From one working agent to a hundred across your organization.

They help decide which data the tools can use, so approval doesn’t hold up the work. Before we write code, we meet your security, legal, and IT stakeholders to confirm which data sources your policies approve and how each one can be used. We agree on the controls with them up front: access, logging, retention, and human review. We provide architecture diagrams, data-flow documentation, and answers for your security questionnaire, and we work through any AI governance process you already run. Each approval also adds to what’s cleared for use, so later builds get through review faster.

Your data stays inside your environment and under your access controls. We build on the cloud tenant and AI platform you already run, such as Google Cloud, Microsoft 365 and Copilot, or Claude Enterprise, and we work within the enterprise data terms you already have with those vendors. If a build needs a model or service you haven’t approved yet, we flag it early and route it through your standard vendor review.

Yes. Regulated data is what we built this practice for. We design each build around the obligations that apply to it, such as HIPAA for protected health information, privilege and confidentiality rules for legal work, and FERPA for student records, and we document how the build meets them. That includes least-privilege access, audit logging, and clear limits on what the AI can see and do.

We measure accuracy before anyone relies on it. Before committing to a build, we run a quick feasibility test with your data. Each build gets an evaluation set drawn from your real cases, with the answers your experts would accept, and we test against it throughout development. Answers come from your approved sources and cite where each one came from. When the sources don’t support an answer, the build says so and routes the question to a person. We monitor quality after launch to catch drift early.

We structure builds so the model can be updated or swapped without rewriting the workflow around it, and the evaluation set from development becomes a regression test. When a vendor ships an update, you rerun the tests and see whether quality held before the change reaches your users.

We build on the platform you already chose: Claude, Gemini, ChatGPT, Microsoft Copilot, or your own stack, running on Google Cloud, AWS, or Microsoft 365. We recommend Gemini and Claude for most regulated work. When a different tool fits your use case better, we’ll tell you plainly.

Yes. Most of the value comes from integration. We connect agents and workflows to the systems your people already work in, using their APIs and connectors like the Model Context Protocol (MCP). That includes EHRs through standards like SMART on FHIR, SharePoint and document management, CRMs, ticketing tools, and internal databases. If a system has no clean integration point, we tell you during scoping.

A prototype moves into a sandbox that mirrors your environment, is tested against real cases, passes the review steps your IT team defined, and is promoted through a repeatable process. Platform Activation sets up that process, and once it exists, later builds go through it more quickly.

No. People building their own agents is a good sign. The risk is that the tools stay isolated, when they are worth far more connected to your data, your systems, and each other. We give you a safe way to review, harden, and publish what your people build, so the best of it spreads and the rest is retired deliberately.

Choose a defined build when you have one clear workflow, a named owner, and a scope you can describe today. Choose an Embedded FDE Pod when you have a pipeline of builds, shifting priorities, or work nobody can fully scope at signing. A forward-deployed engineer (FDE) is a senior engineer who works inside your organization, learns your workflows firsthand, and builds alongside the people who will use the result. You can start with one defined build and move to a pod once the second and third use cases appear.

You’ll have a timeline and success measures before work starts. Defined builds are scoped to deliver a working result in weeks, not quarters. The first build in a new environment takes the longest because it sets up the deployment path and approvals. Later builds reuse what the first one put in place. We baseline the measures that matter, such as hours spent, cycle time, error rates, or cases handled, and track them after launch, so you can show leadership the result.

You own it. The code, configurations, data, and documentation are yours, delivered in your environment and your repositories. Every build ships with runbooks and a handoff where we train your people to operate, monitor, and extend it. If you want ongoing engineering support, an Embedded FDE Pod provides it on a retained basis. Either way, you don’t need us to keep it running.

Two of our founders are JD-trained, including a former litigator and general counsel at a tech company. We treat compliance requirements as part of the specification from the start.

Yes. We design for approval from the start, which lets us ship quickly and still carefully.

Josh Dayberry leads our AI Engineering practice. He spent more than a decade at Google and AWS.