Manual data entry
People retype the same data between two systems every day
We connect the systems directly and send a person only the cases the automation cannot read with confidence.
AI and automation
AI automation for the steps your team repeats every day
We map the work before choosing a model or platform. The useful target is a repeated step with clear inputs, a checkable output and a safe exit when the system is uncertain. That is the part we build first.
Sound familiar?
You are in the right place if
Repeated questions
Support answers the same twenty questions from documents nobody can find
An assistant answers from your own documents and shows the source, so the team can check the reply.
Vague brief
You were told to add AI and nobody said what it should do
We start with a review of the work and name the one or two steps where automation is worth the cost.
Not sure which step to automate first? Start with a short process review.
Start a projectScope
What is included
Process audit
What is worth automating, what is not, and the reason for each.
Assistants over your documents
Answers come from your knowledge base, with a link to the source.
Data from documents, mail and forms
Fields are extracted, checked and placed where your team needs them.
Pipelines between systems
Data moves between tools without anyone copying it by hand.
Human review in the loop
Uncertain results go to a person instead of straight into your systems.
Process
How the work runs
Map
We follow the current process from input to outcome and record the repeated work, waiting time and cost of mistakes.
Pick
We choose one or two steps with a clear input, a checkable output and enough frequency to justify the change.
Build
We build the automation together with the review queue, monitoring and fallback needed when the result is uncertain.
Measure
We compare the same workflow before and after launch, including run cost, human review and corrections.
Next step
Describe one process your team repeats every week. We will tell you honestly whether automation is worth it.
Start a projectResult
What you get at the end
A process map with the expected effect
Each step marked with what changes and what it takes off the team.
Working automation with a fallback
Monitoring, alerts and a manual route for when the system is unsure.
A cost model per run
What each run costs you, so the budget is known before the volume grows.
What if the model gets it wrong?
Then a person catches it. We put review exactly where an error is expensive and let the rest run unattended.
Where does our data go?
We agree on that before the first line of code, and the answer goes in writing into the contract.
Planning something like this?
Two or three sentences are enough. We will reply with the questions needed to define a useful next step.