This isn't theoretical.

A global biotech had never let an outside vendor touch its data. Not once. Then enterprise AI arrived, and the science teams wanted in.

We moved fast and brought security with us. We vetted the vendor’s data and trust practices, and struck the balance between protecting their data and getting it into scientists’ hands. In 30 days, 2,000 scientists were using it.

The value showed up faster than anyone expected, including the executives. It won the company’s top innovation award several years running, and grew into a 5,000-license enterprise deal.

It didn’t stop there. They brought in more AI vendors, and we helped them build the governance to run it all: a Responsible AI program, an AI registry, ROI tracking, and build-versus-buy decisions made at the global level.

A national lab needed an AI assistant for export-control compliance, one of the most tightly governed corners of the federal world. Export controls and Department of Energy oversight, with real consequences for getting it wrong.

We built it on their own platform in eight weeks, an assistant that answers from their own documents. But the real work wasn’t the chatbot. It was the discipline underneath. We built a trusted set of answers to grade it against and a clean pipeline to prepare the data. Then we built an eval harness and measured it hard, on whether it found the right information and whether it answered correctly.

That discipline surfaced something they had to face. The AI was only as good as their data, and their data governance needed work. Fixing that counted more than any model.

Their team runs the assistant today.

Most firms hand over a slide deck or a static PDF when a project ends. We handed them a working, interactive application they could drill into, from the board down to the engineers running it day to day: what we built and how it works, with every deliverable in one place they could actually use. That close-out became a foundation to build on, and they’ve asked us back to help shape their broader AI strategy and AI security.

An aerospace defense contractor had critical work knotted up for years. The root problem was identity. Without a modern identity architecture, project after project stalled. Earlier attempts to fix it had come up short, and the work stayed stuck.

We were brought in to deploy a heavy Zero Trust networking platform, the kind that inspects everything and that most teams are afraid to fully switch on. Zero Trust means never trust, always verify. We did it inside a brutal compliance environment: controlled unclassified information, CMMC Level 2, Gov Cloud High, and FedRAMP High. Getting technology that intrusive live in a place that strict is the hard part. We did it, then ratcheted it up.

That earned us the next problem. We designed the Zero Trust strategy that had been missing. Then they asked us to build and lead the identity architecture that had been the real blocker all along.

We still run it today.

A critical infrastructure utility ran 140 offices and dozens of power transmission and distribution sites. It needed a Zero Trust plan for two very different worlds: the IT systems behind the desks, and the OT systems that keep the power flowing. The two carry different threats and different rules. Most firms treat them as one problem.

We didn’t. We built two separate strategies, each measured against the CISA Zero Trust Maturity Model: one for IT, one for OT. Each came with its own roadmap and a plain-language read the board could follow. One shared vocabulary tied them together, so leadership could compare progress across both sides of the business.

The utility runs both roadmaps today. It’s used the benchmarked structure as the basis for its next round of conversations with regulators.