AI agents that hold up in production.

AE Studio is an AI agent development company for enterprises, and we start by questioning the process before we automate it. We map the workflow, question its assumptions, and rebuild it around what AI makes possible, with oversight where it matters, write-back into your systems of record, and the evals that prove it behaves.

Start with an AI readiness assessment Talk to an engineer

The assessment runs about 3 weeks and ends with a ranked list of what to build.

The first workflow runs on your real data within weeks. Global Shop Solutions cut back-office overhead 90% in about 5 weeks, and Azul Airlines books $6M/week on 8+ models we run in production.

Every process encodes assumptions. Most of them are outdated.

A three-step approval chain exists because one person couldn't be trusted with the full context. A weekly status meeting exists because information was expensive to aggregate. A handoff document exists because the next team couldn't see what the previous team saw.

These were reasonable solutions to real constraints, but now the constraints have changed.

AI removes the reason some steps exist at all. The right question is what this process would look like if we designed it today, knowing what AI can do.

Most teams never ask it. They automate the process as it runs today, and every step that only existed to work around an old constraint gets automated with it. Gartner expects more than 40% of agentic AI projects to be cancelled before the end of 2027, citing unclear ROI, cost, and inadequate risk controls.

None of those three is a model problem. Each one is settled when the process is designed, before anyone writes code.

Ten years of shipping for enterprises has taught us that the projects which reach production begin with strategy. So that is where we start, and the strategy takes the form of a redesigned process, running on your real data within weeks.

Map. Reimagine. Build. Secure.

AE Studio builds agents in five steps, run in the same order every time and inside your environment. Map the process, question its assumptions, redesign it, build against evals, secure it to its risk.

Map the process and the data

We walk through your workflow as it actually runs today. Where does information come from, where does it go, and who touches it? What decisions get made, by whom, based on what, and can an agent reach the data?

We sit with the people who do the work.

Question the assumptions

Every process has assumptions baked in. Which steps exist because of limits on human attention or memory, which handoffs because information was hard to share, which approvals because one person couldn't see the full picture? We name each assumption and ask whether it still holds.

Redesign around what AI makes possible

With the assumptions identified, we work with your team to design the new version of the process. Some steps collapse. Some handoffs disappear.

Some decisions that required a meeting now happen automatically, with human oversight at the points that matter. The redesign is collaborative. Your team knows the domain, we know what AI can do.

Build against evals

We define what working means before we build, then turn that definition into evals, so working is a result the system demonstrates. Agents that read data, synthesize, decide, and act, with human-in-the-loop at the checkpoints your team defines. Nothing moves from pilot to production until it passes.

Working software every week. Deployed into your environment, tested on your real data.

Assurance and security

Evaluations, guardrails, monitoring, and access controls scoped to what the workflow actually does. Some workflows need minimal oversight, some need immutable logging and red-teaming, and we match the governance to the risk.

If your agents touch a high-risk domain under the EU AI Act, Article 73 serious-incident reporting has been enforceable since August 2, 2026. EU AI Act compliance for agents means incident detection, logs that reconstruct what happened, and a working reporting path, and the evals, logs, and checkpoints above are that evidence.

See our assurance approach →

You probably need a workflow.

Workflows and agents are both agentic systems, and we build both. A workflow runs steps that were decided in advance, sometimes with a model call choosing which branch. An AI agent decides its own next step.

Industry analysts put true agents at only about 16% of enterprise AI deployments, and a workflow is usually the right design. Agents cost more to build, evaluate, and supervise than workflows do, and paying agent prices for a workflow problem is how a budget dies in pilot. So before anything else, we tell you which one your problem needs.

Your situation	What to build	What it looks like
The steps never change	A script, with a model doing one step	Form submissions read, cleaned, and filed into your CRM
The steps are known in advance, but each case takes a different path through them	A router. One model call picks the path, then fixed steps run	A support ticket classified on arrival, then sent down one of four set paths
The steps themselves are not known in advance	An agent. The model decides what to do next at each step	A closed deal that triggers research, drafting, and setup across five systems, different for every deal

Swipe sideways to see all columns.

If you are in the first row, you may not need us. Microsoft Copilot or UiPath handles standard cases well, and buying is often the faster answer. We build the second and third rows, and telling you which one you are in is the first thing we do.

What you get.

Every AE Studio engagement leaves these running in your environment.

A plain answer on agent, routing, or workflow before you commit to any of them.
A redesigned process, designed for the constraints you have now.
An eval suite that defines working and gates the move from pilot to production, growing with your cases.
Human checkpoints on the decisions you designate, with exceptions routed to a review queue, so nothing fails silently.
Logs that reconstruct any action, with every action written back into the systems you already run.
Working software weekly, on your real data.

Here is what that produced for four organizations, one of them ours.

What this looks like in practice.

OPERATIONS: GLOBAL SHOP SOLUTIONS

Document ingestion and vendor management

Old process: Invoices arrived as PDFs, a person manually entered them into the ERP, and discrepancies were caught weeks later during reconciliation. The assumption was that a human has to read each document and type the data.

New process: Agents ingest, parse, and structure documents automatically. Anomalies (pricing drift, duplicate vendors, terms that don't match the contract) are flagged in real time. Humans review the exceptions; the routine runs itself.

90% less back-office overhead At 95% extraction accuracy, live in about 5 weeks. Global Shop Solutions →

PROCESS: AE STUDIO

Deal-to-project handoffs

Old process: When a deal closed, a PM manually created Slack channels, a project board, a kickoff document, invited the team, and scheduled the kickoff meeting. Information from the sales process lived in the salesperson's head and had to be re-communicated verbally.

