Our approach
This page explains the method we propose for an engagement: how we tell the difference between work that needs an agent and work that does not, how a pilot is scoped, how much independence a workflow is given, and how it is governed and measured once it runs.
This describes our proposed engagement method. It is not a description of services already running for customers, and it does not claim any vendor platform, certification or partnership.
Start here
"AI agent" gets used for three fairly different things. Telling them apart is the first practical decision in any project, because the cheapest option that actually fits is the one worth building.
If a scheduled report, a form validation rule or a shared template solves the problem, that is what we will tell you to build. There is no advantage to you in paying for reasoning where a rule would do, and a simpler design fails less often and in more predictable ways. Several of the workflows we examine end up as ordinary automation with a small amount of judgment at one specific step, rather than an agent from end to end.
Work that turns on empathy, negotiation or reading an unspoken situation belongs with people. So does anything where a mistake is expensive and hard to walk back, or where the rules are genuinely contested inside your business rather than just undocumented. If the inputs are unreliable, if no system involved offers a usable interface, or if the operating cost would exceed the value of the time released, the honest recommendation is not to build it.
How an engagement runs
Every stage ends with a concrete deliverable, and every stage is a point where stopping is a reasonable decision.
Understand the work as it is actually done, not as the process document describes it.
You get: A written workflow map with measured volumes and times, a list of exceptions, and a plain recommendation on whether to automate, simplify first, or leave it alone.
Establish whether it can be built here, and whether it is worth building.
You get: An integration assessment naming what is feasible, what needs work and what is not practical, alongside a cost comparison that keeps released capacity separate from cash.
Prove the workflow on a narrow slice before anything depends on it.
You get: A working pilot within agreed limits, its run logs, and a review against the criteria set at the start, with a decision to expand, adjust or stop.
Keep it working, and keep it honest about whether it is still worth running.
You get: A monitored workflow with a named owner, a recovery path, and a scheduled review that can conclude the workflow should be changed or switched off.
How much independence
The same workflow usually contains steps at different levels. A step earns more independence by being well understood, low consequence and easy to reverse, not by being convenient.
It proposes; a person does the work.
The workflow surfaces what it thinks should happen and why, and someone decides. This is where new or sensitive steps start, and where some stay permanently.
For example: Flagging the five overdue accounts that look worth a call this week, without contacting anyone.
It prepares the work; a person approves before it takes effect.
The output is complete enough to accept or reject quickly. The reviewer sees what was used to produce it, so approval is a real check rather than a rubber stamp.
For example: Preparing an accounting entry from a matched invoice and holding it for approval before posting.
It completes the step on its own, inside limits you agreed.
Reserved for repetitive steps with clear rules and a recoverable outcome. Routine communications can run here when the message and the circumstances are already approved. Everything is logged, and anything outside the agreed pattern stops instead of improvising.
For example: Booking a confirmed appointment into an available slot, or sending the reminder your policy already defines.
Some actions stay with people no matter how well the workflow performs. These are exceptions by design, not a limitation we expect to remove with more confidence.
When work moves to a person, it should arrive with the original request, what has already been done, exactly what it could not resolve and why, and a named owner rather than a shared queue. A handoff that forces someone to reconstruct the case from scratch has moved the work, not reduced it.
Operating boundaries
These are the decisions we would work through with you before a workflow goes live, and revisit whenever its scope changes. They are operating practice, not a product feature set.
A workflow gets access to the specific records and actions its task needs, and nothing broader. It should run under a credential that is identifiable as the workflow acting for a named owner, rather than a shared login that makes every action untraceable. Permissions are reviewed when scope changes, not left to accumulate.
Agree in advance what may be read, what may be stored, how long it is kept and what must never leave a given system. Where processing happens and which providers are involved is part of that decision. Sensitive categories can be excluded from a workflow rather than handled carefully within it.
List the systems the workflow may call and, for each one, whether it may read, write or only propose. Anything not on that list is unavailable to it. This is what keeps a wrong decision small: a workflow that cannot issue a refund cannot issue a wrong refund.
Runs are logged in enough detail to reconstruct what happened and why. Quality, exception volume and cost are watched over time, because problems tend to appear as gradual drift rather than an obvious failure. Repeating or looping behavior is a specific thing to watch for and stop.
Design for the outage and the bad day rather than assuming availability. That means knowing how the workflow is paused, what happens to work in flight, how the manual process resumes, and who is called. Recovery effort is a real cost and belongs in the business case.
Connecting to an existing system is an access question before it is a technical one: does a supported interface exist, does your licensing permit this use, whose credentials are involved, what data crosses the boundary, and what happens when it is unavailable. We report what is feasible and what is not before proposing an integration. Nothing is assumed connectable.
None of this is a one-time approval. Systems change underneath a workflow, volumes shift, rules get revised and scope tends to widen. Governance means the same questions get asked again on a schedule, with someone named to answer them.
How we measure it
Speed alone is easy to improve and easy to misread. We pair how fast work moves with whether it was done correctly and what it costs to keep running.
Is the work actually moving faster?
Is it being done correctly?
Is the exception path working, or quietly absorbing the load?
Is it worth continuing to run?
For every measure that matters, settle what is being counted, over what window, from which data source and who owns the number, before anything changes. A measurement agreed afterward tends to describe whatever the result happened to be.
When a workflow removes ten hours of monthly effort, that is ten hours of capacity. It becomes money only if it prevents overtime, avoids a hire you would otherwise have made, or is spent on work that brings revenue in. We track capacity released, cost avoided, cash actually saved and new revenue as four separate things, and we do not claim that business movement in the same period was caused by the automation. We publish no savings percentages or target figures, because we would be inventing them.
The business case itself, including the manual-work baseline, is set out on the homepage economics section.
Start with one recurring task
The fastest way to test any of this is against work you actually do. Describe one recurring task, roughly how often it happens and where it gets stuck, and we can talk about whether discovery is worth your time.