Let us help you automate your workflows
Most AI engagements end with a slide deck and a list of recommendations. Ours end with agents running in your business doing the tasks that used to sit on somebody's desk until Friday.
Four steps, in this order, every time
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Audit
We map where the week actually goes, by task and person and then find the work worth handing over.
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Design
Together, we pick one or two workflows with the best return and agree exactly what the agent may and may not do.
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Build
We build the agents and wire them into your tools. No new system for your team to learn.
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Govern
Every agent action is logged on Aegis from its first action and recorded per your retention policy.
Agents
An agent is not a chatbot. A chatbot answers when you ask it something. An agent goes and does the thing: it sends the reminder, updates the record, books the slot, chases the invoice, and tells you when it needs a decision.
We build them into your existing systems. Your team does not get a new app to learn, they get a job that stops landing on their desk.
The work we start with
- Invoicing and paymentsChase what is unpaid, send the reminders, reconcile what came in.
- Intake and schedulingCapture the job, book the slot, confirm the day before.
- Customer follow-upThe check-in, the quote nudge, the review request nobody gets to.
- Records and adminUpdate job status, keep the ledger clean, stop the re-keying.
Teach the team, not the tool
Tool training goes stale in a quarter. Judgement does not. We run working sessions on your tools and your actual tasks, so people leave knowing what AI is genuinely good at, where it will confidently mislead them, and when to stop and check.
This is grounded in delivering more than eighteen university courses, which is a different discipline from giving a talk. The material is built to be taught, practised, and retained.
What a training engagement covers
- Working sessions, not slidesYour team, your tools, your live tasks.
- Where it helps and where it liesWhat AI is reliably good at, and how it fails convincingly.
- Habits that survive real workPrompting and review practices people actually keep using.
- When to involve a personHow to recognize the decisions that should never be delegated.
Find out what is worth building before you pay to build it
The most expensive AI project is the one aimed at the wrong workflow. The audit comes first for a reason: we map where the time goes, rank the workflows by what automating them is actually worth, and tell you plainly which ones to leave alone.
It stands on its own. Take the audit, act on it internally, and come back later, or not at all. That is a fine outcome.
What you get back
- Where the time goesA map of the repeating work, by task and by person.
- Ranked by what it is worthWhich workflows repay automating, and which do not.
- What each would takeAn honest estimate of effort before you commit to any of it.
- What to do firstA recommendation you can act on with or without us.
Every agent we build is accountable from its first action
Handing work to something that acts on your behalf only works if you can see what it did and undo it. That is not an add-on we sell you later. Agents we build run on Aegis, our governance platform, from day one.
Aegis is pre-general-availability and we are onboarding partners in small cohorts, so you would be coming in early, and we say that plainly rather than after you sign.
See how Aegis works →- An audit-ready record of every action
- High-risk work waits for a person before it runs
- Roll an agent back when its behavior changes
- No platform team or QA department required
Tell us what eats your week
Thirty minutes is enough to work out whether there is anything here worth doing. If there is not, we will say so.