Use AI to classify, extract, summarise, draft, and route repeatable work while keeping approved data, permissions, review gates, and accountability visible.
AI workflow automation uses a model for a bounded task such as classifying an enquiry, extracting document fields or drafting a support response, then connects that output to a reviewed business process. The pilot needs representative examples, clear permissions and an agreed quality threshold before wider rollout.
Repeated entry or manual review is slowing this part of the process.
Source-field mapping, validation rules, approval gates and a reviewed set of sample records.
A workflow map and acceptance criteria agreed with the team that owns the records.
Records or customer actions need to move reliably between teams and systems.
A scoped connection with source identifiers, duplicate checks, retries and an exception queue.
Reconciliation checks and a runbook for failed or incomplete handoffs.
You want to verify the operating value before expanding access or volume.
A baseline, representative test cases, monitoring and an agreed production rollout gate.
Results, limitations, operating ownership and a prioritised next-workflow backlog.
Discovery confirms available APIs, permissions, data quality and the number of connections. The quote separates implementation from platform subscriptions, model usage, licences and support. Pilot milestones cover mapping, testing and reviewed rollout; third-party access and clean sample data are prerequisites.
Apply AI to repeatable business workflows with approved data, explicit permissions, human review, and measurable operating outcomes instead of deploying an isolated chatbot or an ungoverned model.
Use AI to classify, extract, summarise, draft, and route information while people retain control of consequential decisions.
Define what the workflow may access, what it may change, and exactly when a person must review or take over.
Measure quality, time saved, exceptions, and adoption before expanding automation into more sensitive processes.
The model is one component. We design the data, tools, evaluation, approvals, recovery, and ownership around it.
We start with a bounded task, a useful baseline, and examples of acceptable and unacceptable outputs. This keeps the project tied to operating value and makes quality testable before the workflow reaches customers or sensitive records.
The workflow receives only the context it needs and uses tools through explicit permissions. We define which actions may run automatically, which require confirmation, and how the system behaves when evidence is incomplete.
We test realistic scenarios, edge cases, incorrect inputs, unavailable integrations, and adversarial instructions. Monitoring tracks quality and operating outcomes so expansion is earned by evidence.
Identify the source record, system of record, owner, repeated work and exceptions.
Define fields, access, review gates, quality criteria and the measure of operating value.
Check duplicates, missing data, unavailable systems, retries and reconciliation on representative samples.
Release the approved workflow with monitoring, documentation and clear escalation responsibility.
A fixed-scope review of what is holding growth back, with a prioritised plan you can act on with us or on your own.
A defined scope with milestones, sign-off points and a clear launch date for new builds, redesigns and migrations.
An ongoing team for optimisation, campaigns and support, reviewed monthly against the numbers that matter to you.
We need a workflow example, source and destination systems, representative records, approximate volume and the approval rules. We assess available APIs or authorised export methods before committing to the integration approach.
The discovery review checks versions, API availability, licences, authentication, rate limits and data ownership. Some connections can use existing APIs; others need an approved export or a custom adapter. Access and third-party costs are stated in the scope.
The scope includes source identifiers, duplicate checks, retry limits and an exception queue. The responsible team can review incomplete records, reconcile totals and decide whether to retry or correct the source. Failures should be visible rather than silently skipped.
Agree a baseline for manual effort, error rate or customer delay and review representative results against acceptance criteria. We report exceptions, recurring costs and support effort as well as time saved. Expansion follows an approved operating review rather than an assumed return.
Bring a repetitive process, sample inputs, and the outcome your team needs. We will help separate a useful pilot from an expensive demo.
Share the essentials and the right TNM specialist will follow up with a useful next step.
