# Safe4AI

> Enterprise AI deployment — on your hardware or private cloud. Full data control, zero exposure.

Safe4AI designs and deploys AI solutions for enterprise clients — including AI agents, conversational chatbots with RAG architecture, document intelligence (OCR), model training pipelines, and deep enterprise system integrations (ERP, DMS, CRM, HRIS). Deployed on-premise or private cloud, within your security perimeter.

## Home

- [Safe4AI Home](https://safe4ai.com/): Enterprise AI deployment on your hardware or private cloud. The homepage presents a phased AI integration roadmap for business software — four phases: Intelligent Document Processing (automated extraction, classification, and routing of documents across ERP, DMS, CMS with 99%+ accuracy), Knowledge Assistant (context-aware AI connected to ERP, DMS, CMS, HR systems with cited sources and role-based access), Deep System Integration (AI agents operating natively inside ERP, DMS, CMS, HRIS with full audit trails and RBAC), and Real-Time Voice & Multimodal Layer (voice and multimodal interface for customer service, internal support, hands-free operations). Technology stack includes agentic AI frameworks (LangGraph, Pydantic AI, LlamaIndex, LangChain), AI/ML infrastructure (custom model training, RAG architecture, OCR pipeline), security & compliance (on-premise deployment, enterprise SSO/RBAC, data sovereignty), and integration (DMS/ERP connectors, API gateway with REST and gRPC, voice interface). The "Token Capital" concept: companies should build two forms of capital — Human Capital (people hold the judgment, tacit expertise, customer context, operational judgment) and Token Capital (models hold the memory, private model weights, domain adapters, agent workflows) — creating a private learning loop where AI learns from expert work and people use that AI to make better decisions. Four reasons to choose Safe4AI: your data stays on your infrastructure, production-proven security (RBAC, audit logging, encryption, GDPR-aligned and SOC 2-ready architecture), regional language expertise (NLP training, custom tokenization, domain-specific fine-tuning for local markets), and end-to-end ownership (from hardware provisioning to model deployment to UI, single team, no vendor lock-in). Homepage FAQ covers: what on-premise AI deployment is, how Safe4AI ensures data privacy and security (encryption at rest and in transit, RBAC, audit logging, Active Directory/LDAP/SAML integration, controls designed for HIPAA, DORA, NIS2, EU AI Act, and GDPR-aligned requirements), which AI frameworks are used (LangGraph, Pydantic AI, LlamaIndex, LangChain, PyTorch, HuggingFace Transformers, LoRA/QLoRA, Whisper, Wav2Vec, LayoutLM, PaddleOCR, Tesseract, Docker, Kubernetes), and which industries are served (healthcare, financial services, legal, manufacturing, government and public sector).

## About

- [About Safe4AI](https://safe4ai.com/about): Safe4AI is a team of experienced engineers and AI specialists based in Belgrade, Serbia, building on-premise AI infrastructure for enterprises that need data sovereignty, compliance, and full control over their AI systems. The company exists because enterprises in healthcare, finance, legal, and regulated industries should be able to use state-of-the-art AI without handing their data to a third-party cloud, paying per-token forever, or depending on a vendor that can change pricing or deprecate a model overnight. Three core principles: data sovereignty (data never leaves your infrastructure — not during inference, not during training, not ever), no vendor lock-in (open-source foundation models on your hardware, no subscription that doubles next year), and regulatory alignment (GDPR-aligned, SOC 2-ready architecture designed for HIPAA, DORA, NIS2, and EU AI Act requirements). Every project is handled by experienced engineers — not passed to juniors after the sale — and success is measured against the business problem, not lines of code delivered. Working principles: infrastructure first (design around your existing stack — bare-metal, VMware, Kubernetes, private cloud, no migration required), small team with full ownership (experienced engineers on every project, direct access to the people building your system), and outcomes over outputs (measured against the business problem — fewer manual hours, faster processing, reduced risk).

