Ship product AI your buyers can approve
- with measurable controls
So Security, Privacy, and TPRM can approve your AI features and data workflows - with measurable risk controls and review-ready artifacts (not promises).
For active deals: PAMOLA Evidence Pack (2-4 weeks) accelerates procurement review.
The Deadlock
Your AI features are ready - but enterprise customers can't approve them.
"We can't approve AI without evidence"
Why: Customers need proof that sensitive data isn't exposed, re-identified, or repurposed beyond scope.
Consequence: AI features stall in security/TPRM review - or ship with constrained scope and low adoption.
Multi-tenant learning raises cross-customer risk
Why: Improving models across customers creates fears of leakage, membership/linkage risk, and unauthorized reuse.
Consequence: Vendors can't use the best signal, can't generalize, and can't scale product learning.
Per-customer deployment doesn't scale
Why: Regulated buyers require on-prem or private cloud. You have SaaS architecture - private deployment becomes a custom project every time.
Consequence: Enterprise tier becomes a bespoke delivery motion - margin collapses, deals slow down.
Realm provides the governed AI layer and evidence artifacts to move forward.
What You Get
Realm delivers both evidence artifacts for customer approvals and a governed AI layer for ongoing product integration.
For Customer Approvals
The same four canonical outputs across all Realm engagements - standardized for reviewer expectations.
| Privacy-Transformed Outputs | Share-ready or training-ready data that protects sensitive elements while preserving utility |
| Privacy / Utility Metrics | Measurable tradeoffs reviewers can evaluate (residual disclosure risk vs. value retained) |
| Attack Validation Results | Stress tests demonstrating resistance to re-identification and membership/linkage inference |
| Evidence Pack | Review-ready evidence package for security review, procurement/TPRM, and internal governance |
Outcome: Customer Security/TPRM approves your AI features and data workflows.
For Ongoing Product Integration
For vendors who need ongoing AI capabilities - not just one-time evidence.
| API-Accessible AI Backend | Integrate governed AI into your product via REST/gRPC |
| Policy-Bound Responses | Every interaction respects configured privacy and governance rules |
| Audit Trace + Privacy Score | Per-response artifacts for governance review and debugging |
| Deployment Flexibility | A unified architecture designed to reduce per-customer engineering |
Outcome: AI features with materially lower governance overhead.
Where this fits in your product:
- Support copilot / case summarization
- Detection / scoring assistance
- Knowledge workflows
Policy-bound. Runs in approved deployment profile.
Evidence Pack Structure
- Scope + data-flow summary
- Residency statement
- Run metadata
- Control mapping outline
- Approvals trail (where applicable)
AYITA Governance Features
- Decision trace for every interaction
- Privacy Score per response
- Policy-bound execution
- Reproducible for forensics and compliance
ENTERPRISE AI ARCHITECTURE
From Strategy to Governed Intelligence — Where REALM Fits
REALM Where Realm sits
How Technology Vendors Use Realm
Three scenarios where Realm helps you ship AI features and close enterprise deals.
Embed Governed AI in Your Product
The situation:
You're adding AI to your product (support copilot, analytics, detection, recommendations), but buyers block deployment without evidence that sensitive data is protected.
What Realm does:
AYITA operates as governed AI backend inside your product. PAMOLA produces privacy-transformed outputs and review artifacts for customer approvals.
Who signs off: Customer Security + TPRM + Privacy/DPO
What you get: AI features with materially lower governance overhead
Evidence: Transformed outputs + metrics + review-ready Evidence Pack
Improve Models Without Cross-Tenant Risk
The situation:
You want learning across customers (better accuracy, fewer false positives), but cross-customer training is a procurement red flag.
What Realm does:
Enables controlled, measurable privacy transformation for multi-tenant learning workflows - so you can improve models without exposing any customer's sensitive data.
Who signs off: Your Security/Privacy + Customer Security/Privacy
What you get: Better models. Scalable improvement without per-customer negotiation
Evidence: Per-tenant artifacts + validation summaries + governance docs
Private Deployment Without Custom Engineering
The situation:
Enterprise customer requires private cloud / on-prem deployment. You only have SaaS architecture.
What Realm does:
Deployment profiles (cloud, VPC, on-prem) designed to reduce per-customer custom engineering. Single architecture that can deploy in your cloud, their VPC, or fully on-prem.
Who signs off: Customer IT/Security + Your Platform Engineering
What you get: Enterprise tier without dedicated ops team per customer
Evidence: Deployment docs + residency statement + governance artifacts
How It Works
Start with a workshop - no formal partnership required.
Architecture Workshop
60-minute session to define integration pattern (API touchpoints, data flows, policy boundaries) and identify sign-off owners.
Outcome: Go/no-go + roadmap
Integration Sprint
4-6 weeks. Integrate → Configure → Validate → Package. API integration, governance config, test deployment, first customer artifacts.
Outcome: Working integration + Evidence Pack
Production Deployment
Operationalize the integration. Scale across customers with repeatable evidence generation.
Expansion: From evidence → full governed AI workflows
Deployment Profiles
A unified architecture designed to reduce per-customer engineering.
Profile A
Your Cloud
Realm runs in managed infrastructure, API-accessible from your product.
Profile B
Customer VPC
Container deployment in customer's private cloud environment.
Profile C
On-Prem
Appliance-style deployment inside customer perimeter.
Regulatory Alignment Support
Maps evidence artifacts to common governance and procurement expectations - helping teams prepare for internal approvals and external reviews.
Third-Party Risk / TPRM
Standardized evidence pack for vendor risk assessment - repeatable across customers.
Data Residency Requirements
Deployment profiles with residency documentation for jurisdiction-specific requirements.
AI Governance (EU AI Act)
Governance artifacts supporting high-risk documentation expectations.
Security Review
Control mapping + attack validation aligned with common security frameworks.
DPIA-Style Assessment
Evidence structure that supports privacy impact assessment workflows and reviewer questions.
Ship approvable AI features - at enterprise scale
Start with an Architecture Workshop. We'll assess fit, define integration pattern, and map sign-off requirements.
What you leave with: Clear integration roadmap + artifact outline for your first customer approval.
Turn "we think it's safe" into review-ready evidence your buyers can sign off.