Executive Summary
Enterprises face a critical tension: they need the power of large language models (LLMs) to unlock corporate knowledge, but uploading sensitive documents to AI providers creates unacceptable security, compliance, and sovereignty risks.
Talvi resolves this tension through intelligent chunking - a pre-processing architecture that breaks documents into small, targeted fragments before any interaction with an LLM. The full document never leaves your control. Only tiny, relevant chunks are sent to the AI model, and only when needed to answer a specific query.
This approach is fundamentally more secure than alternatives, maintains complete data sovereignty, and aligns with the regulatory landscape heading into 2026 and beyond - including the EU AI Act, GDPR, and emerging data residency requirements.
Why Uploading Documents to LLMs is Risky
The simplest way to use AI with corporate content is also the most dangerous: upload an entire document to ChatGPT, Claude, or a similar service and ask a question. This approach is widespread - and deeply flawed for enterprise use.
Total Data Exposure
When you upload a 200-page compliance manual to ask one question, the entire document is transmitted to a third-party provider. Every page, every piece of sensitive information - regardless of relevance.
Training Data Risk
Many LLM providers use input data to improve future models - meaning your confidential documents could influence responses given to other users. Even with opt-outs, verification is impossible.
Sovereignty Violations
Documents uploaded to cloud-based LLMs may be processed across multiple jurisdictions, violating data residency requirements. For GDPR-subject organisations, this creates immediate compliance exposure.
Prompt Injection Attacks
When entire documents are processed, hidden malicious instructions can manipulate the LLM's behaviour. The larger the data exposure, the larger the attack surface.
The shadow AI crisis: GenAI usage among employees has tripled in the past year. Data sent to AI tools has increased sixfold. Sensitive data policy violations have doubled. The average organisation now experiences 223 AI-related security incidents per month. Yet 50% of organisations still lack enforceable data protection policies for AI applications.
Sources: Cyberhaven Labs 2026 AI Adoption & Risk Report; Netskope Cloud and Threat Report 2026
The Regulatory Landscape
The regulatory environment is tightening rapidly. Organisations that don't address AI data handling now will face significant legal and financial exposure.
| Regulation | Timeline | Maximum Penalty |
|---|---|---|
| EU AI Act | Full enforcement August 2026 | Up to 7% of global annual turnover |
| GDPR | Active - enforcement intensifying | Up to 4% of global turnover or 20M EUR |
| EU Data Act | Effective September 2025 | Covers industrial and non-personal data |
| UK AI Framework | Sector-specific guidelines active | Sector regulator dependent |
| US DOJ Bulk Data Rule | Effective April 2025 | Prohibits sharing with "countries of concern" |
The sovereign cloud market is expanding from £125 billion in 2025 to an estimated £670 billion by 2032, reflecting the urgency with which organisations are seeking jurisdiction-aware infrastructure.
Talvi's Approach: Pre-Process, Don't Upload
Talvi takes a fundamentally different approach to enterprise AI. Instead of sending your documents to an LLM, we pre-process them within your controlled environment and only send small, precisely targeted chunks when needed to answer a specific query.
This is the critical distinction: your full documents never reach the LLM. The AI model only ever sees carefully selected fragments - typically around 5% of the total content - that are directly relevant to the employee's question.
The Three-Stage Pre-Processing Pipeline
Stage 1: Content Parsing
Talvi's secure connectors ingest content from your LMS, SCORM packages, SharePoint, Google Drive, and other repositories. Text is extracted from over 20 file formats. Audio and video content is transcribed.
Security: Processing happens within your controlled infrastructure. Raw files are never transmitted externally.
Stage 2: Intelligent Chunking
Talvi segments documents into optimally-sized chunks - preserving contextual relationships while creating granular, independently retrievable pieces of knowledge.
Security: No single chunk contains enough context to reconstruct sensitive complete documents, even if intercepted.
Stage 3: Vector Generation & Storage
Each chunk is converted into a high-dimensional mathematical embedding - a numerical representation that captures its meaning. These embeddings are stored in Talvi's vector database.
Security: The vector database stores mathematical representations, not readable text. Even direct access would not expose original documents.
What Happens When an Employee Asks a Question
Hybrid Semantic Vector Search
ANN algorithms search the entire internal vector database to identify the most relevant chunks based on the query's meaning, while also incorporating keyword and full-text signals to improve precision.
AI Relevance Grading
A specialised agent evaluates search results, filtering out contextually irrelevant content. This is a second security gate.
Response Composition
Only now are the selected chunks (typically 5-10 short passages) sent to the LLM. Typically a chunk is 256-512 AI tokens. This means the minimum amount of data possible is sent to the LLM.
Instant Answers
The LLM returns the focused answer - or multiple answers, depending on the agent - and presents them to the employee within Talvi, in seconds.
Source Attribution & Launching
The original source files are displayed and can be launched directly: videos cued to the exact second, SCORM files opened at the relevant page, and PDFs navigated to the specific section.
A Real-World Example
Traditional Approach
Employee asks: "What's the verification process for cash deposits over £10,000?"
