AI TRANSFORMATION
Reinventing how your company runs touches your data, your workflows, your tools, and eventually your org chart. It only works on AI your people can trust, so that is where we start. One team runs the loop across the company, slice after slice, and hands you the controls as fluency grows. You redesign roles and structure on evidence, at your own pace.
THE THESIS
A company transforms when the loop runs again and again: define what working means, instrument it, test against it, fix the root cause, refine against production, and expand what passes. Run it once and you get a reliable system. Run it as a sustained program and you get a different company.
Most companies attempt this as a collection of experiments: a readiness assessment here, an automation sprint there, a training workshop, maybe some data cleanup. Each one is valuable on its own. But without someone holding the full picture, the pieces don't compound. Learnings from one project don't flow into the next. Momentum stalls between engagements.
A transformation engagement holds the full picture. One team that understands your organization, builds the foundation once, and carries context across every workstream: from assessment through the data layer, the automations, and the training your teams need to run it. We've codified what this looks like across dozens of companies, and we've applied the same playbooks to our own.
The hardest calls stay yours: which roles change, how the org is structured, who does what. What we build gives you the evidence to make them well, a live view of how work actually happens, so you can redesign with data behind you and do it gradually as the work itself changes. We do this with you, and we keep doing it, because the work never stops shifting.
THE APPROACH
The method is the same loop that runs across our applied AI practice. The program is everything around it: frameworks we've developed and tested, applied to your specific situation, with the engineering team that ships the systems.
We assess your systems, data, workflows, knowledge gaps, and team capabilities. We rank the opportunities by impact, effort, and data readiness. You get a clear picture of where you are and a prioritized map of where to go.
We redesign the highest-value workflows around what AI now makes possible. As the work changes, we give leadership a clear view of how roles are actually shifting and where decision authority can move. You make the calls on structure and people, with the evidence to make them well and keep your best people through the transition.
Senior practitioners embed with your team and ship production automations from week one. We wire up data sources, build workflows, deploy intelligence systems, and document everything for your team to own and operate.
Every slice deposits reusable assets: eval suites that encode your standards, data made legible, observability that widens, templates your team can rerun. The tenth workflow costs a fraction of the first, because the program is designed to compound.
Hands-on training for your teams: how to work with agents, how to evaluate AI output, how to identify automation opportunities in their own workflows. Leadership coaching on setting standards, building advocacy, and managing through rapid change.
What works in one department informs the next. Learnings compound across your organization. We help you build the internal playbook and capability so your teams can continue the transformation without us.
HOW IT UNFOLDS
We start with a focused engagement and ship something visible fast. The foundation builds in parallel, and it's what turns those early wins into compounding value. Most transformation engagements follow a rhythm like this.
Ship the first automation into production while the foundation work begins: opportunities mapped, the first data sources connected, observability and assurance set up from day one. Something visible in weeks, with the business case for going further built from real results.
More workflows redesigned and shipped, while the data layer and observability fill out and assurance coverage widens. Teams gain the fluency and tools to run what's live and start extending it themselves. 2–4 production automations running, with a growing backlog.
The intelligence layer starts generating its own insights. Cross-department patterns emerge. Observability now spans your people and your agents, so quality holds as more work shifts to AI. Expansion to additional departments based on what's working, with internal teams taking over more of the day-to-day.
This is the plan from day one. Your team runs the workflows, extends the automations, and owns the roadmap. We stay on the durable layer, the assurance, monitoring, and evaluations that keep the system trustworthy as models and risks change, and we step back in when the next reinvention is worth making.
HOW IT'S PRICED
Every phase is scoped and priced before work begins, and the evals we define together are the acceptance criteria: a phase is done when the system passes the standard in production. The standard itself evolves: each phase pins the version it will be judged against, and what production teaches rolls into the next. The price of a phase tracks the strictness of the standard: the higher the stakes, the stricter the evals. The observability we build is also how you audit us. You see what changed and what it cost.
Engagements typically begin with a fixed-price reliability diagnostic, and every phase quote after it comes from the diagnostic's findings, so you commit to numbers built on evidence about your own systems and data.
The sustained program runs as a retainer that carries the continuous work: strategy, observability, assurance upkeep, and training, with each slice quoted as a fixed phase inside it. As your team takes the controls, the engagement narrows by design to the durable layer: the assurance, monitoring, and intelligence that keep the system trustworthy, for as long as you want us watching.
STAYING TRUSTWORTHY
The observability layer we build does two jobs at once. It shows leadership how work actually happens, the evidence behind every org decision. And it watches quality across both your people and your agents, so standards hold as more of the work becomes autonomous. One layer, strategic visibility and assurance together.
This is a continuous practice. As autonomy grows, the risk surface changes, so the evaluations, guardrails, and monitoring stay current with what your systems can do. It runs on the same frontier alignment research that informs everything we build.
Most companies discover this layer late. Here it is in from week one, and it is the part of the engagement that stays.
See how assurance works →WHAT MAKES THIS DIFFERENT
This engagement produces both the organizational clarity and the shipped systems, from one team. The people who map the opportunity build the first version and stay to operate it with you.
AE runs intelligence loops across our own projects, deals, and people. We've hit the edge cases, debugged the integration quirks, and learned which approaches work at scale. Every improvement we make for a client improves the system for everyone, including us.
The intelligence layer, the automations, the documentation, the playbooks as applied to your company. We build it for you to run. The goal is to make your organization capable, then step back. Some AE frameworks inform the architecture (we bring background expertise from years of doing this), but there's no dependency. You keep it all.
AE has been building production AI since 2016. ~150 senior professionals. Frontier alignment research alongside client work, with collaborators including DARPA and Anthropic. We understand the models at a level that informs every recommendation and every system we build.
WHERE TO START
LET'S TALK
We'll bring the team that ships.