## Products

- [private·ai](https://safe4ai.com/products/private-ai): A compliance-first enterprise RAG assistant built by Safe4AI — every retrieval is logged, every answer is grounded, every span is traceable, without sending a byte to the cloud. Runs entirely in your VPC using local Ollama for inference, Qdrant for vectors, and Postgres for audit. Zero data egress, no API keys to a third party. Six core capabilities: persistent audit rows for every prompt/retrieval/latency/trace ID (exportable as CSV, retained 90 days), three guard filters (input guard blocks injections and blocked terms, content filter scrubs PII from retrieved chunks, output filter checks for hallucinated PII and bounded length), hybrid retrieval (dense vector similarity + sparse BM25 fused with RRF and reranked), full OpenTelemetry distributed tracing shipped to Jaeger, grounded citations anchoring every assertion to source chunk and page, and full privacy on your network with your data and your weights. Seven-stage pipeline: User (auth/SSO/RBAC) → Input Guard (injection/PII/policy) → Hybrid Retrieve (dense/BM25/RRF) → Content Filter (PII redact/k filter) → LLM Generate (Ollama/Qwen/vLLM) → Output Guard (PII/length/cite) → Audit + OTEL (Postgres/Jaeger). Comparison vs generic cloud LLM: data never leaves your network (vs every request), persistent queryable audit trail (vs log on best effort), built-in PII redaction in retrieval (vs DIY in your wrapper), grounded citations required by default (vs optional, often omitted), model choice of local/hybrid/cloud (vs whatever the vendor ships), OpenTelemetry tracing on every span (vs vendor dashboards only), tenant isolation in your VPC with your weights (vs shared infra), and predictable cost per query on your own hardware (vs $$$ at scale). Free 60-day evaluation pilot with deployment support included and 24h response SLA. Live at private-ai.uk. Designed for regulated teams in healthcare, finance, legal, and government who need AI their auditor will love.

## Services

- [All Services](https://safe4ai.com/services): Overview of all eight enterprise AI services with FAQ. Services FAQ covers: what makes Safe4AI different from cloud AI providers (zero ongoing per-token API costs, no vendor lock-in, GDPR-aligned and SOC 2-ready architecture designed for regulated environments, you own the models/weights/compute), how long deployments typically take (focused RAG chatbot or document intelligence pipeline: 4–6 weeks; custom AI agent system with ERP/DMS/CRM integration: 8–12 weeks; full multi-agent deployments with custom fine-tuning: 14–16 weeks), support for existing enterprise infrastructure (bare-metal, VMware, Proxmox, OpenStack, Kubernetes, Active Directory, LDAP, SAML 2.0, OAuth, SAP, Oracle ERP, SharePoint, Salesforce, REST and gRPC APIs, GPU or CPU-only), and fine-tuning open-source models on proprietary data (Llama, Mistral, Qwen, Phi, Gemma with LoRA/QLoRA, training data never leaves your environment, continuous fine-tuning pipelines).

- [On-Prem AI Deployment](https://safe4ai.com/services/on-premise-ai-deployment): Full-stack AI infrastructure deployed on your hardware or private cloud — models, agents, and pipelines running entirely within your security perimeter. We provision and configure GPU clusters, containerized model serving (Docker, Kubernetes), and inference endpoints for bare-metal, VMware, and private OpenStack environments. Every component runs air-gapped from external providers. Typical deployment timeline is 4–8 weeks. Architecture designed for HIPAA, GDPR, DORA, NIS2, and EU AI Act data residency and control requirements. Used by enterprises in healthcare, finance, legal, and manufacturing that require AI capabilities without external data transmission.

- [Custom AI Agents](https://safe4ai.com/services/custom-ai-agents): Autonomous AI agents built on LangGraph, Pydantic AI, LlamaIndex, and LangChain — deployed on-premise and integrated with your ERP, DMS, CRM, and internal APIs. Agents perform multi-step reasoning, use external tools (document retrieval, database queries, API calls), and operate within role-based access controls governed by your Active Directory or LDAP setup. Use cases include procurement automation, contract review workflows, HR process handling, and internal knowledge operations. Deployment timeline: 6–12 weeks depending on integration complexity. Zero data egress — all reasoning and tool use occurs within your infrastructure.