New process: The handoff triggers automatically. Channels are created, boards populated with context from the deal record, the kickoff document drafts itself from the sales conversations, and the right people are notified with the context they need. The PM reviews and adjusts a complete draft.

Built for ourselves Where it runs: our own consultancy.

REVENUE: AZUL AIRLINES

Dynamic pricing and network optimization

Old process: Pricing analysts reviewed reports weekly, adjusted prices manually based on rules of thumb, and coordinated with marketing and network planning in separate meetings. The assumption was that pricing decisions require a human to weigh all the factors.

New process: ML models run continuously across thousands of route and date combinations. Pricing adjustments execute within guardrails. Network and marketing recommendations surface automatically; humans set the strategy and the guardrails, and the system executes within them.

"AE is our secret weapon."
Head of Product, Azul Airlines

$6M/week new revenue With 8+ models in production daily. Azul Airlines →

EDUCATION: ALPHA SCHOOL

Personalized learning at scale

Old process: One teacher, thirty students, the same lesson at the same pace. Students who fell behind stayed behind, and feedback on writing was delayed by days. The assumption was that personalization doesn't scale past one-on-one tutoring.

New process: AI tutors provide instant, personalized feedback to every student simultaneously. Writing instruction adapts to each student's level and assessment happens continuously, in real time. The teacher focuses on the students who need human attention.

Students in the top 1–2% nationally Alpha School →

Human checkpoints where you want them.

Human checkpoints sit exactly where your team defines them, so the agent proposes and a person decides wherever the stakes demand it. Exceptions route to a review queue, so nothing fails silently.

Evals catch regressions before your customers do, and observability watches behavior in production with real users. When something slips through, the logs reconstruct what happened and why, and the miss becomes a new eval case. Every miss becomes a test the system has to pass from then on.

The same loop is what keeps the agent from quietly eating its own return.

Cost is decided before anything gets built.

You should know what a task is worth before an agent runs it. The definition of working that AE Studio writes with you sets the balance among quality, speed, and spend per task before anything gets built.

Evals then make optimization safe. Once a system passes, we swap in smaller and cheaper models, tune for speed, and prove the quality held.

Each workflow makes the next one easier.

Over time, we build out the knowledge graph so every agentic workflow reads from and writes to a shared data foundation. The first workflow connects a few data sources and solves one problem. The second workflow benefits from the connections the first one created.

By the fifth or tenth, the foundation is rich enough that new workflows come together faster and produce better results, because they build on everything that came before.

This isn't a prerequisite. You don't need a knowledge graph to start, and the first workflow delivers value on its own. But if you choose to keep building, the compounding is real, and it's what separates a collection of automations from a system.

We know because we did it to ourselves. The deal-to-project handoff above is one of ours, and the same approach now takes on much of what used to be middle-management work at AE.

Our delivery leads carry 3x the workload, our project managers run 50% more projects at once, and each of them gets back at least 15 hours a week that went to collecting status, flagging project risk, conducting research, and routing it all upward. Those hours now go into more projects and higher-value work.

How the knowledge graph works →

Questions buyers ask.

What does it cost?

It depends on the workflow, its risk level, and the systems involved. AE Studio scopes it with you after mapping the process, and you have the full number before anything gets built.

How long until an AI agent runs in production?

The first agentic system AE Studio builds, usually a workflow, runs on your real data within weeks. Global Shop Solutions' document ingestion workflow shipped in about 5 weeks.

What is the difference between an AI agent and a workflow, and which do we need?

Both are agentic systems. A workflow runs steps decided in advance, sometimes with a model call choosing the branch, and an AI agent decides its own next step. Statistically you need a workflow, since only about 16% of enterprise deployments are true agents. AE Studio builds both and tells you which you need before you commit, even when the answer is the cheaper one.

Can you automate the process we have, or do we have to redesign it?

We can, and it is how most agent projects die. In our own work, the projects that reached production are the ones that redesigned the process first, and McKinsey sees the same pattern, with organizations seeing real returns about 3 times more likely to have redesigned their workflows. The redesign is usually smaller than teams fear, because most of it is removing steps.

Do we need an AI agent development company, or can we buy a platform?

Sometimes a platform is the right answer. A copilot rollout with no custom logic belongs on Microsoft Copilot, and one standard automation inside one app belongs on UiPath or your platform's native features; we are not the right fit for either, and we will say so. Build when the workflow is your differentiator, your access controls are unusual, or the system has to pass an audit; the readiness assessment settles which you are.

Who owns the IP?

You do. You own all the IP and code AE Studio produces for you.

Do you work with our existing stack, and what do you need from us?

Yes. We build into the systems you already run and write agent actions back into your systems of record. From your side we need someone who can grant data access and someone who owns decisions, because agents surface decisions faster than most organizations can close them, and a review queue without an owner becomes the bottleneck the agent was meant to remove.

Is this AI automation consulting or implementation, and who does the work?

Both, from one team. AE Studio does the agentic AI consulting (mapping the process, questioning its assumptions, redesigning it) and the AI agent implementation (building against evals, deploying into your environment) with the same senior engineers, and our senior data scientists inform the guardrails and evals, so there is no handoff to a junior bench. AE Studio is a product and engineering studio of about 150 people that has built custom software and AI systems for enterprises since 2016.

The process was designed for a world without AI. Redesign it.

Bring us one workflow. Pick the process where a working agent would matter most, and on the first call we will tell you whether it needs an agent, routing, or a plain workflow, and what it takes to run. If the right answer is to buy, you will hear that too.

PwC finds 56% of CEOs have seen no revenue growth or cost savings from AI. Every quarter spent on another pilot is another quarter in that number, and the first workflow takes weeks.

Start with an AI readiness assessment Talk to an engineer