Integration & AI Automation
Integration bots and generative AI that feed the dashboards and remove manual consolidation. In a single outsourcing project we built more than twenty.
Talk to a specialistEvery Monday someone rebuilds the same spreadsheet.
Almost every operation has a routine like this: export from one system, export from another, paste into a spreadsheet, check, fix what does not match and email it out. It takes a few hours, happens every week, and the person doing it is almost always someone who should be analysing the result, not assembling the file.
That work does not disappear because a dashboard was built. It disappears when something starts doing it on its own and that is what this front delivers: the layer that connects the systems that never talked and feeds the dashboard without human intervention.
What we automate
From the simplest to the most elaborate. Order matters: a bot on a messy process only automates the mess.
- System integration. ERP, CRM, spreadsheet, API and legacy base starting to talk. Where an API exists we use the API; where it does not, the bot operates the interface as a user would.
- Consolidation and loading. The routine someone does by hand becomes a scheduled run, with a log of what happened and an alert when something fails instead of the failure only showing up when the dashboard wakes up empty.
- Automatic checking. Rules that check what came in and block out-of-pattern records before they contaminate the indicator.
- Applied generative AI. Where the work involves text: classifying tickets, extracting information from unstructured documents, summarising large volumes of open-ended answers. Applied to a specific task with a verifiable result, not as a general-purpose assistant.
Where this usually shows up
The processes that consume the most hours and generate the most errors in the projects we run.
- Monthly close. The consolidation that ties the team up in the first working days of every month.
- Reconciliation between systems. When the revenue figure has to match the stock figure and someone checks line by line.
- Feeding an indicator. The KPI that depends on someone remembering to update a tab every week.
- Document and ticket triage. Large volumes of text that today are read and classified by hand.
What you are left with
- The bots in production. With scheduling, execution logs and failure alerts configured.
- Documentation for each routine. What it does, where it reads from, where it writes to and what to do when it fails.
- The contingency plan. How to run it manually if the source system changes because sooner or later it does.
- Team training. To monitor the runs and tell whether the problem is the bot or the source.
How we run it
We start by mapping the manual routines and measuring how long each actually takes the number is usually higher than the estimate of whoever does it. We prioritise by return: hours saved times frequency, discounted by technical difficulty. The first bot goes live quickly and sets the standard for the rest.
How we measure results
The main indicator is manual working hours given back to the team, measured before and after. We also track the reduction in typing and consolidation errors, and the drop in closing time.
We now have a far more robust, fast and visual view of each management area’s results.
Sales director national fuel distributor
About this service
What if the source system has no API?
It still works. Where an API exists it is the preferred route because it is more stable. Where it does not, the bot operates the interface as a user would. That is more fragile to screen changes, which is why we document the contingency plan.
Isn’t automating risky if nobody checks any more?
It would be, if there were no automatic checking. Every routine we deliver has validation rules and alerts: if something arrives out of pattern or the run fails, someone is notified. The bigger risk is the current manual process, where the error only surfaces when somebody happens to notice.
Where does generative AI actually come in?
Where the work is reading and interpreting text: classifying tickets by subject, extracting fields from unstructured documents, summarising large volumes of open-ended answers. Always on a specific task with a verifiable result we do not use generative AI to calculate indicators, which is where it would fail silently.
Does this replace people?
The honest answer is that it replaces tasks, not people and the tasks it replaces are the ones nobody enjoys. On the projects we run, the practical effect was freeing whoever used to build the spreadsheet to analyse the result instead.
Who maintains the bots afterwards?
Your team, with the documentation and training that are part of the delivery. Automation requires maintenance: when the source system changes, the routine has to follow. If you would rather not build that structure in-house, we maintain it under an outsourcing contract.
How many hours does your team get back each month?
One conversation is usually enough to find the first routine that never needed to be manual.
We understand processes before recommending technology. Automation, data, artificial intelligence, and custom software for companies seeking efficiency, control, and scale.