Integrations & UI
Quoted separately
Document intake and output connectors, private deployment, and workflow hooks outside the AI-core scope.
Privacy-Safe Document AI for Regulated Teams
Automate without leaking PII. Sensitive documents with patient, client, or financial data: we detect PII, mask it, and run the AI model on redacted input only. Structured output maps back where your workflow needs it, with an audit trail for compliance. Typical timeline: 2–3 weeks.
When to build custom
Privacy-safe document AI pays off when automation is blocked by legal or security sign-off, and generic cloud tools cannot pass procurement.
- Healthcare, legal, or finance teams process documents that contain personal or client-identifiable data.
- GDPR, HIPAA, or EU AI Act requirements are in scope for the automation you want to ship.
- Investor due diligence or enterprise procurement requires an audit trail for what was detected, masked, and sent to the model.
- Legal or security teams blocked a prior AI rollout because personal data would reach an external model unchanged.
What you get
PII detection, masking, and routing to the AI model without personal data in the call. Sensitive fields stay out of the model; you still get structured output for your workflow.
- PII detection. Names, IDs, account numbers, and other sensitive fields are identified on your document types before any model runs.
- Masking. Detected values are replaced with tokens. Raw personal data does not reach the model or external APIs.
- AI on redacted input. Extraction, classification, and reasoning run on masked content only. The model works on structure and context, not identifiable data.
- Restore where needed. Outputs map back to real values in your systems when the workflow requires it. Token-to-field mapping stays under your control.
- Audit trail. Logs of what was detected, masked, sent to the model, and restored when compliance or procurement needs proof.
How we deliver
Scope & estimate
Intro call: we map document types, data categories, compliance requirements, and integrations. You get a fixed-scope estimate before any paid work.
Sample documents
You share representative files under NDA. We run a short validation pass to confirm PII detection, masking, and model routing on your real formats.
Contract & payment
We agree scope, milestones, data-processing terms, and IP in the contract. Work on the AI core starts after the first milestone is paid.
Deliver AI core
We build the privacy-safe module: PII detection, masking, AI on redacted input, token restore, and audit logging on agreed scope.
Integrate if needed
Optional phase: plug the module into your document intake and output systems, or deploy in your private environment. Quoted separately when not in the initial AI-core scope.
Pricing
Fixed scope and price for the AI module, agreed before the build starts.
AI module
$5,750+
PII detection, masking, and AI on redacted input only. Token restore and audit trail on agreed scope.
1–2 weeks for a focused document set; more variety or stricter rules, longer delivery.
Final module price depends on document variety and compliance requirements. Intro call gives a range; sample documents confirm scope before contract.
FAQ
What is privacy-safe document AI?
R[AI]SING SUN builds document AI pipelines that detect PII, mask sensitive fields, and call the model on redacted input only. Personal data stays out of the model and external APIs; structured output maps back where your workflow needs it. An audit trail logs what was detected, masked, sent to the model, and restored for compliance or procurement.
How much does a privacy-safe AI module cost?
The AI module starts from €5,000, $5,750 USD, or £4,250 GBP for fixed scope agreed before the build. Final price depends on document variety and masking rules. Document intake and output connectors, private deployment, and workflow hooks outside the AI core are quoted separately. An intro call gives a range; sample documents confirm scope before contract.
How long does a privacy-safe AI module take to deliver?
Typical module delivery is 1–2 weeks after contract and first milestone payment for a focused document set and clear masking rules. Broader format variety or stricter audit requirements take longer. A sample-document validation pass on your real files usually runs for a few days before the paid build is contracted.
What is included in the AI module vs integrations?
The module price covers PII detection, masking, AI on redacted input, token restore, and audit logging on agreed scope. It does not include document intake and output connectors, private deployment, or workflow hooks unless scoped as a separate phase. See Integrations & UI on this page for optional work outside the AI core.
Is this GDPR and HIPAA ready?
We design for GDPR-aligned data minimisation: mask before the model, log processing steps, and document what leaves your boundary. For HIPAA workloads, architecture supports BAA-ready patterns with audit trails and configurable retention. Final compliance posture depends on your deployment, policies, and contracts; we align technical design to your legal requirements in scope.
How are masked values restored in output?
When your workflow needs real field values in the target system, token-to-field mapping puts them back after the model runs on redacted input. Mapping tables stay under your control. Restore steps are included in the audit trail alongside detect, mask, and model-call events.
Who owns the code after delivery?
Rights are defined in the contract before work starts: full buyout or a license for your deployment, with different pricing for each. Module pricing reflects scope fit and reuse of proven pipeline components from prior deliveries, not a greenfield six-figure build from scratch.
When is privacy-safe AI better than a generic PII scrubber?
A scrubber alone does not deliver your workflow. Privacy-safe document AI fits when you need detection, masking, model calls on redacted input, restore, and audit logging in one pipeline for regulated documents, not just anonymisation before pasting into ChatGPT. Public-domain content with no personal data usually does not need this layer.
Get in touch
Describe your documents, data categories, and compliance constraints in a couple of sentences. We'll reply with fit and next steps.
Or email [email protected]