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Service
AI automation measured in hours removed per week
We start from a stopwatch, not a model. Which task takes the most human hours, how often does it go wrong, and what would it be worth to remove it? The technology choice comes after that answer.
Resultados
- Named tasks removed from named people's weeks
- Fewer errors because fewer hands touch the data
- Volume growth without proportional headcount
- An audit trail for every automated decision
Service
Casos de uso
Inbox to system
Emailed orders and PDFs turned into structured records.
Exception handling
Automation handles the routine, people handle the unusual.
Reporting and reconciliation
The monthly spreadsheet ritual, gone.
Qué entregamos
- Process mapping and time-cost baseline
- Integration between existing systems
- Document extraction and validation
- Rules plus AI hybrid pipelines
- Monitoring, alerting and rollback
Who this is for
- Back-office teams whose day is inbox triage, copying data and chasing approvals
- Operations with a high volume of repetitive decisions that follow written rules most of the time
- Finance and admin functions with rising headcount tied directly to transaction volume
When this is the wrong choice
- Processes still being redesigned — automate a stable process, not a moving one
- Low-volume tasks where the automation costs more than the hours it saves
Problems this solves
Inbox as a work queue
Requests arrive by email and are handled in whatever order they were noticed, with no visibility of backlog or ageing.
Approval chasing
Work waits days because an approval sits unseen. Routed, escalating approvals compress that to hours.
Copying between systems
Hours per week spent moving the same values between tools, with the error rate that always implies.
Architecture and integration
- Event-driven triggers from mailbox, API or scheduler, each with retry and a visible failure state.
- Deterministic rules layer in front of any model to keep cost and variability down.
- Exception queue as a first-class interface, not a log file.
- Complete audit trail of what the automation decided and why, for compliance and for trust.
How delivery runs
- 01
Time study
We count the hours and volumes on the target process so savings can be verified later, not just claimed.
- 02
Rules before models
Deterministic rules handle what they can; AI handles only the judgement portion. That keeps the system cheap and predictable.
- 03
Shadow run
Automation runs alongside the human process and its decisions are compared before it is allowed to act.
- 04
Cutover with exception queue
Everything uncertain lands in a monitored queue with a named owner, so nothing disappears.
Realistic timeline
Weeks 1–2
Time study, process map, target measures.
Weeks 3–7
Build rules, integrations and the exception interface.
Weeks 7–10
Shadow run, comparison, supervised cutover.
What moves the price
Number of process variants
One clean path automates quickly; fourteen regional exceptions is a different project.
System access
Systems without APIs need file, email or browser-level integration, which is slower to build and to maintain.
Exception handling depth
Deciding what happens in the unusual 8% is most of the design work.
Where projects go wrong
Automating a broken process
Speeding up a bad workflow multiplies the damage. Simplify first, then automate.
No exception owner
Unowned exception queues grow until the automation is abandoned.
Savings never measured
Without the before-and-after numbers the programme loses support at the first budget review.
Presupuesto orientativo
Most automations are scoped to pay back inside 12 months.
FAQ
Preguntas frecuentes
Do we need to replace our systems first?+
No. Most automation sits between existing systems and leaves them in place.
How do you prove it worked?+
We baseline the hours before and measure them after. If the number does not move, the automation was the wrong one.
You already have the idea. Let's define what comes next.
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