Enterprise AI that understands your business, knowledge and workflows.

A Private AI Assistant is an AI-powered system designed to work with an organisation's own approved data, documents, applications and processes. Unlike a general-purpose public chatbot, it can be designed around internal terminology, users, permissions, security requirements and the business systems people already use.

Important information is often spread across policies, shared folders, technical manuals, support records, knowledge bases, databases and disconnected applications. A private assistant creates a conversational layer over approved information so the experience can move from repeated searching and comparison to asking, retrieving, understanding and acting.

• POLICIES AND STANDARD OPERATING PROCEDURES
• TECHNICAL AND PRODUCT DOCUMENTATION
• SUPPORT AND PROJECT RECORDS
• CRM, ERP AND OPERATIONAL DATA
• INTERNAL KNOWLEDGE BASES

Retrieval-Augmented Generation, or RAG, searches approved knowledge before a language model creates a response. The assistant interprets the question, retrieves relevant information, supplies that context to the model and can show supporting sources where verification matters. This lets the system work with organisation-specific information that may not exist in a model's general training data.

• INTERPRET THE USER'S REQUEST
• SEARCH APPROVED ENTERPRISE SOURCES
• RANK AND FILTER RELEVANT INFORMATION
• GENERATE A GROUNDED RESPONSE
• SHOW SUPPORTING SOURCES WHEN APPROPRIATE

Private AI Assistants can work across structured and unstructured information. The right ingestion and retrieval approach depends on source quality, scale, permissions and update patterns. Structured systems require controlled queries and record-level access, while documents require careful extraction, cleaning, chunking, metadata and indexing.

• PDF, WORD, PRESENTATION AND SPREADSHEET CONTENT
• POLICIES, MANUALS, REPORTS AND KNOWLEDGE ARTICLES
• SQL DATABASES AND DATA WAREHOUSES
• CRM, ERP, HRMS AND TICKETING SYSTEMS
• DOCUMENT PLATFORMS, INTERNAL APPS AND CUSTOM APIS

For important enterprise questions, an answer should not always stand alone. The assistant can provide the relevant document title, section, knowledge article, record or linked source so an authorised user can inspect the information behind the response. Grounding, source restrictions, validation and refusal behaviour help reduce unsupported answers without pretending AI is infallible.

A useful first implementation focuses on a real group of users, trusted information and measurable work. The same foundation can later support additional departments and tools after retrieval quality, permissions and user value have been proven.

• EMPLOYEE KNOWLEDGE AND POLICY ASSISTANT
• HR AND IT HELPDESK ASSISTANT
• TECHNICAL SUPPORT COPILOT
• ENGINEERING AND OPERATIONS KNOWLEDGE
• SALES AND CUSTOMER-SERVICE SUPPORT
• CONTRACT, POLICY AND DOCUMENT ANALYSIS

Where the source material and permissions allow it, assistants can summarise, extract, classify, compare and navigate information across multiple documents. They can preserve useful conversational context for follow-up questions and learn the organisation's terminology, abbreviations, products and processes without carrying irrelevant context into unrelated requests.

• SUMMARISATION AND INFORMATION EXTRACTION
• DOCUMENT AND POLICY COMPARISON
• TABLE AND CLAUSE RETRIEVAL
• MULTI-DOCUMENT SYNTHESIS
• DOMAIN-AWARE FOLLOW-UP CONVERSATIONS

An assistant can be given permission to use selected tools—for example, retrieving a CRM record, checking ticket status, preparing a report or drafting a response. Higher-risk actions can require explicit human review and approval before execution. Tool access should remain narrow, traceable and aligned with the active user's permissions.

• APPROVED TOOL AND API ACCESS
• EXPLICIT OPERATIONAL BOUNDARIES
• HUMAN APPROVAL CHECKPOINTS
• TRACEABLE TOOL AND WORKFLOW HISTORY
• SAFE HANDLING OF EXCEPTIONS

Retrieval and tool use can be designed around roles, individual users and source-level permissions. Depending on the environment, the assistant can integrate with existing identity and Single Sign-On infrastructure for authentication, role mapping, session management and auditability. Sensitive sources should be connected intentionally rather than made available by default.

