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Service
Automation that removes the copy-paste work.
AI assistants grounded in your own content, document and invoice processing, and workflow automation between the tools you already pay for. We start with the task that wastes the most hours, not the one that demonstrates best in a meeting.
What is included.
Automation work is mostly plumbing and judgement. Here is the whole list.
Process audit and automation shortlist
AI assistants grounded in your content
Customer-facing chatbots
Document and invoice processing
Workflow automation between tools
API integrations and data sync
Human review and approval steps
Monitoring, logging and cost control
How we approach it.
Start with the hours, not the hype
We audit where time actually goes and rank tasks by hours saved against effort to automate. Some of the answers turn out not to involve AI at all, and we will tell you when that is the case.
Ground it in your own data
An assistant that answers from your documents and systems and shows its sources, rather than one that produces a confident answer nobody can check.
Keep a person in the loop where it matters
Anything touching money, contracts or customers gets an approval step. Automation should reduce the work, not the oversight.
What you actually end up with.
Automation projects fail quietly when nobody measures them. These three stop that.
A shortlist with numbers on it
Hours per week saved per task, and what each would cost to automate, so the decision is yours to make rather than ours to sell.
Something in production in weeks
One workflow live and measured before anyone talks about the next five. If it does not save the hours we predicted, we say so.
Running costs you can see
Token and API spend logged and capped, so the bill does not surprise you in month three and you can decide what is worth running.
Common questions.
Is our data used to train someone else's model?
Not on the setups we build. We use API tiers where inputs are not used for training, and for sensitive work we can keep processing inside infrastructure you control. We will tell you exactly which provider sees what before anything is connected, in writing.
What happens when the AI gets something wrong?
It will, occasionally, which is why the design matters more than the model. Answers cite their sources so they can be checked, confidence thresholds route uncertain cases to a person, and anything consequential needs approval before it goes out. We design for the failure case first.
Do we actually need AI for this?
Often not. A scheduled script, a better form or a fixed integration solves a good share of what people arrive asking AI for, at a fraction of the cost and with none of the uncertainty. We would rather build you the boring version that works than the impressive version that needs watching.
Tell us which task wastes the most time.
Describe the process and who does it today. That is usually enough for us to say whether it is worth automating at all.
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