organization
Multilipi
Timeline
July 2025 – Present
Status
Live
Deployment
Production - serving Multilipi's client base
Tech Stack
Impact & Results
- 40% cost reduction in translation pipeline via Gemini upgrade
- Clients' websites gained measurable AI search engine quotability
- New GEO capability became a differentiating product feature
- First GEO system introduced into the Multilipi product
The complete GEO (Generative Engine Optimization) stack integrated into Multilipi's platform. This system enables clients' websites to be optimally cited and quoted by AI-powered search engines like ChatGPT, Gemini, and Perplexity.
The Problem
As AI-powered search engines replaced traditional search for many queries, Multilipi's SEO-focused product needed to evolve. These AI search engines have different indexability requirements than traditional SEO, and no GEO capability existed in the product to capture this new traffic source.
The Solution
A pioneering GEO architecture providing LLM-generated structured schemas, citation-optimized multilingual content, and automated audit tools, alongside a major upgrade to the platform's core translation pipeline.
Architecture & Implementation
Schema Generation: LLM-generated Schema.org structured data (JSON-LD) for all client content, improving AI crawler indexability.
Citation-Optimized Content: Prompts structured to generate content specifically designed to be quoted by ChatGPT, Gemini, Grok, and Perplexity.
Entity Content Layers: LLM-powered FAQ sections and entity-level content generation.
GEO Audit Tool: Automated audit reports analyzing a website's GEO score and recommending improvements.
Translation Pipeline: Upgraded the machine translation pipeline from a baseline model to a Gemini-powered solution.
Data Used: Client website content, multilingual SEO/GEO metadata.
Tech Stack: Gemini, Vertex AI, Django, Python, React.js, Schema.org, JSON-LD.
Challenges & Solutions
Latency: Ensuring low-latency LLM generation across multiple languages concurrently.
Accuracy: Bypassing hallucination issues when generating highly factual schema citations.
Integration: Seamlessly integrating the new GEO architecture into the existing SaaS product.
Key Takeaways
Advanced prompt engineering and caching strategies.
Deploying highly scalable LLM infrastructure in production.
Understanding the evolving landscape of Generative Engine Optimization (GEO).