About
We didn't build another AI tool. We built the control layer.
MegaLens assembles AI models from different families, makes them review independently, then a judge fills gaps and delivers a structured 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. Models 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
Task-specific model selection
Different scopes route to different AI models: code specialists, research and live-data models (Perplexity, Grok), legal and copy reviewers, and more. The roster evolves as we benchmark new models.
Structured findings, not summaries
MegaLens returns each finding with file, line, severity, and a proposed fix. Compressed enough that your primary IDE can read and act on them without re-reasoning through the whole analysis.
Thinking, writing, and reviewing routed separately
Planning and audit run through specialist models. Simple fixes on Claude Code and Codex CLI get handed to a cheaper model for implementation. Your strongest model always comes back for the final review.
Multi-scope audit framework
Code, security, architecture, compliance, research, copy. Different models per scope. Execution stays in your IDE. The scope is broad; the workflow stays the same.
MCP on every plan
MegaLens connects to Claude Code, Codex, Cursor, and other MCP-compatible editors. Included on every plan: Free (your own keys, fair-use), Pro, and Managed Credits. No separate add-on.
Honest boundary
Claude Code and Codex CLI get automatic fix routing. Cursor, Copilot, and others get full review findings. We only claim what actually works on each editor.
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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