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AI-Powered Search & Optimization Platform

Agent-ready commerce intelligence — brand visibility in LLM answers, structured discovery, and frictionless agent journeys.

AI-Powered Search & Optimization Platform

Overview

Project snapshot

Agent-ready commerce intelligence — brand visibility in LLM answers, structured discovery, and frictionless agent journeys.

AI-Powered Search & Optimization Platform — project overview
AI-Powered Search & Optimization Platform — challenge

Challenge

What we were solving

As AI agents browse and buy on behalf of users, traditional SEO dashboards miss silent failures: blocked agent traffic, broken checkout selectors, weak entity signals, and unknown competitor citations across ChatGPT-class surfaces. Teams lacked a single system to monitor, diagnose, and prioritize fixes with engineering-ready specs.

Our solution

How we approached it

We engineered a full-stack platform combining LLM and RAG pipelines for insight generation, vector-backed retrieval for large crawl and citation corpora, and automated agent-browser test harnesses that score discoverability and transactional flows. Dashboards track brand mentions and competitive share of voice; audits output prioritized action cards (schema, selectors, auth blocks) tied to revenue impact narratives for leadership.

AI-Powered Search & Optimization Platform — solution
AI-Powered Search & Optimization Platform — outcomes

Results

Outcomes and tech stack

  • Unified visibility into how AI systems cite and recommend the brand versus competitors
  • Technical friction audits with reproducible agent runs — not guesswork from human-only crawls
  • Structured data, entity signals, and llms.txt-style surfaces aligned to answer-engine optimization
  • Backlog-friendly outputs so engineering can ship fixes in sprint-sized increments

Tech stack

Node.jsPythonLLM APIsRAGVector databasePlaywright / agent testingMongoDBPostgreSQLAWS

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Project Type

Build with Mansoori Technologies

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