AI workflow automation.
A document arrives. Someone retypes it, checks it against a record, then decides what to do. We design bounded AI workflows around those steps, with a visible route for exceptions.
For repeatable tasks with identifiable inputs, a useful output and an operator who can review uncertain or consequential decisions.
- Document & record workflows
- Explicit action permissions
- Human review & exceptions
- Evaluation before rollout
In the scope
Choose a task with a clear finish.
Useful starting points include extracting fields from a document, drafting a response from approved records, or routing an enquiry to the right workflow. We define the output before choosing a model or building an agent.
- Name the incoming document, message or business event.
- Specify the fields, draft or decision the operator needs.
- Identify what the workflow may read, what it may change and what always needs approval.
Make the input and output contract explicit.
The workflow needs a known source of truth and a structured output that the next step can check. Missing fields and ambiguous records belong in the design, rather than being left to an open-ended prompt.
- Define accepted formats, required fields and how source references are retained.
- Validate model output against the expected structure and business rules.
- Separate a proposed change from an authorised action in the destination system.
Give exceptions somewhere to go.
An amount mismatch, duplicate reference or uncertain interpretation should reach an operator with the source and the reason for review. The workflow must make a held action visible and allow a correction to be checked again.
- Show the source, flagged check and proposed next step together.
- Define who can resolve an exception and approve a consequential action.
- Record the decision and prevent retries from creating duplicate actions.
Agree the data boundary before connecting a model.
We identify which data can leave the application, which providers receive it and which records must stay within the agreed environment. Provider terms, retention and access requirements are reviewed for the specific engagement.
- Classify sample documents and remove unnecessary personal or confidential fields.
- Name model providers, storage destinations and credential owners in the integration plan.
- Agree retention, access permissions and the review process for changing providers.
Evaluate the failure cases as well as the clean ones.
A representative sample set becomes the basis for evaluation. We check extraction or response quality, exception handling and action permissions against agreed criteria before widening the rollout.
- Include incomplete, conflicting and duplicate inputs in the sample set.
- Review proposed outputs with the person who performs the task today.
- Keep a baseline and repeat the checks when prompts, models or rules change.
Keep automation close to the work.
TripSync connects WhatsApp updates with transport records. Our manufacturing ERP engagement includes embedded assistance within its delivery scope. These are different workflows and stages; an AI feature needs its own agreed permissions, review rules and rollout checks.
- Define which records the workflow can read and change.
- Agree the exceptions that need a person’s review.
- Test the workflow against your own anonymised examples before rollout.
One workflow. A clear starting point.
Describe one repeated task, its input and the output someone needs to approve. Use anonymised sample documents when sharing examples.
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