AI for Document Processing and OCR at Scale

Sending customer data to a third-party AI provider introduces the same category of privacy questions as any other data processor relationship, and answering them properly upfront avoids much bigger problems later. Sending customer data to a third-party AI provider raises the same questions as any other data processor relationship: where is the data stored, how long is it retained, and is it used to train future models.

For sensitive data, techniques like redacting personal information before sending it to an AI model, or using providers that offer contractual guarantees against training on your data, reduce exposure significantly.

We build a data flow diagram for every AI feature we implement, so clients can see exactly what leaves their systems and where it goes.

Retention policy is a detail businesses frequently overlook until it is raised in a compliance review: many AI providers retain request and response data for some period by default, sometimes for abuse monitoring purposes, which can conflict with data minimisation commitments a business has already made to its own customers. Checking and, where necessary, contractually limiting this retention is a straightforward but often-skipped step.

Redaction of personal information before it ever reaches the AI model is one of the most effective privacy controls available, and it does not require a sophisticated system: even simple pattern-based redaction of names, emails, phone numbers, and payment details before a request is sent meaningfully reduces the sensitivity of what leaves your infrastructure, while typically preserving enough context for the AI to still perform its task well.

For businesses in regulated industries, we also recommend confirming whether the AI provider offers a signed data processing agreement with specific, auditable terms rather than relying on general terms of service, since regulators and enterprise customers increasingly expect this level of documentation as standard practice.

The data flow diagram we build for every AI feature serves a dual purpose: it gives the client's compliance and legal teams a concrete artefact to review rather than an abstract description, and it forces us as the implementer to be explicit about every point where data leaves the client's own systems, which often surfaces a hop nobody had previously considered.