Build · Workflow Automation
Redesign repetitive workflows around people, systems, and AI.
End-to-end redesign of high-volume processes, combining automation, integration, and AI assistance, with human judgement kept where it belongs.
Violet marks an AI-assisted step. Dashed blue marks a human decision gate. Mint marks a verified, recorded outcome.
Trigger
01Work arrives
A request, document, or event enters the workflow.
AI-assisted
02Draft or classify
The model proposes an outcome with its sources and confidence.
Gate
03Human decision
Approve, edit, reject, or escalate before anything is actioned.
Threshold rules decide what must be reviewed and by whom.
Action
04System of record
The approved action is written to the owning system.
Evidence
05Recorded outcome
Decision, reviewer, inputs, and timestamp are stored.
Implementation blueprint
How this is actually built and run
01 · Trigger
A defined event: a form submitted, a document received, a record created, a threshold crossed, or a scheduled run.
02 · Context and data
Only the records and documents the workflow is approved to read, scoped to the requesting user's existing permissions.
03 · AI task
A bounded task — classify, extract, summarise, or draft — with a defined output shape and a confidence signal.
04 · Human control
A review point before any consequential action. Low-confidence cases are routed to a person rather than pushed through.
05 · System action
Writes to the system of record, routes to the right owner, or produces a draft in the tool where the work already happens.
06 · Evaluation
Tested against a representative set of real cases with an agreed accuracy bar before release, including the awkward cases.
07 · Monitoring
Volume, exception rate, override rate, and cycle time tracked, with alerting when exceptions rise.
08 · Ownership
The business owner of the process owns the workflow; changes go through your existing change process.
The problem
What this solves
Repetitive work rarely lives in one system. It lives in inboxes, spreadsheets, and manual re-keying between tools. Bolting AI onto that mess accelerates a broken process. The workflow has to be redesigned before it is automated.
When this solution fits
- Skilled people spend hours on copy-paste and re-keying
- Work stalls in shared inboxes, queues, or spreadsheets
- Volume is growing faster than headcount
- Exceptions are handled inconsistently and invisibly
- The same data is entered into more than one system
Example workflow
Document-driven intake, from arrival to system of record
01
Intake
Items arrive by email, portal, or upload and are captured in one queue with a reference.
02
Extraction
Key fields are extracted and normalised, with a confidence score against each field.
03
Validation
Items above the confidence threshold continue; anything below routes to an exception queue.
04
Human review
A person reviews exceptions, corrects fields, and their correction is logged.
05
System update
Validated records are written to the system of record with a full audit trail.
06
Feedback
Correction patterns feed the improvement backlog so the exception rate falls over time.
What is implemented
- Current-state process map including exception paths and volumes
- Future-state workflow design with explicit human checkpoints
- Automation across intake, extraction, routing, enrichment, and approval
- Integration with the systems already used to run the process
- Exception queues, audit trails, and escalation paths
- Runbooks and handover documentation for your operations team
Systems and data inputs
- Source channels: email, forms, portals, file drops
- Systems of record such as CRM, ERP, service desk, or finance systems
- Reference data used for validation and enrichment
- Access credentials and permission scopes agreed with your IT owners
- Historical examples for testing, including known edge cases
Human control
How oversight is designed in
- Confidence thresholds are set with you and can be tightened at any time
- Every low-confidence or policy-flagged item routes to a person
- Actions that create financial or customer-facing commitments require approval
- Full audit trail of automated and human actions
- A documented pause and rollback path for the automation
Tangible outputs
- A running, integrated workflow in your environment
- Exception queue with reviewer tooling
- Audit log of every automated and manual step
- Operational dashboard for volume, exceptions, and cycle time
- Process documentation and runbooks
Measurement model
How we know it is working
| Measure | How it is tracked |
|---|---|
| Cycle time per item | Median and 90th percentile from intake to completion |
| Exception rate | Share of items requiring human correction, tracked over time |
| Manual touches removed | Steps eliminated compared with the baseline process map |
| Rework rate | Items reopened or corrected after completion |
| Throughput per person | Items completed per operator per period |
Boundaries
What this solution does not promise
- We do not promise fully unattended automation for judgement-heavy work
- We do not claim zero exceptions; the goal is a visible, falling exception rate
- Extraction accuracy depends on document quality and variability
- Automation cannot fix upstream data that is missing or wrong at source
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Find the AI opportunities worth implementing in your business.
Start with a structured assessment of your workflows, systems, and data, and leave with a prioritized view of where AI can create real value.