Use the AI tools your compliance team has been blocking.
Your external AI adoption is blocked by compliance. Your internal datasets sit unused because safe anonymisation destroys their utility. Presential transforms PII into contextually equivalent fiction at the infrastructure boundary, then reverses it on return. The output comes back accurate. Your data never leaves.
The model is not the bottleneck. Your data pipeline is.
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 because you should not.
of regulated AI pilots stall before production, blocked by data governance, not model capability.
of banking executives name data privacy as their top GenAI barrier (EY, 2024).
Presential RSP versus traditional masking. Tested across 500,000 regulated cases and nine foundation models.
Masking kills accuracy. The data proves it.
Every regulated organisation tries masking first. Replace names with [NAME-1], addresses with [ADDRESS-1], amounts with [AMOUNT-1]. The model receives placeholders. It returns unusable output. The project stalls within a week. The bottleneck is not the model. Masking destroys the context the model needs to reason. Presential preserves it.
Data In, Usefulness Out
The model receives no names, no addresses, no monetary values. It produces generic, operationally useless responses littered with placeholders that require manual reconstruction.
Opaque Identifiers, Zero Utility
The LLM processes nonsense and returns nonsense. You have burned inference costs and learned nothing.
Real Context, Safe Data
Identifying entities become fictitious but realistic. The model reasons on full context. Every entity reverses on return.
Brittle Rules, Perpetual Maintenance
Pattern-matching catches the obvious cases and misses everything else. Contextual references, co-references, novel formats. Every new data source means new rules. You are building a maintenance liability, not infrastructure.
Deploys on-premises or in your VPC. Your data is transformed before it crosses any network boundary. The LLM only ever sees pseudonymised content.
RSP replaces PII with consistent surrogates on the way out, then reverses them on the way back — full utility, no leakage.
customer environment
external
outbound · raw
inbound · restored
RSP edge
pseudonymised
response · raw
See it work
500K test cases. Nine models. Full methodology.
Summarisation Accuracy
BLEU / ROUGE-L composite · 500 financial services test cases · methodology on request
System Performance
Fail-closed: entities below confidence threshold are redacted, never passed through
What this means for your team
Complaint summarisation
A model summarising a financial complaint needs names, amounts, dates, and account references to produce useful output. Placeholder masking strips all of these. Presential replaces them with fictitious equivalents. The summary reads naturally. Every entity reverses on return. Your team gets actionable output, not a redacted mess.
Claims triage
An LLM triaging an insurance claim needs claimant details, policy context, and incident specifics. Tokenisation replaces these with opaque IDs and produces garbage output. Presential preserves the full semantic structure. The model triages accurately. Your claims team ships faster.
Clinical note analysis
A model extracting insights from clinical notes needs patient context to identify relevant findings. Presential pseudonymises patient identifiers while preserving medical context, diagnostic codes, and temporal relationships. Clinicians get useful AI output without exposing patient data.
Fine-tuning on regulated data
A bank, insurer, or professional services firm wants to fine-tune a model on real client files, case records, or transaction histories. Redaction destroys the signal the model is meant to learn from. Training on raw data never clears compliance. Presential produces a fully pseudonymised training corpus that preserves semantic structure, relationships, and edge cases. The model learns what it needs to learn. The training data never contains a real client name, account, or identifier.
Agentic workflows and tool use
An agent retrieving data from a CRM, a core banking system, or a claims platform pulls live PII with every tool call. Redacting at the boundary breaks the agent’s reasoning. Leaving the data intact breaks compliance. Presential transforms every tool output on entry and reverses every agent action on exit. The agent reasons on full context. The underlying systems only ever transact with pseudonymised content.
Activating dormant data assets
Many enterprises hold aggregated customer data they have the contractual right to use and do not. The cost of safely anonymising the aggregated dataset often exceeds the value of activating it, so the asset sits dormant. Presential pseudonymises at scale with semantic structure preserved, which means a latent dataset your business has already paid for can finally be used for the analytics, insights, or product development it was intended to enable.
In 2024, a single breach in financial services averaged 4.45 million dollars in direct costs (IBM Cost of a Data Breach Report). For a UK-regulated firm, ICO enforcement can reach 17.5 million pounds or 4% of global turnover. Both figures assume data left the network. Presential removes that assumption.
The EU AI Act and DORA are live. Data governance is no longer optional.
The regulatory window for informal AI experimentation has closed. EU AI Act high-risk requirements are in force. DORA operational resilience mandates are live across UK and EU financial services. The FCA’s AI guidance names third-party inference risk as an active compliance consideration. Your compliance team is reading the rules correctly. The question is whether you build LLM data governance from scratch or deploy infrastructure that already meets the standard.
DORA operational resilience mandates live (UK and EU)
EU AI Act: prohibited AI practices provisions apply
EU AI Act: full high-risk system requirements in force
What your CISO will ask. Already answered.
Data Never Leaves Your Network
Transformation happens on-premises or in your VPC. Only the fiction reaches the LLM provider. You control the perimeter.
45 to 90ms. Your SLAs Hold.
The transformation layer adds negligible overhead. Inference time dominates. Your latency commitments stay unchanged.
Fail-Closed by Design
If the system cannot classify an entity with high confidence, it redacts rather than passes. No silent failures. No silent leakage.
Every Transformation Is Logged
SHA-256 verification. Full audit trail. Designed for EU AI Act, DORA, FCA/PRA, and sector supervisory reviews. Your compliance team can verify every operation.
Switch Models Without Rewriting Your Governance
Works with Anthropic Claude, OpenAI GPT, Google Gemini, Mistral, or your own fine-tuned models. Your data governance layer survives model changes.
Understands Context, Not Just Keywords
50+ entity types: names, addresses, accounts, amounts, dates, and the relationships between them. Context-aware classification means fewer false redactions and better LLM output.
Trusted in regulated environments
Tier-1 UK Bank
Reduced LLM deployment approval timeline from 9 months to 6 weeks.
FTSE 250 Insurer
Moved claims summarisation from pilot to production in 8 weeks.
NHS Trust
Achieved IG Toolkit compliance for clinical AI in 4 weeks.
Currently piloting with Tier-1 UK financial institutions. Case studies available on request.
Model Agnostic: Works With Any LLM Provider
Your compliance team gets evidence, not assurances
Audit-ready from day one. Every transformation is logged with SHA-256 verification. When the FCA, PRA, or ICO asks what happened to which data, you have the answer.
Built against EU AI Act (high-risk system transparency), DORA (operational resilience), FCA/PRA supervisory requirements, GDPR (data protection by design), and SOC 2 controls. Not retrofitted. Built in.
Your team can inspect the methodology, review the classification rules, and verify what gets redacted and why. No black boxes.
Production is not a model problem. It is a trust problem.
Presential is the infrastructure layer between your data and every AI model. Move from pilot to production without compromising on what your security, compliance, and infrastructure teams require.
96.2% accuracy. Zero PII exposure.
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