# Naringhi > Naringhi is an AI engineering studio in Baku, Azerbaijan. It designs and builds autonomous AI agents, private (on-premise) LLM systems, Edge-Native software and data-driven growth engines (GEO/AIO) for B2B organisations worldwide. Key facts: - Website: https://naringhi.dev/ - Founder & Principal AI Engineer: Vusal Muradli (https://www.linkedin.com/in/vusal-muradli/) - Contact: hello@naringhi.dev — free 30-minute architecture consultation via https://naringhi.dev/contact - Social profiles: LinkedIn (https://www.linkedin.com/company/naringhi), Facebook (https://www.facebook.com/profile.php?id=100095536867572), Instagram (https://www.instagram.com/naringhi/) - Headquarters: Baku, Azerbaijan; serves clients worldwide - Philosophy: “Beyond lines of code, we build tomorrow’s reality.” - Industries: FinTech and banking, healthcare, legal, supply chain and logistics, manufacturing, SaaS and professional services - Data privacy: private LLMs are deployed on the client's own infrastructure (on-premise or a private cloud tenant); client data is not sent to external AI providers unless the client agrees in writing. NDAs and DPAs are signed before any data is exchanged. - Pricing: focused AI agent or RAG systems typically start in the mid-five-figure USD range; enterprise on-premise LLM deployments are scoped individually with a fixed-price architecture proposal. - Timelines: discovery and architecture take 2–3 weeks; a first production-ready AI agent or Edge-Native application typically ships in 8–12 weeks; enterprise on-premise LLM rollouts take 4–6 months. - Engineering benchmarks (design targets, not aggregated client results): under 50 ms global edge latency (Cloudflare Workers and WebAssembly); zero external data egress on air-gapped and private-VPC deployments; 8–12 weeks from discovery blueprint to production; 24/7 agent operation with human-in-the-loop escalation. - Ownership: under the standard agreement the client owns the source code, configuration, documentation and any model weights fine-tuned on its data; no vendor lock-in. Open-source components and open-weight base models keep their own licences. - Integration: connects to existing ERP, CRM and on-premise databases through APIs, database access, message queues, file exports or the Model Context Protocol (MCP). - Published work: the website shows reference architectures (illustrative designs), not client case studies. Naringhi does not claim certifications or audit reports it has not obtained. - Last updated: 2026-10-30 ## Company - [Home](https://naringhi.dev/): What the studio does, its engineering benchmarks, three reference-architecture summaries and a short overview of how a project runs. - [About](https://naringhi.dev/about): Who Naringhi is, its four working principles (AI-first architecture, measured outcomes, open stack, client ownership), vision and mission. - [Contact](https://naringhi.dev/contact): Email, LinkedIn, location and an enquiry form for a free 30-minute architecture consultation; what happens after you get in touch and how to prepare. ## Services - [AI & Data Engineering](https://naringhi.dev/services#ai-data): Autonomous AI agents and workflow automation (tool use through secure APIs and MCP); deployment and fine-tuning of open-weight models (Llama, Mistral, Qwen) in private infrastructure; RAG knowledge bases with hybrid retrieval, re-ranking and access control; predictive analytics; computer vision and speech intelligence. - [Software & Product Engineering](https://naringhi.dev/services#software-engineering): Decoupled TypeScript / Next.js / headless CMS platforms; Edge-Native applications on edge runtimes such as Cloudflare Workers; WebAssembly; Progressive Web Apps; AI-assisted delivery with automated testing and SAST; multi-tenant AI-native SaaS. - [Product Design & Digital Strategy](https://naringhi.dev/services#product-design): Product discovery and UX research; tokenized Figma-to-code design systems; conversion rate optimisation (CRO). - [Tech-Enabled Growth & Search Authority](https://naringhi.dev/services#growth): Generative Engine Optimization (GEO/AIO) and entity schema; CRM and funnel automation; technical SEO and Core Web Vitals; AI-assisted social listening. - [RAG vs. fine-tuning vs. AI agents vs. predictive ML](https://naringhi.dev/services#choosing-an-approach): Comparison table of the four AI approaches — what each does, when to use it, the data it needs and its typical deliverable. ## Reference architectures - [Private Compliance Knowledge Engine (RAG)](https://naringhi.dev/architectures#private-rag-engine): Open-weight LLM on private GPU infrastructure with hybrid, permission-aware retrieval and cited answers for compliance, risk and legal teams. - [Multi-Agent Exception Handling](https://naringhi.dev/architectures#multi-agent-exceptions): Triage and specialist AI agents connected to ERP/TMS and carrier APIs that resolve logistics exceptions within policy guardrails and escalate the rest to people. - [Edge-Native Platform, Ready for AI Search](https://naringhi.dev/architectures#edge-native-platform): Edge-rendered semantic HTML, WebAssembly processing and a connected JSON-LD entity graph for fast global delivery and machine readability. - [Public cloud AI wrappers vs. sovereign AI architecture](https://naringhi.dev/architectures#comparison): Comparison of data egress, latency, compliance, hallucination control, cost at scale and ownership. - [Engagement model](https://naringhi.dev/architectures#engagement): Discovery, build, evaluation and hand-over phases with typical durations. ## FAQ - [Frequently asked questions](https://naringhi.dev/faq): On-premise vs. private cloud vs. public AI API (with comparison table), AI agents versus chatbots, data privacy and compliance, IP and source-code ownership, legacy ERP/CRM integration, Edge-Native software, GEO, pricing, timelines and industries served. ## Optional - [Privacy Policy](https://naringhi.dev/privacy): What personal data the website collects, why, and visitors' rights. - [Terms of Service](https://naringhi.dev/terms): Terms for using the website. - [Security & Responsible Disclosure](https://naringhi.dev/security): How client data is handled and how to report a vulnerability.