AI & Automation
Useful AI, connected to the
systems that run your business.
Assistants, agents, and automated workflows wired into the tools your team already uses — measured by hours saved, not by novelty.
Why automation
The repetitive work
is the expensive work
Most businesses lose more time to copying data between systems, answering the same question, and formatting the same report than to anything a strategy day would surface.
Manual steps do not scale
Work that grows linearly with volume eventually needs another hire, or it starts getting skipped.
Re-entry causes errors
Every manual copy between two systems is a chance for the two to quietly disagree.
Knowledge is scattered
The answer usually exists — in a document, a thread, or someone's head. Finding it is the actual cost.
Attention is the constraint
In a small team, removing an hour of routine work is worth more than adding another tool.
How we work
Automate the workflow, not the demo
Start with the time sink
We look for the task someone does every day, not the one that would look impressive in a screenshot.
Connected to real systems
An assistant that cannot read your data is a toy. The integration is most of the work and most of the value.
Deterministic where it matters
Plain code handles the steps that must be exact. The model handles the parts that genuinely need judgement.
A human in the loop
Anything that sends, pays, or deletes gets a review step until it has earned the right not to have one.
Failure is designed for
Retries, logging, and alerts, because an automation that fails silently is worse than no automation.
Measured in hours saved
If we cannot point at time or errors removed, the automation was not worth building.
Honest fit
AI is not
always the answer
A lot of what gets sold as AI is a scheduled script with better marketing, and plenty of problems are process problems that software will only make faster. We would rather say that than sell a model.
Automation makes sense when
- The task is repetitive, frequent, and well understood
- The data lives in systems that can be reached by API
- Someone can define what a correct result looks like
- The volume is high enough that saved minutes add up
- A wrong answer can be caught before it does damage
Hold off when
- The process changes every week and nobody agrees on it
- The underlying data is incomplete or untrusted
- The task needs accountability that cannot sit with a model
- The real fix is a process change, not software
- The main driver is that AI is expected rather than needed
Architecture
What an automation
build looks like
A typical shape. The model is one component among several — the triggers, the tools it can call, and the guardrails around it are what make it usable in production.
What we build
Assistants, agents,
and plumbing
FAQ
Questions
we get asked
Where does our data go?
Wherever you decide, and we make that explicit before building. Which provider processes what, what is retained, and what never leaves your infrastructure are decisions to make deliberately rather than discover later.
How do you stop it giving wrong answers?
By constraining the job. Grounding responses in your own content, keeping the model away from steps that must be exact, and putting a review step on anything with consequences. You reduce the risk; you do not remove it, and anyone claiming otherwise is selling something.
Do we need AI at all, or just automation?
Frequently just automation. If the rules can be written down, plain code is cheaper, faster, and predictable. We use a model where the input is genuinely unstructured or the judgement is real.
What does it cost to run?
There is an ongoing cost — API usage, hosting for the workflows, and someone maintaining it as the systems around it change. We estimate that up front, because it changes whether a project is worth doing.
Can you work with the tools we already use?
Usually, if they have an API. That is the first thing we check, because a system with no integration path tends to decide the shape of the whole project.
Related
Often part of the same project
Work with us
Have something
you want built?
Small enough to care about the details. Experienced enough to build the difficult parts. Tell us about the problem and we'll come back with a technical direction, scope, and timeline.
You work directly with the people building your product.