The Trust Control Plane
for Enterprise AI.
Your organisation has hundreds of AI pilots. Virtually none are in production.
The reason is not technology. It is trust. Real customer data cannot reach an LLM — so your models reason over sanitised fragments, and your pilots die on the vine.
Reversible Semantic Pseudonymisation (RSP) replaces real entities with fictitious but contextually equivalent alternatives before inference, and reverses the substitution after. The LLM never sees real data. The business user never sees fictional data. The Trust Ceiling — the point at which data sensitivity prevents AI deployment — ceases to exist.
You are not blocked by AI. You are blocked by data.
Every regulated organisation runs the same programme. Build a pilot. Feed it sanitised data. Watch it underperform. Kill it. Repeat.
The bottleneck is never the model. It is the data you refuse to send it — because you cannot, and you should not. Only 16% of identified AI use cases reach full deployment. 42% of organisations abandoned most AI initiatives in 2025. The models are ready. The trust infrastructure is not.
Placeholder Masking
The model receives no names, no addresses, no monetary values. It produces generic, operationally useless responses littered with placeholders that require manual reconstruction.
Tokenisation
Replaces data with opaque identifiers. The LLM processes nonsense. It returns nonsense. You have achieved nothing except burning inference costs.
These are not privacy solutions. They are production blockers wearing a compliance badge.
¹ Based on benchmarking of placeholder-masked inputs versus raw data across summarisation, reasoning, and classification tasks. Benchmark methodology available on request during architecture review.
Redact what identifies. Preserve what matters.
Presential sits inline between your systems and the LLM. Every request passes through a deterministic transformation fabric — a runtime layer that detects and classifies sensitive entities, redacting what could identify individuals while preserving the context, values, and relationships the model needs to produce correct answers.
Transform
"Sarah Mitchell, 42 Pembroke Gardens" becomes "James Patterson, 17 Rosemary Lane." Names and addresses are replaced. Jurisdiction, transaction amounts, product types, and regulatory references are preserved — because the model needs them to reason correctly. The LLM cannot identify the customer. It can still answer the question.
Infer
The LLM reasons with full operational context. It drafts a response, flags a risk, calculates exposure, or summarises a case — working with real values where accuracy matters and redacted identifiers where privacy requires it. The output is correct, specific, and operationally complete.
Reverse
Every fictional entity maps back to the original. "James Patterson" becomes "Sarah Mitchell." The business user receives a finished document with real names, real addresses, real account numbers. Total reversal rate: 100%. Every entity is restored — 88.9% via exact-match, the remainder via contextual fuzzy matching. No placeholders. No manual reconstruction.
Built for production. Proven at scale.
Presential governs the data layer. The inference layer is your choice.
The full detection, transformation, and reversal pipeline completes in under one second. Your inference time dominates the request. Your SLAs hold.
If entity detection confidence falls below 95%, the entity is redacted rather than passed. No silent failures. No silent leakage.
Not a sidecar. Not a proxy. Presential sits in the request path. Every token is governed.
Works with any LLM — Anthropic, OpenAI, Google, Mistral, or your own fine-tuned model. The trust layer is independent of the inference layer.
The transformation fabric runs entirely on-premises. No external calls. No telemetry. Self-hosted models supported for full air-gap; cloud LLMs accessible via VPC peering.
Every transformation logged with SHA-256 verification. Every reversal traceable. Complete chain of custody for regulatory and supervisory review — EU AI Act, DORA, FCA, PRA, HIPAA, and sector-specific frameworks.
Firewalls govern your network.
Nothing governs your reasoning.
You would not connect a production database to the internet without a firewall. You would not deploy an API without authentication. Yet every AI pilot in your organisation sends sensitive, unstructured data to a third-party model with no inline control, no transformation layer, and no audit trail.
This is not a technology gap. It is an architectural omission.
Every request is transformed. Every response is reversed. Every entity is audited. Your models operate at full capability. Your data never leaves your control. The EU AI Act requires transparency and auditability for high-risk AI systems. DORA demands operational resilience. Financial conduct and prudential regulators expect evidence of supervisory-grade governance. Presential provides the infrastructure that makes this achievable — across financial services, insurance, healthcare, and any industry where AI operates on identifiable personal data.
The regulated organisations that reach production will not be the ones with the best models. They will be the ones that solved trust. And trust infrastructure does not stop at privacy — it extends to policy control, reasoning auditability, and multi-model governance. That is where we are going.