About
We built a control layer for your AI tool.
MegaLens selects up to four AI models from different companies to review your code. They review on their own, then a final check reviews their findings. Where they disagree is the main thing we show you.
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 answers were not bad, but they were confidently incomplete. One model would miss something that a model from another family pointed out.
We got into the habit of running every important question through three models by hand. We would paste it into Claude, then ChatGPT, then Gemini. Then we would spend a long time reconciling the three answers: where they agreed, where they disagreed, and which disagreement actually mattered.
That manual process kept finding problems one model alone had missed. 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.”
What helped was using different AI, from different companies, such as OpenAI, Mistral, DeepSeek and Google. Each is trained differently and has different blind spots. When they reach the same conclusion independently, that is a strong signal. A finding that only one of them sees can be the one that matters most.
“Weighted dissent is the product.”
Some tools merge every answer into one summary. That can hide the one model that was right. MegaLens keeps structured claims and minority opinions. That is our core IP.
What we built
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.
Review kept separate from your IDE
Planning and audit run on MegaLens, through the models it selects. Your IDE reads the findings and decides what to fix.
Multi-scope audit framework
Code review, security audit, research, planning and diff audit run in your IDE.
MCP on every plan
Works with Claude Code, Codex CLI, Cursor, Gemini CLI and Lovable, and with any tool that supports OAuth MCP connectors, such as ChatGPT and Claude.ai. GitHub Copilot is coming soon. Included on every plan, with no separate charge.
Honest boundary
Claude Code, Codex CLI, Cursor, Gemini CLI, Lovable and Claude.ai get full review findings. ChatGPT connects the same way, through its developer mode. GitHub Copilot is coming soon. We only claim what works on each editor.
Built by practitioners
MegaLens is built by SERPreach, an SEO and outreach agency. Its founder has worked in SEO 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 are not a research lab building benchmarks. We audit code, review security, and ship SaaS products, and we built MegaLens for that same daily work.
AI models can miss issues. Another model may find what the first missed.
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