AI infrastructure

What it does for a business

Most teams do not need a research department. They need their product to call real tools reliably. They also need to trust what comes back before it reaches a customer.

That trust is an engineering problem, not a model problem. It looks like a quality gate that rejects a draft before it sends. A review layer willing to say no. A search layer that knows which of sixteen sources answered and how far to trust it.

Where data cannot leave the building, the same systems run on local hardware, which is a deployment decision rather than a different architecture.

Shipped proof

• Sixteen search engines and sixteen social platforms behind one interface, each wrapped with its own client, rate policy and failure mode, including archives, open directories and Tor. Built as an MCP server and still maintained. mega-search MCP
• A quality panel of four model judges on every outbound message, each scoring a different lens, with blinded A/B testing ranking which model writes best. Nothing sends until it passes. Outreach Campaign Engine
• Thirteen specialist agents with practitioner doctrine reviewing a research platform's output independently, on the record, with the ability to refuse. Quant Trading Platform
• Local inference offered as the default where it matters. A team with data-residency constraints gets the same tooling without sending material out.

Stack

What this solves

Agent capability without a research hire. The engineering is in the gates, the reviewers and the failure modes, which is why this is built and operated rather than advised on.