- [Intelligent Chatbots & Assistants](https://safe4ai.com/services/chatbots): Domain-specific conversational AI trained on your internal documentation, policy manuals, and knowledge base — deployed entirely on your infrastructure using RAG architecture. Supports multi-language interaction with native NLP and returns cited, source-attributed responses rather than hallucinated answers. Integrates with your existing ticketing, intranet, or support systems. Typical use cases: internal employee Q&A, customer-facing support, clinical staff reference tools, and regulatory compliance assistants. Deployment timeline: 4–8 weeks. Designed for sensitive documentation environments with GDPR-aligned and HIPAA-ready controls.

- [Document Intelligence (OCR)](https://safe4ai.com/services/document-intelligence): Multi-stage document processing pipelines for invoice extraction, contract analysis, and HR document automation — deployed on your infrastructure using LayoutLM, PaddleOCR, and Tesseract. Pipelines include OCR preprocessing, entity extraction, validation rules, exception handling, and automated routing to downstream systems (ERP, DMS). Handles PDF, DOCX, scanned images, and legacy XML formats. Supports structured output to SAP, Oracle, SharePoint, and custom APIs. Typical processing accuracy above 95% after validation tuning. Deployment timeline: 4–6 weeks. Used in finance, healthcare administration, and legal document management.

- [RAG Knowledge Systems](https://safe4ai.com/services/rag-systems): Retrieval-Augmented Generation systems over your private knowledge base — combining hybrid vector search (dense + BM25) with a locally hosted LLM to deliver accurate, source-cited answers from your internal documents. Built with Qdrant or Weaviate for vector storage, and sentence-transformer models for embedding. Supports document-level access controls so users only retrieve content their role permits. Handles knowledge bases from hundreds to millions of documents. Deployment timeline: 4–8 weeks. Used in healthcare (clinical protocols), legal (precedent search), and enterprise knowledge management where data cannot be sent to external AI APIs.

- [Model Training & Fine-tuning](https://safe4ai.com/services/model-training): Custom model training and fine-tuning on your proprietary data — entirely within your infrastructure, with no training data transmitted externally. We fine-tune Llama, Mistral, Qwen, Phi, OpenAI and Gemma variants using LoRA and QLoRA adapters for domain adaptation, instruction tuning, and task-specific performance improvements. Includes data preparation pipelines, training run management, evaluation, and continuous fine-tuning setup so models improve over time as your data grows. Uses PyTorch and HuggingFace Transformers on GPU clusters. Deployment timeline: 6–14 weeks depending on dataset size and target performance. Suitable for enterprises with proprietary language, domain terminology, or internal process knowledge.

- [Voice Interface Layer](https://safe4ai.com/services/voice-interface): Real-time voice processing for AI agent interaction — low-latency automatic speech recognition (ASR) using Whisper and Wav2Vec, paired with local text-to-speech (TTS) synthesis. Designed for customer service automation, accessibility applications, internal support desks, and clinical documentation workflows. Operates fully on-premise with no audio data leaving your infrastructure. Supports multiple languages and accent-robust transcription. Integrates with existing telephony systems and agent platforms. Deployment timeline: 4–8 weeks. Latency under 300ms for ASR on appropriately provisioned GPU hardware.

- [Enterprise System Integration](https://safe4ai.com/services/enterprise-integration): Bidirectional connectors between AI components and your existing enterprise systems — DMS, ERP, CRM, HRIS, and legacy databases. Built using RESTful and gRPC APIs with rate limiting, circuit breakers, audit logging, and SSO/RBAC authentication. Supports event-driven integration via Kafka and RabbitMQ for high-throughput pipelines. Compatible with SAP, Oracle ERP, Microsoft SharePoint, Salesforce, and custom in-house systems. Includes full audit trail of AI-triggered actions for regulatory compliance. Deployment timeline: 4–10 weeks depending on the number and complexity of target systems.

## Case Studies

- [All Case Studies](https://safe4ai.com/case-studies): Real-world AI implementations proven in production — on-premise deployments, document intelligence, conversational AI, and GTM automation for enterprise clients. Includes a confidential NDA-protected case study: a domain-adapted language model and retrieval-augmented generation pipeline deployed for a mid-size enterprise, handling internal knowledge search, automated report drafting, and customer-facing chatbot interactions — all on-premise with zero data leaving the client's infrastructure (Fine-Tuning, RAG, Chatbot, On-Premise, Custom LLM).

