Client Data Residency and Model Routing Review (Onboarding)

Client Data Residency and Model Routing Review (Onboarding)

1. 01

   Map every document type the client will route through automation before selecting a vendor

   List invoices, contracts, KYC packets, HR forms, and any regulated records separately. Each type carries a different retention and residency obligation, and a single platform decision rarely satisfies all of them.

2. 02

   Confirm where extracted text and document images are stored at rest and in transit

   Ask for the specific region of the storage bucket and the processing queue, not a general 'cloud' answer. Documenso supports self-hosting, which removes the question entirely for clients who cannot accept third-party storage.

3. 03

   Document whether client data enters a shared model training pool

   Forrester analysts argue private AI deployments outperform public tools for B2B use cases precisely because shared model access erases differentiation. Get the answer in writing per account so the finding survives staff turnover.

4. 04

   Score each workflow by autonomy level and flag any that write back to client systems

   An extraction job that returns a CSV is low risk. An agent that posts to a client CRM or sends client-facing email needs a human checkpoint before go-live.

5. 05

   Set a confidence threshold below which a human reviews the extracted record

   Instabase returns confidence scores per field. Pick a number with the client (0.90 is a common starting point for invoice line items) and route everything beneath it to a review queue rather than auto-approving.

6. 06

   Name the single person on the agency side accountable for the routing decision

   The Forward Deployed Engineer model works because one embedded lead owns implementation end to end. Split accountability across account and delivery teams and the residency review becomes nobody's job.

7. 07

   Record the review outcome in the client's account file with a review date

   Regulatory signals are moving. OpenAI pushed its IPO to 2027 citing safety concerns, and political pressure for AI oversight is rising on both sides. A dated review gives the agency a defensible position when requirements shift mid-retainer.