About
We didn't build another AI tool. We built the control layer.
MegaLens assembles specialist AI engines from different families, makes them review independently, then an elite judge performs critical assessment, fills gaps, and delivers the final verdict. The disagreements are the product.
The problem we kept hitting
We run a digital agency. We review code, audit websites, and ship SaaS products. Like everyone, we started using AI to speed up the work — Claude for one task, ChatGPT for another, Gemini when we needed web grounding.
The problem wasn't that the answers were bad. The problem was that they were confidently incomplete. One model would miss a critical auth vulnerability that another caught immediately. A security audit would come back clean — until a different model from a different family pointed out a race condition in the payment flow.
We started running every important query through three models manually. Copy the question. Paste it into Claude. Paste it into ChatGPT. Paste it into Gemini. Then spend 30 minutes reconciling three different answers, figuring out where they agreed, where they disagreed, and which disagreement actually mattered.
That manual process caught 17 critical findings that no single model found alone across our first 5 audits. So we automated it.
The insight
“Models from the same AI family can share the same training biases. Asking one model to find its own blind spots is like asking a fish if the water is wet.”
The key wasn't more AI. It was differentAI. Engines from xAI, DeepSeek, Mistral, Moonshot, MiniMax, Alibaba, Zhipu, Perplexity — each trained differently, each with different strengths, each with different blind spots. When they independently reach the same conclusion, that's strong signal. When one sees something the others miss, that's the finding that matters most.
“Weighted dissent is the product.”
Multi-model tools that synthesize for you bury the minority opinion in a tidy summary. The outlier that might be the only correct answer? Gone. MegaLens does structured claims, conflict clusters, minority opinions, confidence-weighted voting. That's the core IP. Not summarizing — preserving what matters.
What we built
Specialist engines from different families
xAI (Grok), DeepSeek, Mistral (Devstral), Moonshot (Kimi), MiniMax, Alibaba (Qwen), and more. Different training data, different architectures, different blind spots. The roster evolves as we benchmark new engines.
Elite judges
An elite judge — Claude Opus, GPT-5.4, or Gemini depending on the task — performs critical assessment of the debate, fills gaps the specialists missed, and delivers the final verdict. Judges never debate — their only job is assessment and judgment.
Skill categories
Code, Security, Full Audit, Research, SEO, SaaS Launch, WordPress, Legal, and more. Each with optimized engine squads selected by benchmark testing.
SupremeCode audit framework
15-category deep audit (Architecture to Supply Chain) with Phase 0 system mapping, evidence-first findings, and patch-ready fixes. No padding, no invented issues.
MCP server for IDE integration
Connect directly to Claude Code, Codex, or Cursor. MegaLens reads your actual repo, detects the host AI, and routes around it. Zero duplicate perspectives.
Transparent process
You see which engines reviewed, which judges evaluated, where they agreed, where they disagreed, and what each one added. Nothing hidden behind "proprietary AI."
Built by practitioners
MegaLens is built by SERPreach — an SEO and outreach agency that has been shipping since 2009. We built MegaLens because we needed it for our own work. When we realized it caught things we kept missing, we turned it into a product.
We're not a research lab building benchmarks. We're practitioners who audit code, review security, and ship SaaS products. MegaLens is built for the same work we do every day.
Every AI has one blind spot. Different families rarely agree on the same mistake.
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