AI enablement · Applied AI in the workflow
AI built into the work, not added alongside it
We build AI that extracts information from documents, sorts requests, summarises and drafts, directly into the processes you already have. Success is measured by whether handling time falls, not by how impressive a demonstration looks.
Where this starts
A chatbot is rarely what was needed
The usual failure is not a poor AI model. It is that the AI was delivered as a separate place people have to go, alongside the system they already work in. People use it enthusiastically for a couple of weeks, then quietly stop. The AI that pays off is usually invisible. A document arrives with its details already extracted, a queue arrives already sorted, and a draft is already waiting rather than having to be requested.
What we usually find
- An AI tool in a separate tab that people forget about
- Results that still need just as much checking as the manual work did
- Confidence scores on screen, with no guidance on what to do with them
- Difficult cases passed back to the person the AI was meant to help
- No way for people to correct mistakes, so the same errors come back every week
- Nobody able to say whether handling time has actually fallen
Our position
If people have to go somewhere else to use the AI, most of the benefit is lost. The work should arrive already done.
What the work covers
Everyday, high-volume work
In these areas, the value is reliable, the accuracy can be measured, and the ways it can go wrong are understood well enough to design around.
Extracting information from documents and forms
Invoices, purchase orders, statements, claims and identity documents are turned into structured records. Anything the AI is unsure about goes to a person, rather than being filled in with a confident guess.
Sorting and routing
Emails, tickets and requests are sorted and sent to the right team as they arrive. This removes the sorting step entirely, rather than making it slightly faster.
Summaries for decisions
Long email threads, case files and reports are reduced to what the next person needs to make a decision. The source is shown alongside, so the summary can be checked rather than simply trusted.
Drafts for approval
First drafts of routine replies and documents are prepared and waiting for a person to approve or change. The person keeps the final say, because they remain accountable.
Capturing details from conversations
Notes, calls and messages are turned into the fields your main system needs. A surprising amount of manual re-typing happens here.
Making corrections easy
When someone spots a mistake, they can fix it, and that correction helps improve the system. It does not only fix the single record in front of them.
How accuracy is maintained
Clear thresholds, not hope
Every one of these tasks will sometimes be done wrongly. Almost all of the engineering is about what happens when it is.
- Confidence
- Each field has a confidence threshold, tuned against your real volumes. Below it, the item goes to a person. The threshold is set deliberately, not left at the supplier’s default.
- Review
- Items the AI is unsure about go into a queue, with the extracted value shown next to the original. Checking is genuinely faster than re-typing, so it actually gets done.
- Feedback
- Corrections are saved as examples, which is what allows the next version to improve.
- Measurement
- We continuously report how many items go through without a person, how many need correcting and how long handling takes. A decline shows up as a number before it shows up as a complaint.
- Fallback
- If the AI provider is slow or unavailable, the process follows a defined fallback. The queue keeps moving instead of stopping without anyone noticing.
What you are left with
- AI built into the system people already use
- A confidence threshold tuned against your real volumes
- A review queue that is faster than re-typing
- Corrections that help the system improve
- Continuous reporting of automation rates and handling times
Tell us which queue is the longest
That is usually where AI pays off first, and it is often not the one people suggest in the meeting.