AGENTIC AUTOMATIONS

AI changes how work gets done.

The biggest mistake companies make with AI is bolting it onto existing processes. The processes themselves were designed around assumptions that no longer hold: limits of human attention, information that was expensive to move, coordination that required meetings. We map your workflows, question those assumptions, and rebuild the work around what AI makes possible.

Talk to us How it works

THE THESIS

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 Most of them are outdated.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. The constraints have changed.

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

HOW IT WORKS

Map. Reimagine. Build. Secure.

Map the process and the data

We walk through the workflow as it actually runs today. Where does information come from? Where does it go? Who touches it? What decisions get made, by whom, based on what? Where does data live, and how accessible is it to agents? This isn't a theoretical exercise. 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 exist because information was hard to share? Which approvals exist because one person couldn't see the full picture? Which workarounds compensate for the fact that humans can only process so many inputs at once? 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 and implement

We build the agentic workflows that run the redesigned process. Agents that read data, synthesize, decide, and act, with human-in-the-loop at the checkpoints your team defines. Working software every week. Deployed into your environment, tested on your real data.

Assurance and security

We assess the risk surface of each workflow and build the appropriate level of governance. 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. We match the governance to the risk.

See our assurance approach →

COMPOUNDING

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. 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.

How the knowledge graph works →

REIMAGINED WORKFLOWS

What this looks like in practice.

OPERATIONS

Document ingestion and vendor management

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

The 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, not the routine.

90% less back-office overhead Global Shop Solutions →

REVENUE

Dynamic pricing and network optimization

The 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: pricing decisions require a human to weigh all the factors.

The 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; the system executes within them.

$6M/week new revenue Azul Airlines →

PROCESS

Deal-to-project handoffs

The 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.

The 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.

EDUCATION

Personalized learning at scale

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

The new process: AI tutors provide instant, personalized feedback to every student simultaneously. Writing instruction adapts to each student's level. 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 →

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

Every workflow your organization runs was designed around constraints that have changed. We help you find the assumptions, question them, and rebuild the work around what's now possible.

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