The work we cover
AI data readiness means having information that is usable for a particular task, with the right ownership and access. We help companies connect existing systems and resolve the gaps that prevent a workflow from operating reliably. The scope follows the business process, keeping the required data work bounded.
We inspect source records, document collections, identifiers, field definitions, permissions, freshness, and change history. Common work includes connecting a CRM and ERP, preparing an approved document collection, reconciling customer records, and building a reliable feed into an operational application. We also examine the route for approved changes back to the source system.
Different questions need different sources. A current order status needs an authoritative record with a known update time. A procedure question needs the approved policy version. A financial total needs a defined calculation over the correct population. We choose the connection and validation method around those differences.
How we deliver
We map the information required by the workflow and name the source owner for each field. Where two systems disagree, a business owner decides which record governs the task and how to resolve exceptions. Engineering implements that decision; it cannot establish the business rule by guessing from whichever value is easiest to retrieve.
We profile a representative sample before designing a broad pipeline. We examine missing records, duplicates, invalid values, inconsistent identifiers, stale updates, and changes in document format. We quantify the effect on the chosen task and distinguish a blocking issue from a tolerable exception.
The integration design specifies permitted reads and writes, update cadence, failure behaviour, and recovery. We test authentication, expired access, partial exports, rate limits, and changed fields. Repeated deliveries need duplicate protection, and a failed write needs a way to establish whether the source accepted the earlier attempt.
We keep a reconciliation path between source and destination. Counts alone are insufficient where records can be merged, deleted, or updated. We test representative field values and relationships, including the cases the application must withhold from an AI model or end user. The OAIC commercial-AI guidance provides a primary reference for reviewing personal-information handling with your advisers.
An illustrative engagement
This proposed engagement is illustrative and does not claim a client outcome. A field-service business stores customers in a CRM, jobs in a scheduling tool, and invoices in its accounting system. Staff search all three before answering a question about an incomplete visit.
We agree the identifiers connecting an account, location, job, and invoice. The first release retrieves only the fields required to prepare a service case and displays their source and update time. Unmatched records go to an exception queue rather than being silently joined on a similar name.
The workflow starts with read access. An approved later stage can create a proposed follow-up task through a bounded interface. Tests cover changed locations, cancelled jobs, duplicate customer names, and unavailable sources. The service owner decides how stale information affects the response and when staff must verify it directly.
How we measure value
We measure readiness against the task: successful record matching, required-field coverage, acceptable freshness, correct access, and the proportion of cases staff can complete. A general data-quality score is less useful than evidence that a specific defect blocks an agreed outcome.
For live integrations, we track failed updates, reconciliation differences, source delays, duplicate records, and manual recovery effort. Alerts need an accountable recipient and an action. A pipeline that reports success while dropping an important class of records has failed its business purpose.
We also compare the work saved downstream with the effort required to maintain the connection. A very frequent refresh may add cost without improving the service. A broad data cleanup may exceed the value of the initial workflow. We make those trade-offs explicit and expand only when another use justifies the work.
What your team owns
You receive source mappings, field definitions, access decisions, validation and reconciliation checks, and operating documentation. The data contract records what each source supplies, when it changes, and how consumers learn about a changed field or business definition.
We identify business and technical ownership separately. A source owner corrects the underlying record or definition; a technical owner maintains the connector and recovery process. Training includes interpreting a failed reconciliation and handling cases that need a manual decision. Engagement terms establish access, code, configuration, and ongoing maintenance responsibilities.
Questions before you start
Do we need a data warehouse first? We assess the smallest architecture that serves the actual task. A direct connection, approved export, or existing data platform may be sufficient. A broader platform needs its own reasons, owners, and operating budget.
Can you connect a legacy system without an API? We inspect supported exports, database access, vendor interfaces, and operational constraints. We agree a reliable method with the system owner. Fragile screen-based automation requires a clear maintenance and failure plan.
How clean must the data be? It must support the first scope's acceptance criteria, including known exceptions. Perfect organisation-wide data is unnecessary for every project. AI implementation uses those criteria for a production workflow; knowledge assistants apply them to approved answers and source access.