Will this cost people their jobs?

That is your decision, not ours, and we will not pretend otherwise. In practice most clients redeploy people onto the exception and judgement work that was previously being squeezed in around the data entry. We help you plan that shift explicitly.

What if the automation makes a mistake?

Every automated action is logged with its inputs and is reversible. Confidence thresholds route uncertain cases to a person, and you set those thresholds — conservatively at first, then tuned as evidence accumulates.

Our system has no API. Can you still automate it?

Usually yes, via a hardened browser-automation layer with screenshot-based verification. It is less robust than an API, so we treat it as a bridge while a proper integration is negotiated.

How do you decide what to automate first?

Volume × cycle time × error rate, divided by build effort. We publish the ranked list and you choose — it is your process and your priorities.

Give the work back to the people

Intelligent Automation

Automate the repetitive, rule-bound work that consumes skilled people — with a human in the loop wherever judgement genuinely matters.

Discuss a intelligent automation project What this includes

Hours returned per month: 1 240; Median across automation programmes in their first full year.
Processing error rate: 91%; Reduction in data-entry errors after removing manual re-keying.
Payback period: 5 months; Median time for an automation programme to cover its build cost.

Our position

How we think about it

Automation fails when it is applied to a broken process; you simply get the wrong answer faster. We map the process first, remove the steps that exist only because of a historical system limitation, and then automate what remains. Exceptions route to a person with the full context in front of them, and every automated decision is logged and reversible.

What you receive

Process map with cycle time and cost per step
Automation candidate list ranked by payback period
Orchestration platform with visual run history
Exception queues with full audit context
Rollback and manual-override procedures
Savings dashboard measured against the pre-automation baseline

Capabilities

What we actually build

Business process mapping and re-design
Workflow orchestration with human approval gates
Document and invoice processing
Automated reconciliation and exception handling
Scheduled and event-driven data pipelines
System-to-system integration replacing manual re-keying
Notification, escalation and SLA tracking
Robotic process automation for systems without APIs

Technologies we use

Temporal
n8n
Node.js
Python
PostgreSQL
Redis
RabbitMQ
Playwright
Docker

Process

How a intelligent automation engagement runs

Each phase ends with something you can look at and a decision you can make.

01
Map the process
We shadow the work as it is actually done and measure cycle time, touch points and rework — not how the SOP says it happens.
2 weeks
02
Simplify first
Eliminate steps before automating them. The cheapest automation is the one you did not need to build.
1 week
03
Automate the spine
The high-volume, rule-bound path goes first, with exceptions routed to people from day one.
4–8 weeks
04
Widen the envelope
Each exception category is analysed and, where the rules are clear enough, brought into the automated path.
Ongoing
05
Prove the saving
A dashboard comparing current cycle time and cost against the baseline we measured in step one.
Ongoing

Where this applies

Questions