- ✗ Uploads entire AML compliance manual (100+ pages)
- ✗ 150,000 tokens sent to external LLM
- ✗ Internal investigation protocols fully exposed
Talvi's Approach
Same question. Fundamentally different data exposure.
- Vector search finds 3-4 relevant sections
- Only 7,500 tokens sent to the LLM
- LLM never sees investigation protocols
95% less data exposure. Same accurate answer.
The Security Benefits of Chunking
Talvi's chunking architecture delivers security advantages at every level - from the fundamental data minimisation principle through to resistance against emerging AI-specific attack vectors.
Data Minimisation by Design
GDPR Article 5(1)(c) establishes data minimisation as a core principle - personal data must be "adequate, relevant and limited to what is necessary." Talvi's architecture enforces this at a technical level. By sending only the specific chunks needed to answer a query, the minimum possible data reaches the LLM. This isn't a policy decision that staff might override - it's built into the architecture itself.
Reduced Attack Surface
The less data transmitted, the smaller the window for interception, leakage, or misuse. If 150,000 tokens of a compliance manual are sent externally, the attack surface is 150,000 tokens wide. If only 7,500 targeted tokens are sent, the attack surface shrinks by 95%. The remaining 142,500 tokens never leave your infrastructure.
Granular Access Control at the Chunk Level
Talvi's vector database supports metadata filtering based on department, classification level, and user permissions. Role-based access controls are applied before retrieval - meaning the AI will not even search content that a user isn't authorised to see. Sales teams never encounter confidential HR data. This is fundamentally impossible when uploading whole documents to a generic LLM.
Complete Audit Trail
Every query generates a traceable record of exactly which chunks were retrieved, which sources they came from, and what answer was delivered. This is critical for regulated industries - Financial Services, Healthcare, Pharmaceuticals - where demonstrating what information was accessed, by whom, and when is a compliance requirement.
Resistance to Prompt Injection
Prompt injection - where malicious instructions hidden within documents manipulate the LLM - is a significant AI security threat. With chunking, only small, pre-processed fragments reach the LLM, dramatically limiting the ability for hidden instructions to influence output.
Data Sovereignty & Deployment Flexibility
Data sovereignty isn't just about where your data is stored - it's about who controls the infrastructure, who can access the data, and which jurisdiction's laws apply. Talvi offers three deployment models to match your sovereignty requirements.
Talvi Cloud
Single-tenancy VM with external LLM via encrypted TLS 1.3
- Siloed object store per client
- No data retained by LLM provider
- Fastest deployment
- LLM-independent
Mixed Deployment
Talvi cloud with self-hosted LLM processing
- LLM processing stays internal
- Data processed in RAM only
- Enhanced privacy
- Works with LLaMA, Phi-4, Gemma 3
On-Premises / VPC
Complete deployment within your own infrastructure
- Full data sovereignty
- Regional data residency
- Maximum compliance
- Works with LLaMA, Phi-4, Gemma 3
Encryption at Every Level
Data at Rest
AES-256 encryption using dedicated per-client encryption keys
Data in Transit
TLS 1.3 for internal; TLS 1.2/1.3 with self-renewing certificates externally
LLM Communications
Encrypted via TLS 1.3 - never stored or retained by the LLM provider
Backup & Recovery
Daily encrypted snapshots stored outside core infrastructure
Enterprise Governance & Compliance
Security Standards
- SOC2 / ISO 27001 - Enterprise-grade certification
- GDPR Compliant - Full EU data protection
- EU AI Act - Article 50 compatible
- UK AI Framework - Sector-specific guidelines
Access & Authentication
- SSO Integration - SAML/OIDC
- Role-Based Access - Least-privilege principles
- Two-Factor Auth - Out-of-band magic link
- Full Audit Logging - Complete access logs
The Cost Dividend
A secure architecture also happens to be an efficient one. When you send 95% less data to an LLM, you consume 95% fewer tokens - and that translates directly into cost savings.
95%
Fewer Tokens
7,500 vs 150,000 per query
£475K
Saved Per Million Queries
£25-50 vs £500-1,000 per 1K
95%
Less CO2
2.5-5kg vs 50-100kg per 1K
Beyond Security: What the Architecture Enables
Precision Content Delivery
Direct employees to the exact page in a PDF, specific timestamp in a video, or precise section within a SCORM module.
Multi-Source Intelligence
A single query can draw from SCORM modules, PDFs, videos, policies, and SharePoint simultaneously.
Automatic Content Creation
Analytics identify knowledge gaps and generate targeted content automatically - reducing L&D content creation time by up to 80%.
SCORM Rescue
Extract and preserve knowledge from legacy SCORM packages - even when source files are lost or vendors are no longer available.
The architecture that makes enterprise AI adoption possible
The question is no longer whether to use AI for corporate knowledge - it's how to do so securely. Uploading entire documents to LLMs is fast but fundamentally insecure. Talvi's intelligent chunking architecture provides the same powerful AI capabilities while keeping 95% of your data exactly where it belongs: under your control.
For L&D leaders and CISOs alike, this is the architecture that makes enterprise AI adoption possible without compromising on security, sovereignty, or compliance.