See how it works
100 seconds on governed AI - from problem to proof.
Govern the data. Govern the work. Prove both.
One governed stack, two layers.
PAMOLA, the data layer. AYITA, the runtime layer.
AI adoption doesn't stall on the model. It stalls where risk, privacy, and compliance can't approve what they can't prove.
Fraud and risk models need data, but privacy controls block access - so the models never reach production.
Sensitive records cannot move, so AI projects stall before they start - and collaboration across organizations is impossible.
Employees paste contracts into public LLMs before PII is removed - leaking sensitive data with no record of what happened.
Agents can access, send, approve, or modify data before anyone notices - and nothing in your deployment can prove the action stayed within policy.
Regulated AI does not fail because teams cannot build models. It fails because risk, privacy, and compliance teams cannot approve what they cannot prove.
100 seconds on governed AI - from problem to proof.
Realm is the governed depth layer beneath enterprise AI: it prepares and protects
the data, governs what agents may do with it inside the institution's own perimeter,
and produces evidence a reviewer can re-derive.
It makes AI usable without making your data exportable.
Data stays in your VPC, private cloud, or air-gapped environment. No external calls required.
Every response traceable to sources. Every decision logged. Compliance-ready by design.
Role-based access, policy gates, and disclosure controls - enforced before the action clears.
Contextual memory that is typed, inspectable, editable, and deletable under regulatory obligation.
PAMOLA, the data layer - it governs the data before AI uses it. AYITA, the runtime layer - it governs the work while AI acts. Realm proves both.
Governed runtime for regulated AI work
Governs regulated AI work while it runs - checking consequential actions, keeping named people in the work, and producing an audit-grade trace.
Explore AYITA →
Privacy Engineering Studio
Turns sensitive data into provably private, usable AI-ready data - with the PAMOLA Evidence Pack required to approve and release it.
Explore PAMOLA →Measured results, not vendor adjectives.
reduction in re-identification risk (63% → 0.2%)
membership-inference AUC (near random)
utility under the same protection
linkage attack success
Measured in controlled evaluation on banking-class transaction data. Methodology: the published evaluation framework.
Designed for DORA, EU AI Act, GDPR, and HIPAA requirements from day one - not retrofitted.
On-premises, private cloud, VPC, or fully air-gapped. You choose the boundary.
Fixed licensing. No per-token fees. Budget you can actually plan.
Not adjectives - artifacts. Every Realm deployment produces reviewable evidence a regulator or auditor can open.
The audit-ready artifact behind every data release - what was transformed, how privacy was attack-tested, and why the data can be released.
The decision record behind every governed action - who acted, which agent, what policy applied, and whether it was approved, blocked, or escalated.
Interested in collaborating with REALM? Submit your details, and we'll reach out shortly. You can also email us directly.
The White House - Dec 2025
New executive order establishes federal AI governance standards, creates AI Litigation Task Force to address state-level regulatory fragmentation.
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EY Global - Dec 2025
AI adoption in financial services is outpacing regulatory oversight. 16% have fully deployed agentic AI solutions, with 52% running pilot projects.
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REALM Insights
LLMs are not the system - the agent layer and memory are where decisions form. Govern the loop, not just the model.
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European Commission - Aug 2025
From August 2025, providers must comply with transparency and copyright obligations. Models posing systemic risks face additional requirements.
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REALM Research - Feb 2026
CTO Valery Khvatov and co-author Neyman publish the CVPL framework showing compliant data can still be vulnerable. Now powering PAMOLA's privacy risk assessment.
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Cogent Infotech - Jan 2026
Synthetic data is becoming an enterprise service, reducing AI data costs by up to 70% while improving privacy compliance and governance.
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