- [Medigent](https://safe4ai.com/case-studies/medigent): On-premise AI pre-consultation platform deployed for Medigent.ai — a healthcare technology company serving regional hospital networks. The system structures patient information before appointments using clinically approved intake procedures, giving physicians a prepared brief before each consultation. Reduces per-consultation intake time by an average of 4.5 minutes, enabling doctors to see 40%+ more patients per day. Addresses the structural capacity crisis in primary care: over 6 million GP referrals go unprocessed annually in the UK alone. Designed for HIPAA and UK data protection requirements — no patient data leaves the provider's infrastructure. Deployed across US and UK healthcare environments.

- [Marketing Chatbot](https://safe4ai.com/case-studies/marketing-chatbot): An AI-powered chat widget that sits on your website, engages visitors in natural conversation, answers questions about your business, and quietly captures qualified leads — without forms, without friction, without any manual follow-up setup. Trained on your services, pricing, client profiles, differentiators, and FAQs. Lead capture is built into the conversation naturally: when the AI identifies genuine interest, it gathers name, email, company, and service interest as part of the exchange — no redirect, no separate form, no drop-off point. Captured leads are sent directly to your inbox the moment they are produced. Features: streaming AI responses (word-by-word, real time), business-specific knowledge (no generic AI filler), natural lead capture, automatic email delivery, fully branded (colors, fonts, logo, assistant name), mobile-ready (responsive, iOS Safari safe-area support). Technical architecture: React widget (~200 lines, embeds into any site), HTTP streaming response delivery, full conversation memory per request, structured tag detection for lead extraction, Formsubmit or custom SMTP for email, webhook-ready for CRM/Slack/Notion integration. Deployment time: 2–5 days from brief to live. Live deployments on safe4ai.com and erhrglobal.com.

- [Marketing Agent](https://safe4ai.com/case-studies/marketing-agent): An AI-powered go-to-market copilot that researches your market, builds positioning strategy, generates platform-native content, and runs five layers of automated quality control — turning weeks of campaign work into minutes. Seven-stage workflow: Brief → Research (MCP-connected tools gather live market data via web search, crawling, scraping) → Strategy (ICP, pain points, positioning statement, messaging pillars, brand voice, hooks, channel strategy) → Modules (platform-native content for X, LinkedIn, Instagram, 7/14-day calendars, creative briefs) → QC Review (five independent AI reviewers) → Human Approval → Export (Markdown or JSON with full audit trail). Five QC reviewers: Brand Safety (hate speech, discrimination, misleading framing), Claim Verifier (checks superlatives and statistics against MCP sources), Platform Compliance (X 280-char limit, hashtag counts, engagement bait), Tone Consistency (compares against brand voice), Conversion Reviewer (CTAs, value props, goal alignment). Built with React + TanStack Router/Start, PostgreSQL via Drizzle ORM, PgBoss job queue, OpenAI-compatible LLM (DeepSeek default, swappable), MCP STDIO clients for research. Docker Compose deployment with Caddy reverse proxy, automatic HTTPS, and security headers. Typical deployment: 2–5 business days for a branded instance.

- [CompanySpace](https://safe4ai.com/case-studies/companyspace): An AI-powered invoice processing and expense tracking system that eliminates manual data entry — extracting, classifying, and structuring financial documents automatically so finance teams know exactly where company money is going. Built for CompanySpace (companyspace.cloud), an HR tech platform. Five-stage pipeline: Document Ingestion & Preprocessing (deskew, denoise, contrast enhancement, PDF rasterization) → OCR Layer (multi-language, mixed layouts, tabular data, handwritten annotations) → Document Classification (invoice, receipt, utility bill, contract) → LLM Extraction & Structuring (vendor name, invoice number, dates, amounts, line items, tax breakdowns, employee ID, cost center) → Validation & Output (business rule checks, confidence thresholding, high-confidence straight-through to database, low-confidence flagged for review). Performance: 95%+ OCR accuracy on critical fields, <3 seconds per document, 80%+ reduction in processing time, zero manual data entry for standard invoices. Handles PDF, PNG, JPEG, TIFF, HEIC. Multi-language support (Serbian, English). PostgreSQL + S3-compatible object storage. REST API + webhook integration. Deployed in 8 weeks across five phases: document analysis, OCR pipeline development, LLM extraction engine, integration & API layer, pilot & refinement.

