Competitive Intelligence Platform

Our client operates in one of the fastest-moving corners of technology: the AI product market itself. Staying ahead meant knowing what every competitor shipped, priced, and positioned the moment it changed. Their team was doing that by hand, and the market was simply moving faster than people could read.

Competitive tracking had outgrown manual research:

• New AI products and feature releases landing almost weekly
• Competitor information scattered across websites, blogs, docs, and release notes
• Manual research producing inconsistent, quickly outdated insight
• No standard framework to compare rivals on pricing, features, security, and positioning
• Scores would only be trusted if every number could be traced back to the source evidence behind it

VentureSEA designed and built an LLM-powered Competitive Intelligence Platform, configurable and explainable by design.

• Built automated acquisition pipelines that continuously crawl competitor sites, product pages, blogs, and public docs, with change detection so re-analysis runs only on content that actually moved rather than re-reading the whole web every night
• Used GPT-4o and Claude to extract, summarise, and contextualise findings into structured records, each carrying the source URL and captured text so every claim is traceable to its evidence
• Created a flexible comparison framework so teams define and adjust their own evaluation dimensions as the market shifts, without waiting on an engineering change
• Scored competitors with a transparent, criteria-based algorithm and stored results in pgvector for semantic recall, keeping the scoring logic inspectable rather than buried in a prompt
• Visualised everything through Next.js dashboards and spider charts for instant side-by-side comparison
• Built an evaluation harness with curated golden datasets, so every prompt or model change is regression-tested for scoring quality before release, and model upgrades cannot silently reshuffle the rankings

After launch we tuned the scoring logic and improved output consistency and relevance from real stakeholder feedback, using the golden datasets to prove each change was an improvement rather than a different opinion.