• ROLE-BASED AND USER-LEVEL ACCESS
• DATA-SOURCE PERMISSION ENFORCEMENT
• SINGLE SIGN-ON INTEGRATION
• SESSION AND IDENTITY CONTEXT
• AUDITABLE ACCESS TO KNOWLEDGE AND TOOLS

Private-cloud, on-premise and hybrid architectures can be evaluated according to data control, networking, infrastructure, workload, latency and security requirements. Commercial, open-source or self-hosted models may be appropriate. Separating application logic, retrieval, tools, security, model interfaces and evaluation helps keep the wider business system maintainable as models change.

Enterprise AI security begins with clear boundaries. Architecture can include authentication, encryption, API security, environment isolation, tool permissions, data-retention controls, monitoring and audit logs. Prompt injection and untrusted content require layered controls such as input validation, data boundaries, permission checks, output validation, sandboxing and human approval.

• CLASSIFIED AND INTENTIONALLY CONNECTED KNOWLEDGE
• PROMPT-INJECTION AND UNTRUSTED-CONTENT CONTROLS
• USAGE, SOURCE AND TOOL AUDITABILITY
• DATA-RETENTION AND MONITORING POLICIES
• DEFINED INCIDENT AND HUMAN-REVIEW PROCESSES

Private AI Assistants should be tested with representative business questions, users and failure cases. Evaluation can measure retrieval accuracy, answer relevance, source correctness, completeness, refusal behaviour, permission handling, tool accuracy, latency and user satisfaction. Weaknesses should be found before wider deployment.

• REPRESENTATIVE EVALUATION DATASETS
• RETRIEVAL AND SOURCE QUALITY
• SUPPORTED-ANSWER AND REFUSAL BEHAVIOUR
• PERMISSION AND TOOL-USE TESTING
• CONTINUOUS QUALITY MONITORING

Enterprise documents can contain repeated headers, scanned pages, tables, old versions and inconsistent formatting. A reliable pipeline extracts, cleans, structures, chunks, enriches and indexes approved content. Scheduled or event-based synchronization, version tracking, source expiration and re-indexing help prevent outdated information from remaining authoritative.

Truefox AI approaches models, RAG, data engineering, search, security, integrations, applications and evaluation as one operational system. Delivery begins by identifying users, approved sources, permissions, prohibited access and useful outcomes before a focused proof of concept and production architecture are defined.

• 01 · DISCOVER THE WORKFLOW AND USERS
• 02 · ASSESS KNOWLEDGE, SYSTEMS AND PERMISSIONS
• 03 · PROTOTYPE WITH REALISTIC QUESTIONS
• 04 · DESIGN RETRIEVAL, SECURITY AND DEPLOYMENT
• 05 · BUILD, INTEGRATE AND EVALUATE
• 06 · DEPLOY, MONITOR AND IMPROVE

A general-purpose assistant is designed for broad conversation. A Private AI Assistant is built around specific organisational knowledge, permissions, applications, terminology and workflows, with deployment and governance selected for the enterprise environment.

Yes. Language models can produce incorrect information. Retrieval grounding, source restrictions, validation, evaluation and workflow controls can reduce this risk and help the system refuse unsupported questions, but they do not make AI infallible.

Potentially, yes. On-premise, private-cloud and hybrid deployment can be evaluated, and open-source or self-hosted models may be used when they meet the required quality, infrastructure, latency and security needs.

Private AI Assistants can be combined with Agentic Automation when approved tools and multi-step actions are required, or delivered through custom web and mobile products that fit the organisation's existing user experience.

• AGENTIC AUTOMATION · /agentic-automation
• WEB & MOBILE PRODUCTS · /web-mobile-products
• BOOK A PRIVATE AI DEMO · /book-demo