## Contact

- [Contact Safe4AI](https://safe4ai.com/contact): Tell us about your infrastructure and your requirements — we will scope a realistic solution. Direct access to experienced engineers, not a sales team. Location: Belgrade, Serbia. Email: info@safe4ai.com.

## Legal

- [Privacy Policy](https://safe4ai.com/privacy-policy): How Safe4AI collects, uses, and protects personal data submitted through the contact form, with GDPR-aligned practices.
- [Terms of Service](https://safe4ai.com/terms-of-service): Terms governing use of the Safe4AI website, contact forms, Ask AI assistant, and developer API.

## Safe4AI developer resources

- [Safe4AI developer resources](https://safe4ai.com/developers): Quickstart, OpenAPI 3.1, OAuth 2.0 onboarding, self-service sandbox API keys, public sandbox, JSON errors, rate limits, versioning/deprecation policy, and Safe4AI CLI guidance.
- [Safe4AI API discovery](https://safe4ai.com/api): Public JSON directory of the Safe4AI Agent API surface, including rate-limit and versioning policy links.
- [Safe4AI OpenAPI 3.1 specification](https://safe4ai.com/openapi.json): Typed REST schema with unique operationId values and response schemas for agent function calling.
- [Safe4AI self-service sandbox API key](https://safe4ai.com/api/v1/keys): Zero-approval expiring sandbox key generation (`X-API-Key`).
- [Safe4AI OAuth 2.0 metadata](https://safe4ai.com/.well-known/oauth-authorization-server): RFC 8414 authorization server discovery for `client_credentials`.
- [Safe4AI public sandbox](https://safe4ai.com/api/v1/sandbox): Zero-auth deterministic GET/POST endpoint for integration checks.
- [Safe4AI authenticated agent ping](https://safe4ai.com/api/v1/agent/ping): OAuth or API-key protected sandbox reachability check.
- [Safe4AI CLI](https://safe4ai.com/developers#cli): Official `safe4ai-cli` — `npx safe4ai-cli doctor` and `npx safe4ai-cli sandbox "hello agent"`.
- [Safe4AI API versioning policy](https://safe4ai.com/developers/versioning): URL-path versioning with Deprecation and Sunset headers for agents.

## Safe4AI developer resources

- [Safe4AI developer resources](https://safe4ai.com/developers): Quickstart, OAuth 2.0 onboarding, self-service API keys, sandbox, JSON errors, rate limits, versioning policy, and CLI guidance.
- [Safe4AI API discovery](https://safe4ai.com/api): Public JSON directory of the API surface.
- [Safe4AI OpenAPI 3.1 specification](https://safe4ai.com/openapi.json): Typed REST schema with unique operationId values and response schemas for function calling.
- [Safe4AI self-service sandbox API key](https://safe4ai.com/api/v1/keys): Zero-approval expiring sandbox key generation.
- [Safe4AI OAuth 2.0 metadata](https://safe4ai.com/.well-known/oauth-authorization-server): RFC 8414 authorization server discovery.
- [Safe4AI public sandbox](https://safe4ai.com/api/v1/sandbox): Zero-auth deterministic GET/POST endpoint.
- [Safe4AI authenticated agent ping](https://safe4ai.com/api/v1/agent/ping): OAuth/API-key protected sandbox endpoint.
- [Safe4AI API versioning policy](https://safe4ai.com/developers/versioning): Deprecation and Sunset header conventions.
- [Safe4AI CLI](https://safe4ai.com/developers#cli): Official `safe4ai-cli` for doctor checks and sandbox calls (`npx safe4ai-cli doctor`).