Supervised AI agents, one workflow at a time
Domos Digital starts with a workflow you already run by hand. We map how it actually gets done, count the hours it consumes, price those hours the way your finance team would, and only then propose an AI agent that operates inside limits you set.
For U.S. businesses. Start with a workflow, not a company-wide AI overhaul.
Agentic solutions / measurable work
Domos Digital is an agentic solutions company serving businesses in the United States. We identify repetitive, time-consuming work, quantify what it costs in staff hours and fully loaded compensation, and design supervised AI agents that carry the routine steps while people keep authority over judgment calls, exceptions and consequential actions.
The engagement
A practical path from repetitive work to a supervised agent.
We sit with the people doing the task and trace it from trigger to finished outcome, including the branches, waiting periods, workarounds and undocumented steps nobody wrote down. What we are looking for is repetitive, rule-driven work whose inputs already live in your systems, not a convenient place to put AI.
Together we count how often the task runs and how long each pass takes, then apply a fully loaded hourly cost: compensation plus payroll taxes, benefits and the overhead behind that role, rather than base salary alone. The result is a manual-work baseline you can defend internally. Sometimes the measurement shows the task is not worth automating, and that is a useful answer too.
We propose a deliberately narrow first agent: one defined trigger, a named set of systems, explicit permissions, an approval point and a written definition of a good result. You sign off on the scope, the data it may touch and the actions it may take unattended before anything is built. The pilot is then judged against the baseline you validated.
After launch we track completion volume, correction and escalation rates, review time and the hours actually released. Exceptions route to a person by design. We tune, expand or retire the agent based on what the numbers show. The aim is to give your team its hours back for work that needs judgment, relationships and initiative, not to build a case for cutting jobs.
Where to look first
These are discovery candidates, not off-the-shelf integration promises. We assess your systems, data and risk before proposing an agent.
Requests land in a shared inbox, a web form and three people’s email, and someone reads each one to decide where it belongs. An agent could classify incoming requests, capture the details as a structured record and route them by your rules, flagging anything ambiguous or high-stakes for a person instead of guessing.
Invoices, order forms, applications and signed PDFs get retyped into a system of record. An agent could extract the fields, check them against records you already hold and stage the entry for approval. Confidence thresholds, validation rules and what happens on a mismatch are decided during scoping.
Someone loses the last two days of every month pulling the same exports, reconciling them and rebuilding the same spreadsheet or deck. An agent could assemble that recurring draft on schedule, so the analyst spends the time interpreting and deciding rather than collecting.
Quotes waiting on a reply, documents still outstanding, renewals approaching, tickets sitting idle. An agent could watch for the condition, prepare the follow-up and log the activity. Anything customer-facing goes out only through the review step you define.
Staff interrupt each other to find the current policy, the right form, the pricing exception or the answer buried in a shared drive. An agent could respond from a defined, approved set of internal sources and show where each answer came from, so the person asking can verify it before acting.
The same customer, order or asset exists in several systems and the records drift apart. An agent could compare them on a schedule, surface mismatches with the supporting evidence, apply only the corrections you pre-approved and leave genuinely ambiguous cases for a human decision.
Make the economics visible
The business case starts on your side of the ledger. Take the hours a task consumes in a month and multiply them by a fully loaded hourly cost: compensation plus payroll taxes, benefits and the overhead required to keep that role working. That product is the manual-work baseline. Read it as a measurement of current spend, not as savings waiting to be collected, because a salary line does not disappear the moment a task is automated.
Monthly task hours × fully loaded hourly cost = manual-work cost baseline
Every proposal shows both sides of the calculation. If the numbers do not clear your baseline once implementation, usage and human review are counted, we will tell you the task is not a good candidate yet.
In a real engagement these inputs come from your measured hours and the loaded rate your finance team already uses. The proposal then subtracts the honest costs on our side of the comparison: implementation, platform and model usage, the time your people spend reviewing output, and the share of the work an agent will not take. Hours released are capacity, not automatically cash. What that capacity becomes — less overtime, faster turnaround, or the work you keep deferring — is your decision. Our objective is to propose an agent that runs for a fraction of the validated baseline; whether it can is something the measurement determines, not something we promise in advance.
Illustrative assumptions only. Not a quote, promised savings or a customer result.
Built around your boundaries
An agent receives access to the specific systems, records and actions its job requires, and nothing beyond that. Before it runs we document what it can read, what it can write and where its data travels. Those boundaries are agreed with you as part of the design and revisited whenever the scope changes.
Consequential actions — money moving, customer-facing messages, changes that are hard to reverse — pass through a person, and so does anything the agent cannot handle confidently. We define the approval points and the escalation path with you, and the agent stops and asks rather than improvising.
Each agent ships with the measures that show whether it is working: volume handled, exception rate, correction rate, review time and hours actually released. Runs are logged so a person can reconstruct what happened. If performance drifts, you see it in your own numbers rather than hearing it from a customer.
Before you start
Software that carries a defined task through to completion instead of only answering a question. You give it a trigger, access to specific systems and rules about what it may do unattended. It reads the inputs, chooses among the options you allowed, takes the permitted action, and hands anything outside its scope to a person. Think of a diligent operator with a narrow job description rather than a chatbot.
Tasks that repeat often, follow rules you can write down, draw on information that already sits in a system, and have a clear definition of done. Work that is rare, heavily negotiated, or dependent on relationships and judgment stays with people. Volume matters as much as annoyance: something done twice a year rarely justifies the build, however tedious it is.
We do not publish a rate card, because scope, systems and risk vary too much to price generically. What comes first is the manual-work baseline, established with you from measured hours and your fully loaded hourly cost. The proposal then sets implementation, platform and model usage, and ongoing human review against that baseline, so you are comparing two grounded numbers before committing. If it does not clear, we say so.
That is an assessment question rather than a yes. Many business systems offer an API, a scheduled export or a supported integration path, and where they do not there are often other routes, but licensing, permissions, data residency and your own security review all shape what is genuinely possible. We examine your specific systems during discovery and report what is feasible, what needs work and what is not practical. We do not claim compatibility with a named product before we have looked at yours.
Your people supervise it. Agents operate inside the permissions you grant, escalate exceptions to named owners and log their runs so any outcome can be traced. Data handling — which systems are in scope, where processing happens, what is retained and for how long — is written down before implementation and shaped by your policies and any regulatory obligations you carry. None of this transfers accountability for your data away from you; it makes the boundaries explicit.
Start with one recurring task
You don’t need an AI strategy to begin. You need one recurring workflow, an honest count of the hours it consumes, and someone willing to test whether an agent can carry it for less. Describe the task in a short inquiry and we’ll follow up to arrange a discovery conversation.
Domos Tools
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